Powered by FrappeHub
v3.2.9
The 5th Joint International Conference on Nanoscience & Nanoengineering (https://bond21.unimap.edu.my/) X ICATAM (International Conference of Advanced Technology and Multidiscipline – ICATAM) is a Joint International Conference hosted by the Institute of Nano Electronic Engineering (INEE), Universiti Malaysia Perlis (UniMAP) in collaboration with Universitas Airlangga (UNAIR), Surabaya, Indonesia. The conference will be held at Tenera Hotel & Suites, Selangor, Malaysia, on 30th September to 1st October 2026.
BOND21 believes on the principle of building the nation’s wealth and prosperity through the advancement in science and technology. Therefore, the theme for 2026’s conference is chosen as “Nanoscience and Nanoengineering for Nation’s Growth and Sustainability.”
ICATAM aims to serve as a global platform for researchers, academicians, industry professionals, and entrepreneurs to exchange ideas and foster collaboration across a wide range of engineering and technological disciplines. This conference highlights cutting-edge innovations in power systems and advanced computing, while also exploring emerging topics such as Smart and Micro Grids, Electric Vehicles, Automation and Computing Technologies in Green Commercial Buildings, Molecular Imaging, Smart Photonic Systems, and Cognitive Computing.
IMPORTANT NOTES TO AUTHORS:
First, authors need to submit an abstract, please click the "Submit Now" button at the bottom of this page to proceed with the submission.
Once the abstract was submitted, authors can proceed with the registration as a confirmation of interest to submit to BOND21 2026.
For authors whose abstract has been accepted, please proceed with the submission of your full paper through the FrappeHub Portal by clicking on the tab "My Conference" > "My Contributions", then click on your abstract title. Then, click on the "Submit paper" button. Next, you can upload your full paper.
Once your full paper has been accepted, you can proceed with the registration and payment process.
Chair: Prof. Subash C. B. Gopinath
Chair: Prof. Subash C. B. Gopinath
Chair: Prof. Subash C. B. Gopinath
Chair: Assoc. Prof. Dr. Ramzan Mat Ayub
Co-chair: Annisa Palupi Trisasongko
Effective management of bacterial panicle blight (BPB) is critical to minimizing yield losses, yet current strategies are limited by a scarcity of resistant rice cultivars. Furthermore, while the antibiotic oxolinic acid is a key chemical countermeasure globally, its agricultural use remains unregistered in several countries. Therefore, sustainable biocontrol alternatives are urgently needed for effective BPB management. This study evaluated the field efficacy of bioformulations consisting of the antagonistic bacterium Bacillus sp. B1-2-11 and chitosan nanoparticles (CNP) for managing BPB in paddy rice. Field experiments were conducted during the 2025 off-season at MARDI Seberang Perai using an open-field, small-scale cultivation system. The treatments evaluated the formulations administered either as individual or combined applications via an initial seed treatment and two subsequent foliar applications. Results were compared against current standard operating procedures (SOP) utilizing copper and the antibiotic oxolinic acid. All bioformulated treatments significantly reduced BPB incidence and severity compared to the untreated control. The combined seed and foliar application of Bacillus sp. B1-2-11 achieved the lowest disease incidence (19.9%) and severity score (2.5), demonstrating comparable efficacy to commercial standards. Regarding productivity, the Bacillus sp. B1-2-11 + CNP (seed and foliar) treatment achieved a prominent grain yield of 7.74 t/ha, closely matching the copper SOP (7.97 t/ha) and oxolinic acid SOP (7.50 t/ha). These findings indicate that the nano-biocontrol formulation offers a promising, environmentally sustainable alternative to chemical bactericides for BPB management, providing consistent disease reduction while maintaining high yield potential under field conditions.
Reproducibility during scale-up remains a major challenge in nanoparticle synthesis due to variations in mixing efficiency, local concentration gradients, and crosslinking kinetics. This study evaluated the upscaling of chitosan nanoparticles (CNPs) prepared via the ionic gelation of chitosan with sodium tripolyphosphate (TPP) anions at a critical ratio of 2.4:1. The preparation was successfully scaled up to a final volume of 48,450 mL using two distinct mixing dynamics: manual dropwise addition and a 15-minute roller mixing method. Physicochemical characterization showed that the upscaled CNPs synthesized via the manual dropwise method preserved the original properties of the non-upscaled control, exhibiting no significant differences in mean effective diameter (62.60 ± 1.99 nm vs. 64.42 ± 0.82 nm, respectively). Conversely, the roller mixer method introduced a significant variation in particle size (57.89 ± 0.36 nm), though it successfully remained well within the stable nanoscale range. Both upscaling approaches maintained low polydispersity index (PDI) values (~0.20), indicating excellent homogeneity, a narrow size distribution, and robust formulation stability. Overall, while the manual dropwise addition method is ideal for strictly preserving original physicochemical traits, both upscaling strategies offer viable, stable, and reproducible delivery platforms for large-volume agricultural applications.
Epoxy resin and polyvinyl acetate adhesives are widely used in treating the cracks and fractures for stones antiquities. In this study, details the development, mechanical evaluation, and application methodology of high-performance epoxy resin and (PVAc) adhesives reinforced with inorganic fillers, specifically engineered for archaeological stone restoration. Epoxy resin was reinforced with silica nanoparticles (SiO2, ~50 nm) and iron powder (~50 μm) at weight fractions of 2 wt%, 5 wt%, and 20 wt%. PVAc was reinforced with nano-SiO2 at weight fractions of 2 wt%, 5 wt%, and 15 wt%. An acrylic copolymer (Paraloid B-72) was applied as an isolation coating layer on the stone surface before the application of adhesives. This intervening barrier serves to effectively safeguard the original substrate by ensuring that the adhesive does not come into direct contact with the artifact. Consequently, when removal of the adhesive becomes necessary in the future, this reversible coating facilitates the detachment process without causing any physical or chemical damage to the underlying archaeological stone. The epoxy composite with 5 wt% nano-SiO2 exhibited the highest shear strength of 46.94 MPa, while the PVAc /5 wt% nano-SiO2 composite achieved 35.90 MPa. Conversely, shear testing on aluminum substrates showed that the iron powder-reinforced epoxy outperformed the nano-silica formulation, reaching a maximum shear strength of 5.35 MPa compared to 3.94 MPa. (FTIR) spectroscopy and X-ray spectroscopy (EDS) were used to characterize the prepared adhesives. Finally, the validation of this adhesive system on stone samples—verified via (XRF) spectroscopy to be compositionally identical to Iraqi antiquities
This study deals with the green synthesis of AgNPs in an aqueous solution using leaf extract of Allium ampeloprasum to reduce silver ions and stabilize the formed nanoparticles while also determining the antioxidant activity of the nanoparticles by DPPH radical scavenging assay. The fresh leek leaves were extracted by boiling 25 g of leaves in 100 mL deionized water for 45 min at 80 °C. For the synthesis of AgNPs, 5 mL of the prepared plant extract was mixed with 45 mL of 0.1 M silver nitrate solution. The initial pH of the reaction mixture was 5.5 and it was adjusted to 12.0 using sodium hydroxide. The whole reaction mixture was then allowed to proceed at room temperature for 45 min.
The formation of AgNPs was first observed when the color changed to honey-brown/dark brown, which is due to the reduction of Ag⁺ ions. The nanoparticles were further characterized by UV–visible spectroscopy, FTIR, SEM, XRD, AFM, and DPPH assay. FTIR spectra confirmed the presence of phenolic hydroxyl, carboxyl, and protein-related amide groups in the process of reduction, capping, and stabilization. The SEM images displayed mainly spherical but aggregated nanoparticles; the crystalline nature of AgNPs was confirmed by XRD, which estimated an average size of about 45 nm. AFM images further supported the phytochemical coverage on the nanoparticle surface. The biosynthesized AgNPs showed moderate DPPH radical scavenging activity compared to ascorbic acid, which proves to be a good antioxidant. In general, the leaf extract of A. ampeloprasum is a good green synthesizer for stable AgNPs with antioxidant activity.
Stroke remains one of the leading causes of global mortality and long-term disability, with growing evidence indicating that cerebrovascular injury begins long before the onset of detectable symptoms. Silent brain infarction (SBI), an asymptomatic yet radiologically identifiable form of cerebral ischemia, is recognized as a preclinical stage of stroke that reflects ongoing vascular endothelial dysfunction. Among the molecular mediators implicated in this process, endothelin-1 (ET-1) has attracted considerable interest as a candidate biomarker to monitor vascular injury across the full stroke continuum, from the preclinical and asymptomatic phase through acute ischemic events and post-stroke recovery. This review compile current evidence on the clinical relevance of circulating ET-1 as a important monitoring biomarker in cardiovascular dissease especially stroke. We discuss ET-1 role to cerebrovascular dysfunction, endothelial activation, neuroinflammation, and examine how ET-1 levels have been associated with stroke risk, disease severity and prognosis in acute stroke settings. we also noted that the clinical translation of ET-1 monitoring is constrained by the limitations of currently available detection methods, including enzyme-linked immunosorbent assay and mass spectrometry-based platforms, which are time-intensive, laboratory-dependent, and is not relevant for point-of-care measurement. Hence, recent advances in electrochemical biosensing as an enabling technology to overcome the limitations of conventional assay, with particular focus on aptamer-based electrochemical platforms, which offer a compelling combination of specificity, sensitivity, ease of use, and rapid response. for developing a dedicated electrochemical aptasensor for ET-1, with potential applications in real-time vascular risk monitoring and stroke management.
Oral hygiene is fundamental to maintaining overall health, and antimicrobial mouthwashes play an important role in preventing oral microbial infections. This study aimed to develop and evaluate a natural antibacterial mouthwash based on a nanoemulsion incorporating green-synthesized silver nanoparticles (AgNPs) and Origanum vulgare (oregano) essential oil. The AgNPs were synthesized using epigallocatechin gallate (EGCG) derived from green tea as a reducing and stabilizing agent, while oregano essential oil, rich in carvacrol and thymol, served as the primary natural antimicrobial component. The effects of oregano oil concentration and nanosilver addition on the characteristics, stability, and antibacterial activity of an oregano oil-nanosilver nanoemulsion using Tween 80 as a surfactant were studied.
The nanoemulsion formulation was optimized by varying the concentrations of oregano oil and Tween 80. Selected formulations containing 0.5% and 1.0% oregano oil, with or without 10% AgNP dispersion, were characterized for pH, particle size, polydispersity index (PDI), organoleptic properties during eight weeks of storage, and antibacterial activity against Escherichia coli and Staphylococcus aureus. All formulations exhibited acidic pH values ranging from 3.6 to 4.5, which remained stable throughout the storage period. The nanoemulsions showed mean particle sizes of 20–45 nm with PDI values below 0.30, indicating a homogeneous nanoscale dispersion. The formulation containing 1.0% oregano oil and AgNPs exhibited particle aggregation after four weeks, whereas formulations containing 0.5% oregano oil, both with and without AgNPs, maintained physical stability over eight weeks. The incorporation of AgNPs significantly enhanced the antibacterial activity of the oregano oil nanoemulsion against both test microorganisms compared with the corresponding formulation without nanoparticles.
These findings demonstrate that the combination of green-synthesized AgNPs and oregano essential oil produces a stable nanoemulsion with enhanced antibacterial efficacy, highlighting its potential as a sustainable and effective natural mouthwash for oral healthcare applications.
Keywords: oregano oil, nanosilver, mouthwash, stability, antibacterial activity.
Lead ion (Pb²⁺) is a very poisonous heavy metal, which is widely present in industrial wastewater and poses a serious health hazard even at low quantities. Carbon quantum dots (CQDs) offer a promising alternative as a sensor for heavy metal ion detection, as they are fluorescent, non-toxic, and can be synthesized from sustainable biomass. This study utilizes raw oil palm trunk (OPT) as a green precursor for synthesizing CQDs via a rapid microwave-assisted method for Pb²⁺ detection. The photoluminescence spectra show an excitation peak decreased from 411.5 nm to 390 nm, indicating a quenching effect after Pb²⁺ was introduced in the CQDs. The bandgap energy obtained from this analysis is 4.75 eV, which was obtained by Tauc plot analysis based on UV-Vis data. The size distribution of the CQDs, which was 29.80 nm, reduced to 22.79 nm and was obtained using DLS analysis. The presence of functional groups such as hydroxyl, carboxyl, and aliphatic in the samples was demonstrated in FTIR analysis.
Chair: Dr. Nurul Huda Osman
Co-chair: Dr. Erus Rustami
Point-of-care testing (POCT) puts forward compulsory requirements on analytical platforms to realize the perfect combination of high detection sensitivity, fast response dynamics, low expenditure and single-use function. Electrochemical biological sensing devices based on printed electrodes are a revolutionary technological framework that overcome the operational limitations of traditional rigid solid-state electrodes with the scalability of additive manufacturing. This review provides a systematic appraisal of printed electrodes, based on three main pillars: conductive material, substrate support and manufacturing techniques. Electrochemical window, background capacitance and synergistic charge-transfer dynamics are reviewed for conductive matrices such as carbon allotropes, noble metals and polymer-nanoparticle composites. Rigid ceramics for high-thermal stability, flexible polymers (PET, PI, PDMS) for wearable detection, and cellulose paper for environmentally friendly, pump-free capillary microfluidic applications are included in the substrate selection. We critically compared the manufacturing approaches including mass-scalable screen printing, maskless inkjet printing and laser-induced graphene in terms of their spatial resolution, fluidic requirements and production throughput. Most importantly, the key role of surface functionalization to transform chemically inert printed surfaces into ultra-sensitive biological interfaces is emphasized. Oxygen plasma modification can introduce oxygenated functional groups (hydroxyl, carboxyl) that reduce the charge-transfer resistance by more than 1000-fold and enhance covalent antibody immobilization for disease biological marker detection. We also fabricate state-of-the-art biological interfaces including oriented aptamer self-assembled monolayers, 3D DNA tetrahedral scaffolds with sub-femtomolar detection limits (74 aM) and antifouling coatings. Finally, a rational selection scheme is proposed to optimize the electrode configurations based on the analyte chemistry, operating conditions and production scales, providing practical insights for sophisticated clinical diagnostic applications.
There is a growing need of the miniaturized and fast detecting devices in various applications like medical diagnosis, food safety, and environmental monitoring. This has been an attention that electrochemical biosensor chips have received much in recent years. The structural dimensions of interdigitated electrodes (IDEs) are small, the effective surface area is relatively large, the distribution of the electric field is concentrated, and it is possible to integrate with miniature detection system. This paper begins by presenting the fundamental design of IDEs, the key parameters of their structure, electrode materials and substrate materials. It then concludes on typical methods of detection including voltammetry and electrochemical impedance spectroscopy (EIS), as well as surface treatment of electrodes, biological functionalization, and other important metrics of performance evaluation. In this regard, gold interdigitated tetraelectrode (AuIDTE) is taken as a particular object of research in this paper, which is a AuIDTE, and the feasibility of its use as a biosensor chip is assessed on the basis of existing literature as a literature-based proof of concept. The related literature demonstrates that AuIDTEs can form biorecognition interfaces using antibodies, aptamers and nanoscale functional materials. They may also be coupled with cyclic voltammetry, differential pulse voltammetry and EIS to identify biological targets. Additional studies on the same gold interdigitated electrode platform further suggest that it can be employed in detecting proteins and disease-related biomarkers and has relatively low limits of detection. Moreover, associated research affirm that both the gold electrode and the glass substrate can undergo suitable surface chemical treatment. This offers the prerequisites necessary to build a full biosensing interface. According to the current literature, AuIDTEs seem to have an opportunity to develop biosensor chips regarding device structure, material properties, surface functionalization, and electrochemical detection. The review and proof-of-concept offered here can provide some guidance on future biosensor design, surface modification and experimental work on AuIDTEs.
Quercetin (Que) is a type of flavonoid used as potential phytochemical in treating chronic diseases due to its anti-inflammatory, antioxidant and other therapeutic effects. It is commonly taken via oral and injection approaches. However, its curative efficacy is limited by its low aqueous solubility, extensive first pass metabolism and resultant low availability. In this study, an electrospun Que-loaded poly (ε caprolactone) (Que/PCL) nanofibrous was fabricated as a potential bioactive implantable drug. The prepared nanofibers were characterized by scanning electron microscopy (SEM), Fourier trans-form infrared spectroscopy (FTIR), mechanical test, and thermogravimetric analysis (TGA). In vitro release of Que from the nanofibers was studied in a simulated physiological condition using ultraviolet-visible (UV-vis) spectroscopy. The morphological and mechanical studies showed successful formation of nanofibers with bead-less, randomly interconnected uniform structure and porosity appropriate to be used as drug delivery vehicle. Que re-lease study indicated the highest amount of Que release within 180 min period, and sustained release until day-21. Overall, the fabricated nanofibers have provided desired properties for implantable drug delivery application.
The rapid adoption of smart classrooms has significantly increased the number of connected electronic devices, resulting in higher energy consumption and greater operational complexity. Efficient energy management requires not only accurate prediction models but also reliable sensing technologies capable of continuously capturing real-time environmental and electrical conditions. Recent advances in nano-sensor technology have enabled highly sensitive, low-power, and miniaturized sensing devices that provide fine-grained monitoring for intelligent Internet of Things (IoT) environments. However, most existing energy management systems either focus solely on prediction accuracy or lack interpretable decision-support mechanisms for practical deployment.
This paper proposes a hybrid soft computing framework that integrates nano-sensor-enabled IoT monitoring, Long Short-Term Memory (LSTM), and a Mamdani Fuzzy Inference System (FIS) for predictive energy monitoring in smart classrooms. In the proposed architecture, distributed nano-sensor networks continuously acquire energy-related information, including electrical power consumption and environmental conditions such as temperature, humidity, illumination, and occupancy. The collected data are transmitted through an IoT gateway and processed by an LSTM model to capture temporal consumption patterns and forecast future energy demand. The predicted energy values are subsequently interpreted using fuzzy logic to generate human-readable energy efficiency assessments and operational recommendations.
The proposed framework was evaluated using the publicly available CU-BEMS smart building dataset, which serves as a representative benchmark for nano-sensor-based energy monitoring environments. Experimental results demonstrate excellent predictive performance, achieving a Mean Absolute Error (MAE) of 27.52 W, Root Mean Square Error (RMSE) of 50.03 W, Mean Absolute Percentage Error (MAPE) of 0.99%, and a coefficient of determination (R²) of 0.99. These results indicate that the proposed hybrid framework accurately captures temporal energy consumption patterns while providing interpretable decision support for energy optimization. The integration of nano-sensor networks with hybrid LSTM-Fuzzy intelligence offers a scalable and explainable solution for predictive energy management in smart classrooms, supporting sustainable educational facilities and next-generation intelligent building ecosystems.
Mining operations remain vulnerable to collisions between workers and heavy equipment, particularly in blind-spot areas where conventional personal protective equipment (PPE) provides only passive protection. This paper presents an intelligent safety vest that actively detects hazardous objects and provides real-time warnings to improve worker safety. The proposed system combines a Real-Time Detection Transformer (RT-DETR) for object detection with a Convolutional Long Short-Term Memory (ConvLSTM) network for motion analysis, enabling the system to recognize both the presence and movement of hazardous objects such as trucks, excavators, rocks, and pillars. To enable deployment on a Raspberry Pi 5 edge device, the RT-DETR model is quantized from FP32 to INT8 precision, reducing computational requirements while maintaining reliable detection performance. When a potential hazard is detected, an ESP32 microcontroller activates directional haptic feedback through Taptic Engine actuators, allowing workers to perceive the direction of approaching hazards without relying on visual attention. Experimental results show that the quantized RT-DETR achieves a mean Average Precision (mAP@0.50:0.95) of 0.760, while the ConvLSTM model attains an F1-score of 0.73 and a recall of 0.84 for detecting approaching hazards. In end-to-end system evaluation involving 1,490 test samples, the proposed smart vest achieved an overall accuracy of 66.24% and a recall of 94.35%, demonstrating a strong capability to minimize missed detections, which is critical for safety-critical applications. These results indicate that the proposed wearable edge-AI system provides a practical and proactive solution for enhancing occupational safety in dynamic mining environments.
The increasing volume of non-biodegradable styrofoam waste presents a significant environmental challenge. This study aims to utilize styrofoam waste as a base material for the fabrication of ultrafiltration membranes, incorporating an additive to enhance overall membrane properties and performance. The membranes were fabricated using the phase inversion method across various additive concentrations. Membrane characterization was conducted through evaluations of water flux, water contact angle, surface morphology, rejection of Spirulina platensis microalgae, and porosity. Furthermore, Analysis of Variance (ANOVA) was employed to determine the statistical significance of the experimental results. The findings revealed that the incorporation of the additive significantly improved membrane permeability. Optimal formulations yielded substantial increases in water flux, reaching up to 52.58 L/(m²·h·atm), and achieved a favorable water contact angle of 51.6°. Morphological analysis indicated that the additive influenced structural formation, notably enlarging both the pore size and the finger-like macrovoids. All membrane variations demonstrated a 100% rejection rate, indicating high efficacy in filtering microalgae, with maximum porosity reaching 87.03%. In conclusion, the use of performance-enhancing additives effectively improves the properties and filtration capabilities of styrofoam-based membranes, offering a promising solution for upcycling post-consumer expanded polystyrene waste into high-value functional materials.
Tin selenide (SnSe) doped with manganese (Mn) is a promising mid-temperature thermoelectric material owning to its ultralow lattice thermal conductivity and tunable electronic properties. Advances densification techniques such as spark plasma sintering (SPS) impose significant cost and scalability barriers. This study investigates the use of processing aid (PA) agents, ethylene glycol (EG) and stearic acid (SA) to enhance the cold press densification of Sn0.8Mn0.2Se synthesized by wet ball milling. Pellets were compacted at 200, 390, and 618 MPa and subsequently annealed at 450 °C under a nitrogen atmosphere. Structural, microstructural, thermal properties were systematically characterized using XRD, FESEM, EDX and LFA. Results demonstrate that SA at 618 MPa stabilizes the SnSe orthorhombic lattice (Sn:Se = 1.02:1), preserves p-type Seebeck behaviour, and achieves an ultralow thermal conductivity of ~0.15 Wm−1K−1 through extreme grain refinement (~21 nm) and phonon glass-like scattering. In contrast, EG at 390 MPa promotes selenium vacancy formation (VSe), driving strong n-type behaviour with higher thermal conductivity of ~0.5 Wm−1K−1. These findings establish that PA chemistry and compaction pressure are co-determining parameters that govern thermoelectric performance through competing densification and grain engineered mechanisms, offering a cost-effective and scalable fabrication pathway for Sn-based thermoelectric materials.
Chair: Dr. Mirza Ardella Saputra
Co-chair: Dr.Renan Prasta Jenie
Quasi-solid-state polymer electrolytes (QSSPEs) have attracted considerable interest for dye-sensitized solar cells (DSSCs) because they can provide improved electrolyte stability while maintaining ionic transport. In this study, the effect of propylene carbonate (PC) and 1,2-dimethoxyethane (DME) solvent composition on PVdF-TrFE-based QSSPEs was investigated. The PC:DME volume ratio was systematically varied from 100:0 to 0:100 while maintaining the PVdF-TrFE content and MPII–NaI binary salt composition at 0.20 g and 0.6 M MPII–0.4 M NaI, respectively. The prepared electrolytes were characterized in terms of ionic conductivity, structural characteristics, electrochemical impedance and photovoltaic performance. Among the investigated compositions, the 75:25 PC:DME formulation exhibited the highest ionic conductivity of 6.90 ± 0.09 mS cm⁻¹ and the lowest charge-transfer resistance of 24.6 Ω. The corresponding DSSC achieved an open-circuit voltage of 0.55 V, short-circuit current density of 13.94 mA cm⁻², fill factor of 0.56 and power conversion efficiency of 4.29%. Increasing the DME fraction beyond 25% resulted in a gradual decrease in ionic conductivity and photovoltaic performance. These results demonstrate that the PC:DME solvent composition plays an important role in regulating ionic transport, interfacial charge-transfer behaviour and photovoltaic performance of PVdF-TrFE-based QSSPEs.
Chronic Rhinosinusitis (CRS) is a heterogeneous inflammatory disease, and difficult to classify the in terms of its phenotype. The classification that is based on endotypes necessitates quantifiable biomarkers and the current laboratory-based methodology does not allow standardized sampling, as well as relatively slow turnaround time, and the possibility of measuring them outside specialized facilities is low. Electrochemical biosensors using interdigitated gold electrodes have been suggested to be a viable solution to non-invasive and fast detection of CRS biomarkers at a low cost. This paper assesses the existing situation with regards to IDGE based CRS biomarker testing in relation to three requirements; standardization of sampling, multi-plexed biomarker identification, and constant functioning in biological fluids. Direct evidence of IDGE and transferable technology are also separated throughout. Gold electrode to detect proteins, nucleic acids and small-molecule metabolites are available in the transferable technology category. These are some cases that demonstrate what is technically possible with similar systems, but they do not prove that a CRS sensor based on IDGE would be able to perform at a comparable level in nasal secretions. Clinical and methodological standardization of sampling still is a requirement; multi-analyte electrochemical devices have been demonstrated to allow multiplexed detection of CRS biomarkers in an IDGE, yet there is no proof of stability and reproducibility of complex biological fluids, which is the least fulfilled condition of the three. Based on this, it is best to view the CRS diagnosis based on the IDGE as being a feasible technical option that has not been proven clinically. The information about the stability and reproducibility of the same matrix, and the ability to identify several markers simultaneously with the cross-reactivity identified.
Adsorption behavior of molecules on 2 - dimensional materials such as graphene are a key factor influencing its potential for sensing application. This study reported the interaction of humidity molecules H$_2$O at different adsorption position on graphene/hexagonal boron nitride (hBN) that has been patterned with constriction simulated through Density Functional Theory (DFT). Three possible adsorption sites including those near to the constriction region was simulated and analyzed to evaluate its effect on the energy band gap variation, projected density of states (PDOS), adsorption energy, recovery time, charge transfer and current – voltage (I – V) characteristics. This was evaluated for both narrow and wide constriction in Graphene/hBN geometry. The simulation results indicate the electronic properties such as the energy and PDOS exhibit a minimal effect for all adsorption sites. However, the adsorption energy and charge transfer are found to be dependent on the adsorption sites particularly stable adsorption was achieved at both NC and WC edges. Although different adsorption sites exhibit noticeable variations in adsorption energy and charge transfer, they have only a minimal impact on the current response in the I–V characteristics. These findings demonstrate that the sensing layer geometry plays a significant role in tuning the sensitivity of Graphene/hBN-based devices.
Recent advances in nanoelectronic sensing technologies have enabled the development of compact and intelligent wearable systems for occupational safety applications. This paper presents an IoT-based smart safety vest that leverages miniaturized nanoelectronic wearable sensors to continuously monitor workers' physiological and environmental conditions while providing active feedback for early hazard prevention. The proposed system integrates physiological data acquired from a smartwatch with environmental parameters collected through embedded sensing modules installed on the vest. A low-power embedded controller performs real-time data acquisition, processing, and wireless transmission to a cloud-based monitoring platform for centralized supervision. When abnormal physiological conditions or hazardous environmental situations are detected, the system immediately activates active feedback mechanisms, including vibration alerts, audible alarms, and visual indicators, to notify workers and improve situational awareness. Furthermore, the web-based dashboard enables supervisors to monitor workers' health status, environmental conditions, and location information in real time, facilitating rapid decision-making during emergency situations. By combining nanoelectronic wearable sensing, embedded IoT architecture, and intelligent feedback mechanisms, the proposed smart safety vest provides a scalable and practical solution for enhancing occupational safety in construction, manufacturing, mining, and energy industries. The proposed approach also demonstrates the potential of wearable nanoelectronic systems for future smart industrial safety and digital occupational health applications.
The increasing demand for energy storage drives the development of new anode materials to enhance the performance of lithium-ion batteries (LIBs). The commonly used graphite anode materials still possess limitations in terms of lithium-ion storage capacity, ion diffusion rate, and structural stability during battery operating cycles. This study aims to evaluate the potential of C24, B12N12, Be12O12, and Al12P12 nanocage structures as LIB anode materials using a Density Functional Theory (DFT) approach. Calculations were performed using the M06-2X functional and the 6-31+G(d) basis set. The parameters investigated include adsorption energy, energy gap, and theoretical voltage. Simulation results demonstrate that all nanocage structures are capable of interacting stably with Li atoms, as indicated by negative adsorption energies. The presence of Li atoms affects electron distribution and reduces the energy gap values across all materials, signifying an improvement in electronic conductivity. The C24 and Al12P12 structures exhibit better electronic characteristics compared to B12N12 and Be12O12 due to their lower energy gaps after Li adsorption. Theoretical voltage analysis reveals that only C24 and Al12P12 yield a positive voltage, making them more promising for application as LIB anode materials. Based on the evaluation of all parameters, the C24 structure delivers the most superior performance and holds the potential to be a candidate for lithium-ion battery anodes in future energy storage applications.
Structuring liquid vegetable oils into semi-solid networks provides a viable route to replace saturated and trans fats, which oleogel dispersed in aqueous food matrices require interfacial protection to remain physically stable. Herewith, an olive oil nano-oleogel was structured with glyceryl monostearate (GMS) and encapsulated within a lecithin–pectin bilayer assembled by electrostatic bilayer deposition. GMS with 3, 5, and 7% (w/w), and 5% was selected as the optimal structurant on the basis of dynamic light scattering with size of 789 nm. Bilayer nano-oleogels were subsequently constructed at three lecithin:oleogel ratios (1:1, 1:2, and 1:4, v/v; oleogel fixed at 20 mL) across pH 3–5 and characterized by particle size analysis, zeta potential, ATR-FTIR, static viscosity, and oil binding capacity (OBC). pH 5 produced the most stable colloidal dispersion through near-complete ionization of pectin carboxyl groups. At pH 5, every formulation surpassed zeta potential in < - 30 mV stability threshold (−45.13, −52.83, and −68.17 mV for 1:1, 1:2, and 1:4), the surface charge becoming progressively more negative as the anionic low-methoxyl pectin contribution outweighed zwitterionic phosphatidylcholine at the interface. A pronounced functional trade-off emerged: OBC declined as lecithin volume decreased (94.99, 72.41, and 54.43%), whereas static viscosity rose sharply (463.0, 1760.0, and 2620.0 cP). ATR-FTIR showed no major band shifts, indicating stabilization governed by non-covalent physical interactions rather than new covalent linkages. Overall, the 1:1 formulation at pH 5 offered the best balance of oil immobilization, particle homogeneity, and electrostatic stability, positioning bilayer-encapsulated olive oil nano-oleogels as a clean-label platform for structured-lipid and lipophilic-bioactive delivery applications.
Monitoring the removal of synthetic dyes such as Rhodamine B (RhB) during catalytic treatment typically relies on UV–Vis spectrophotometry, an accurate but laboratory-bound and costly technique. This preliminary study develops a low-cost integrated red–green–blue (RGB) sensor (TCS34725/ESP32) operating in transmission mode and evaluates its suitability for time-resolved monitoring of the Ag–SiO₂-catalyzed reduction of RhB by sodium borohydride. Calibration of the sensor against RhB standards was performed using previously synthesized Ag-SiO2 nanocatalyst. Among the light-source and detection-channel combinations examined, a yellow light source paired with the red channel gave the best (most linear and sensitive) response (R² > 0.99); this combination places the illumination on the long-wavelength flank of the RhB absorption band (λmax ≈ 554 nm) while matching the emission to the responsivity of the red channel. A single proof-of-concept run demonstrated that the platform follows the reduction in real time rather than only at its end-point. Critically, the study identifies the practical challenges that currently preclude quantitative kinetic determination — most notably interference from hydrogen bubbles evolved during the reaction, together with the need for replication and the characterization of analytical figures of merit. By mapping these obstacles and proposing mitigation strategies, this work provides a foundation and roadmap toward a validated, low-cost tool for time-resolved and, prospectively, process-analytical monitoring of catalytic dye reduction.
Keywords: Rhodamine B; catalytic reduction; Ag–SiO₂; RGB colorimetry; low-cost sensor; time-resolved monitoring; process analytical technology.
Chair: Prof. Ir. Dr. Mohd Khairuddin Md Arshad
Chair: Dr. Raden Joko Kuncoroningrat Susilo
Co-chair: Dr. Nur Hamidah Abdul Halim
Preparation of interdigitated gold microelectrode surface (IDGME) for silane-reaction with the aim for biomolecular interactive analysis for high bioreactivity with reliable electrical response. This experiment involves optimization on IDGME surface using non-thermal oxygen plasma for (3-Aminopropyl)triethoxysilane (APTES) functionalization. Bare IDGME chips were characterized using a real-time current-voltage (I-V) analysis at voltage ranges from -4 to +4, supplied by picoammeter. About 20 µL de-ionized water was initially introduced to the surface of the chip followed by electrolyte scouting at pH ranged from 1 to 12, performed with the above measurements. The surface morphology of the bare chip was investigated using high-power microscopy. Separate IDGME chips were exposed to oxygen plasma for 10, 20 and 60 sec for surface activation, after which electrolyte scouting was performed and compared with chemical surface hydroxyl etching. These chips were loaded with 0.5, 1 and 2% APTES with 30% ethanol as solvent. The bare chip was first loaded with 20 µL of 1 M of KOH solution for 5 min, afterthat loaded with APTES solution, whereas the plasma-treated chips were loaded directly with APTES for 30 mins each, and I-V measurements were carried out. The above pilot experimental analysis provided a basis for identifying a suitable IDGME surface preparation in order to get a highly sensitive interaction for biomolecular analysis.
Keywords: Oxygen plasma, (3-Aminopropyl)triethoxysilane, Biosensor, Gold electrode
This research highlights the preparation nanomaterials from resources, such as biomasses, by addressing ‘waste-to-nanomaterials’. Transforming biological wastes into functional nanomaterials sustains waste management and supports technological innovation and environmental protection. The strategies involve ‘green-technologies’ for large scale commercialization strategies to generate waste-derived nanomaterials. This study reveals the production of different particles at ‘nanoscale’ from different resources. Considering the biomasses from plants and animals/insects, they have been displayed for downstream potentials. With the residues of organic and inorganic compounds from these materials, work as reducing and capping agents and to be functional. Herein, different modified metallic and non-metallic functional nanomaterials were revealed with their high efficiency for their downstream applications in different industrial sectors.
Keywords: Nanomaterial, Biowaste, Greener method, Carbon material.
Silver nanoparticles (AgNPs) have attracted considerable attention as catalysts due to their high surface reactivity, yet their tendency to aggregate limits reusability and long-term performance. In this study, calcined eggshells—a low-cost, calcium-rich waste material—were employed as a support matrix to stabilize AgNPs synthesized via the polyol method, using polyvinylpyrrolidone (PVP) as a capping agent and ethylene glycol as both solvent and reducing agent. AgNPs were prepared at four AgNO3 precursor concentrations (12.5, 25, 37.5, and 50 mM) to evaluate the effect of precursor loading on particle formation and catalytic activity. The resulting composites were characterized using UV-Vis spectroscopy to confirm surface plasmon resonance at 400-450 nm, FTIR to identify functional groups involved in nanoparticle stabilization, XRD to determine crystallinity and phase composition, and SEM to observe surface morphology and particle distribution on the eggshell support. Catalytic performance was evaluated through the reduction of Rhodamine B (RhB), a model organic dye pollutant, in the presence of a reducing agent (NaBH4). Results demonstrate that AgNO3 concentration significantly influences nanoparticle size, distribution, and dispersion on the support, which in turn affects catalytic efficiency that was completed within 14-18 minutes. This work highlights the potential of biogenic waste-derived materials as sustainable supports for metal nanoparticle catalysts in environmental remediation applications.
Keywords: silver nanoparticles, calcined eggshell, polyol, rhodamine B, catalysis
Liposomal formulations have attracted considerable interest as sustained drug delivery systems for local anesthetics because they can prolong drug release and potentially reduce dosing frequency. Accurate determination of encapsulation efficiency (EE) and in vitro drug release is therefore essential for evaluating formulation performance. This study validated and applied a reversed-phase high-performance liquid chromatography (RP-HPLC) method for quantitative analysis of ropivacaine in liposomal formulations. Chromatographic separation was achieved using a Purospher Star RP-18 C18 column (150 × 4.6 mm, 5 µm) with a mobile phase consisting of acetonitrile:methanol:water (40:30:30, v/v/v) containing 0.1% triethanolamine at a flow rate of 1.5 mL/min. Detection was performed at 240 nm with an injection volume of 20 µL. The analytical method was validated according to specificity, linearity, limit of detection (LOD), limit of quantification (LOQ), accuracy, and precision. No interference from the liposomal matrix was observed at the retention time of ropivacaine (7.48 min), confirming good specificity. Excellent linearity was obtained over the concentration range of 5–200 ppm (r = 0.9997), with an LOD of 7.77 ppm and an LOQ of 23.39 ppm. Recovery values ranged from 98.02% to 101.91%, while relative standard deviation (%RSD) values remained below 2%, demonstrating good accuracy and precision. The validated RP-HPLC method was successfully applied to determine encapsulation efficiency and evaluate the in vitro release profile of liposomal ropivacaine formulations. These results demonstrate that the proposed method provides a reliable and reproducible analytical approach for quantitative evaluation of liposomal ropivacaine during formulation development and quality assessment.
Biosynthesis has attracted considerable attention as a sustainable and environmentally friendly approach for the production of metal oxide nanoparticles with potential biomedical applications. In the present study, Fe₃O₄ nanoparticles were successfully synthesized using mangosteen (Garcinia mangostana) peel extract as a natural reducing and stabilizing agent. The biosynthesized nanoparticles were subsequently incorporated into a chitosan matrix to fabricate Fe₃O₄/chitosan nanocomposites, and their physicochemical properties and cytotoxic activity against MCF-7 breast cancer cells were evaluated. The influence of mangosteen peel extract concentration (5% and 10% w/v) on nanoparticle formation and the effect of chitosan concentration (0.5%, 1%, and 2% w/v) on nanocomposite characteristics were systematically investigated. UV–Visible spectroscopy revealed a characteristic absorption peak at approximately 294 nm, confirming Fe₃O₄ nanoparticle formation. Particle size analysis demonstrated that nanoparticles synthesized using 5% extract exhibited superior characteristics, with an average particle size of 254.65 nm and a polydispersity index (PDI) of 0.1172. Following incorporation into chitosan, the optimum nanocomposite was obtained using 1% chitosan, exhibiting a particle size of 374.65 nm and a PDI value of 0.1686. FTIR analysis confirmed the presence of Fe–O vibrations and interactions between Fe₃O₄ nanoparticles and chitosan functional groups, while XRD patterns indicated predominantly amorphous structures with characteristic magnetite-related diffraction peaks. SEM–EDX analysis further verified successful nanocomposite formation through the detection of Fe, O, C, and N elements.
Cytotoxicity evaluation using the MTT assay demonstrated concentration-dependent inhibition of MCF-7 cell proliferation, reaching a maximum inhibition of 42.64 ± 7.72% at 400 μg/mL. Although the IC50 value was not achieved within the investigated concentration range, the synthesized nanocomposite exhibited measurable biological activity and favorable physicochemical properties. These findings suggest that Fe₃O₄/chitosan nanocomposites produced through mangosteen peel-mediated green synthesis possess potential as environmentally friendly magnetic biomaterials for future biomedical applications.
Torsades de Pointes (TdP) is a life-threatening ventricular arrhythmia that originates when a drug compound inhibits cardiac ion channels at the nanoscale, altering the ionic currents underlying the cardiac action potential. Translating this nanoscale inhibition into a clinically actionable risk category remains central to the Comprehensive in vitro Proarrhythmia Assay (CiPA) paradigm, yet the three TdP risk classes (low, intermediate, high) are inherently ordinal, a structure conventional classifiers typically disregard. Here, in silico simulations were performed using the optimized IKr-dynamic O'Hara-Rudy (CiPAORdv1) ventricular cell model for the 28 CiPA reference drugs at four concentrations (1-4× Cmax), parameterized using manual patch-clamp ion channel pharmacology data. Eleven biomarkers (qNet, dvdtmax, vmax, vrest, APD50, APD90, max_dv, camax, carest, CaTD50, CaTD90) were extracted from the resulting action potentials and calcium transients and used to train a COnsistent RAnk Logits (CORAL) ordinal artificial neural network with a single hidden layer. Because CORAL's rank-monotonic logits directly encode the ordinal relationship among risk classes, a Torsade Metric Score (TMS) was derived from these logits indicating higher scores intuitively correspond to higher risk. The resulting ordinal deep learning model achieved classification performance comparable to the established qNet metric, while additionally producing an interpretable, continuous TMS rather than a discrete label alone. These findings indicate that ordinal deep learning can serve as a computational bridge between nanoscale drug-ion channel inhibition and clinically meaningful, intuitively interpretable TdP risk stratification, offering a methodological foundation for extending nanobiotechnology-informed cardiac drug safety assessment beyond conventional, non-ordinal classification approaches.
Abstract:
Background: Antibiotic resistance among pathogenic bacteria is a growing global health concern, propting the search for effective and environmentally friendly alternatives to antimicrobial. Silver has been used as an alternative, including in the synthesis of nanoparticles, to treat burns, wounds, and many infections caused by pathogenic bacteria. Objective: This study aimed to biosynthesis silver nanoparticles using Solanum melogena leaf extract and assess their antibacterial activity against selected human pathogens. Methods: Aqueous extracts of sesame plant leaves were prepared and reacted with silver nitrate (AgNO3) solution. The formation of silver nanoparticles (AgNPs) was monitored by opticl color change and characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD) and Atomic force microscopy (AFM). The antibacterial activity of the synthesized nanoparticles against Escherichia coli and Staphylococcus aureus was tested using the agar well diffusion method. Results: the average size of the synthesized silver nanoparticles was verified to be 37.98 nm, confirming that the y were indeed nanoparticles. The antibacterial activity of the synthesized silver nanoparticles was evaluated against both Gram-positive and Gram-negative bacteria, and they were highly against these pathogens. Main Conclusions: S. melogena leaf extract is an effective green agent for synthesizing antibacterial silver nanoparticles with potential biomedical applications.
Keywords: Solanum melongena, Antibacterial activity, Silver nanoparticles.
Oil palm empty fruit bunch (OPEFB) is one of Indonesia's abundant lignocellulosic biomass wastes and has great potential as a raw material for producing nanocrystalline cellulose (NCC) due to its high cellulose content. The quality of NCC is strongly influenced by the pretreatment process prior to acid hydrolysis; therefore, an effective pretreatment method is required to enhance the removal of non-cellulosic components while preserving the crystalline structure of cellulose. This study aimed to investigate the effect of steam-assisted hydrochloric acid (HCl) pretreatment on the optical properties of nanocrystalline cellulose isolated from OPEFB. The synthesis process began with 37 wt% HCl pretreatment, followed by steam pretreatment at room temperature for 24 h to facilitate the disruption of the lignocellulosic structure and improve cellulose fiber accessibility. Subsequently, the treated cellulose was hydrolyzed using 60 wt% sulfuric acid (H₂SO₄) to produce nanocrystalline cellulose particles. The optical properties of the obtained NCC were characterized using UV–Visible spectroscopy over a wavelength range of 200–800 nm, while the optical band gap energy was determined using the Tauc Plot method. The results demonstrated that the steam-assisted HCl pretreatment produced nanocrystalline cellulose exhibiting a dominant absorption in the ultraviolet region and a wide optical band gap, indicating the characteristics of a wide-band-gap semiconductor material. These findings suggest that steam-assisted HCl pretreatment enhances the quality of the nanocrystalline structure, thereby improving the optical properties of NCC and highlighting its potential for environmentally friendly optoelectronic and light-sensing applications.
Keywords: oil palm empty fruit bunch, nanocrystalline cellulose, steam-assisted hydrochloric acid pretreatment, optical properties, UV–Visible spectroscopy, optical band gap.
Chair: Assoc. Prof. Ts. Dr. Foo Kai Loong
Co-chair: Dr. Fadjar Mulya
Additive manufacturing has emerged as a transformative method for fabricating monolithic, assembly-free tactile sensors. However, the operational performance of these sensors depends not only on the intrinsic properties of the base functional materials but also heavily on the structural architecture of the sensors. Modern 3D printing technologies enable precise control over this architecture by allowing adjustments to infill density and geometric infill patterns. This paper presents a review exploring the specific electro-mechanical effects of infill parameters on 3D-printed tactile sensors. We systematically analyze how varying densities across different slicing architectures induce mechanical and electrical modifications in piezoresistive composites and flexible capacitive dielectrics of the tactile sensors.
Home-based resistance training has grown rapidly as a setting for unsupervised exercise, motivating wearable systems that provide automated feedback such as form correction, repetition counting, and load adjustment. Delivering that feedback first requires detecting which lower-limb movement the user is performing, and surface electromyography (sEMG) is well suited to this because each exercise produces a distinguishable muscle co-activation pattern. However, conventional sEMG depends on precise, expert electrode placement and on wet Ag/AgCl gel that dehydrates and irritates the skin; even in controlled laboratory settings its validity is degraded by placement sensitivity, electrode shift during dynamic contraction, crosstalk, and impedance drift, and these problems are magnified in unsupervised home use. Dry electrodes are therefore the realistic option, and graphene has become the most intensively studied dry-electrode material because its combination of high conductivity, mechanical flexibility, and durability under sweat and repeated bending directly targets the failure modes of gel and conventional dry electrodes. This makes a focused appraisal of graphene EMG timely: the graphene-electrode literature has grown quickly but remains scattered across materials and body sites and has never been assessed for the dynamic lower-limb exercise application, so it is not yet known whether graphene can meet the demands of the home gym. Accordingly, this study reviews conventional wet and dry sEMG for lower-limb movement classification and its limitations and asks whether such classification has been demonstrated in gym or home settings; introduces graphene-based EMG and compares graphene material variants quantitatively for durable, reusable dry electrodes; and examines whether graphene-based EMG sensors have been validated in real movement, analysing their measured performance against conventional electrodes. The central finding is that, although graphene dry electrodes match or exceed conventional electrodes under static conditions, no graphene-based EMG sensor has been benchmarked against conventional dry electrodes during dynamic, multi-joint lower-limb resistance exercise.
Abstract. This study presents a structural layer separating method using centrifugation to isolate high-quality, transparent few layer reduced graphene oxide (rGO) from thick black rGO cluster residues. The separated supernatant fractions named as layers 1–4 were drop-casted onto rigid glass and flexible Polyethylene Terephthalate (PET) substrates. High power microscopy con-firmed that the top fractions (Layer 1 and 2) yielded highly uniform, transparent, and shimmering thin films, whereas the bottom fractions (Layer 3 and 4) formed dense macroscopic aggregates on glass and PET due to aggregation of black particles on bottom of the layers. Raman spectroscopy successfully mapped the carbon fingerprints (D and G bands) against the intrinsic carbonyl stretching modes of the substrate. Electrical evaluations revealed that Layer 1 achieved the lowest base sheet resistance alongside unique, voltage-dependent charge-trapping behavior. These multi-analytical insights demonstrated that targeting the topmost centrifugal supernatant fractions provides the optimal morphological and electrical properties suitable for flexible electronics.
Keywords: Reduced Graphene Oxide, Centrifuges, Glass, Polyethylene Terephthalate (PET), High power microscope (HPM), Raman spectroscopy, Bio-sensing, Sheet resistance, Flexible.
Improving optical absorption in the active layer is one of the key approaches to enhancing solar cell performance. Surface structuring has been recognized as an effective strategy to reduce optical losses and increase light trapping within the absorber layer. This study presents a simulation study of active layer surface structuring for enhanced optical absorption in solar cells using the Finite-Difference Time-Domain (FDTD) method. Five surface structures, namely isosceles triangle, inclined triangle, cone, spherical, and cylindrical geometries, were investigated and compared with a conventional flat surface for silicon (Si) and cadmium selenide (CdSe) active layers. Optical absorption was determined from the simulated reflection and transmission spectra. The simulation results show that the influence of surface structuring depends strongly on the active layer material. For Si, the cone structure exhibited the highest total absorbance of 59.38%, representing a significant improvement compared with the flat surface (34.57%). In contrast, the inclined triangular structure achieved the highest total absorbance for CdSe (90.37%), followed closely by the isosceles triangular structure (90.13%), compared with 77.97% for the flat configuration. These findings demonstrate that appropriate surface structuring can substantially enhance optical absorption and provide useful insights for the design and optimization of high-performance solar cells.
Abstract. Background: Non-invasive hemoglobin (Hb) measurement has gained considerable interest as a painless alternative to blood sampling for anemia screening and continuous monitoring. Despite rapid advances in spectroscopy and optical sensing, clinical implementation remains limited by inconsistent diagnostic performance and heterogeneous validation methods.
Methods: This systematic review was conducted in accordance with the PRISMA 2020 guidelines. Literature published between 2015 and 2025 was identified through predefined search queries and citation chaining. Of 365 candidate studies, 344 were relevant, and 213 highly relevant studies were included in the qualitative synthesis. Data were extracted on sensing technology, optical principles, inference algorithms, confounder mitigation, validation methods, and diagnostic performance.
Results: Four major technology categories were identified: multi-wavelength photoplethysmography (37.6%), spectroscopy and hyperspectral imaging (30.0%), smartphone-based imaging (19.7%), and calibration and robustness strategies (12.7%). Machine learning methods, particularly convolutional neural networks, support vector regression, XGBoost, and Vision Transformers, increasingly complemented physics-based optical models. Representative studies reported MAE as low as 0.25 g/dL, RMSE of 0.81–1.88 g/dL, correlation coefficients up to r = 0.987, and diagnostic accuracy exceeding 99% under controlled conditions. However, measurement accuracy remained strongly affected by skin pigmentation, motion artifacts, illumination variability, anatomical measurement site, and calibration transferability.
Conclusions: Spectroscopy integrated with artificial intelligence demonstrates strong potential for non-invasive Hb assessment. Nevertheless, standardized validation protocols, multicenter clinical studies, and improved robustness across diverse populations are essential before widespread clinical adoption.
Thin-film Barium Strontium Titanate (BST)-based light sensors have significant potential for application in various optical detection systems due to their excellent dielectric characteristics and photoresponsive properties. However, the development of these sensors has primarily focused on material characterization and sensing performance, while the mechanical aspect, particularly the protective casing that supports operational reliability, has received limited attention. The absence of a protective casing increases the risk of damage caused by mechanical impact, dust, humidity, and improper handling, thereby reducing the reliability and service life of the sensor. This study aims to design and fabricate a casing for a thin-film BST-based light sensor that provides mechanical protection while preserving the optical functionality of the sensing element. The casing was designed by considering the sensor dimensions, active sensing area, electrical connection pathways, ease of assembly, and unobstructed light access to the sensor surface. Subsequently, the casing was fabricated using polylactic acid (PLA) material to meet the application requirements. The fabricated casing was evaluated in terms of dimensional accuracy, ease of assembly, mechanical stability, and compatibility with the sensor system.
keyword: design and fabrication of a casing; thin-film Barium Strontium Titanate (BST); light sensor
This study presents a systematic investigation into the chemical etching kinetics, geometric optimization, and evanescent field dynamics of unclad multimode optical fibers for refractive index sensing applications. Cladding removal was controlled using hydrofluoric acid (HF) at distinct concentrations of 30.6% and 49%. Experimental results demonstrate that the etching rate is highly dependent on both acid concentration and fiber core dimensions; a 49% HF concentration significantly accelerated the cladding removal process down to 3 minutes for a 105 µm core fiber, compared to 20 minutes for a 50 µm core and 32 minutes for a 9 µm core fiber. The etching deceleration observed near the core boundary is primarily attributed to the structural composition and presence of dopants within the fiber core. Furthermore, mathematical modeling of the evanescent wave penetration depth within U-shape fiber configurations was conducted at a baseline wave-length of 1310 nm. Analytical assessments indicate that larger core diameters (105 µm versus 50 µm) yield an enhanced penetration depth, increasing from 0.218 µm to 0.236 µm at a launch height of h = 0 µm, thereby improv-ing core-ambient field interactions. Conversely, smaller core diameters optimize the spatial reflection frequency along the interaction zone. The penetration depth expanded significantly as the surrounding refractive index approached the core index, yielding intentional, measurable optical power leakage. These findings offer a comprehensive structural framework for designing highly optimized, geometrically tailored evanescent field fiber sensors.
Strontium titanate (SrTiO₃) is a perovskite oxide with excellent dielectric and optical properties, making it a promising material for photocatalytic, sensing, and optoelectronic applications. This study investigates the effect of Ferrum (II) acetate doping on the crystal structure and optical properties of SrTiO₃ ceramics synthesized using the conventional solid-state reaction method. Ferrum (II) concentrations of 0, 0.5, and 1.0 wt.% were introduced into the SrTiO₃ matrix, followed by annealing at 850 °C for 8 h. The synthesized ceramics were characterized using X-ray diffraction (XRD), and UV–Visible spectroscopy (UV–Vis). XRD analysis confirmed that all samples retained a single-phase cubic perovskite structure, while the lattice parameter gradually decreased from 3.909 Å to 3.899 Å with increasing dopant concentration, indicating successful incorporation of Fe ions into the crystal lattice. Furthermore, UV–Vis analysis demonstrated a gradual reduction in the optical bandgap from 3.54 eV to 3.23 eV as the dopant concentration increased. The observed lattice contraction and bandgap narrowing indicate that Ferrum (II) acetate doping effectively tailors the structural and optical characteristics of SrTiO₃ ceramics, highlighting their potential for future optoelectronic, photocatalytic, and semiconductor applications.
Keywords: Crystal Structure; Ferrum (II); Optical Properties; Solid- State Reaction; Strontium Titanate (SrTiO₃).
Chair: Dr. Nor Azizah Parmin
Co-chair: Dr. Retno Sari
Propeller imbalance is one of the most frequent causes of excessive vibration in multicopter UAVs. It often occurs after a crash, a hard landing, or when the propeller clips an obstacle during flight, and the resulting vibration can reduce the maneuverability of the drone. Instead of using an accelerometer on each arm, which increases airframe mass and adds extra data channels that the flight controller must sample and process, this work aims to develop an arm structure that can sense its own vibration condition as part of the airframe. A 3D-printed insert is designed to fit inside the carbon tube arm, carrying a strip of carbon-based conductive PLA as the sensing element. The conductivity of this filament comes from a network of carbon nanofillers dispersed in the polymer. When the arm flexes under vibration, the filler network changes its configuration and causes a measurable resistance change. The insert is placed near the arm-to-center-plate joint, where bending strain from propeller-induced vibration is expected to concentrate. The main part is produced using standard FDM 3D printing, followed by surface finishing and electrode preparation on the sensing element. The sensor output is passed through a signal conditioning circuit before being read by a microcontroller, and the signal is analyzed in the time domain (RMS amplitude) and frequency domain (FFT, focusing on the 1× rotational frequency component) to distinguish balanced from imbalanced propeller conditions. This concept can be developed further for multicopter arms that monitor their own mechanical condition during flight, as a lightweight and low-cost option for in-flight structural health monitoring.
Coffee-pulp-derived carbon quantum dots (CQDs) were synthesized via a hydrothermal method and systematically characterized to assess their potential for energy storage applications. Photoluminescence studies revealed tunable emission and a high quantum yield, indicating well-passivated surfaces with abundant functional groups. SEM analysis revealed a porous and fragmented electrode morphology with visible cavities, which enhances electrolyte accessibility. Electrochemical evaluation of the CQD-based electrodes exhibited nearly rectangular cyclic voltammetry (CV) profiles, achieving a maximum specific capacitance of 133 F/g at 5 mV/s. Galvanostatic charge-discharge (GCD) measurements yielded a specific capacitance of 51.5 F/g, an energy density of 10.3 Wh/kg, and a power density of 309.8 W/kg at a current density of 0.5 A/g⁻. These findings demonstrate that upcycled coffee pulp is a sustainable precursor for producing CQDs with promising electrochemical energy storage performance.
Ba₀.₅Sr₀.₅TiO₃ (BST) thin films have attracted considerable attention for optoelectronic and sensing applications owing to their tunable optical characteristics. This study investigates the effect of manganese (Mn) doping on the optical properties of BST thin films with Mn concentrations of 0.0, 0.5, and 1.0 wt.%. The thin films were synthesized using the Chemical Solution Deposition (CSD) method and deposited onto p-type Si(100) substrates. Film thickness and optical properties were evaluated to determine the influence of Mn incorporation on the electronic structure of the BST films. The average thickness increased from 12.70 nm for the undoped BST film to 17.29 nm and 31.28 nm for the 0.5 wt.% and 1.0 wt.% Mn-doped films, respectively, indicating enhanced film growth with increasing Mn concentration. Optical analysis revealed that the band gap energy increased from 2.50 ± 0.51 eV for the undoped film to 3.15 ± 0.10 eV at 0.5 wt.% Mn, followed by a decrease to 2.29 ± 0.25 eV at 1.0 wt.% Mn. Conversely, the Urbach energy decreased from 0.16 ± 0.04 eV to 0.13 ± 0.04 eV at 0.5 wt.% Mn, suggesting reduced structural disorder, before increasing to 0.24 ± 0.07 eV at 1.0 wt.% Mn due to increased defect states. These findings demonstrate that Mn doping significantly modifies the optical characteristics of BST thin films by influencing the optical band gap and structural disorder. The observed variations indicate that Mn incorporation effectively alters the electronic structure of BST, highlighting its potential for tailoring the material properties for future optoelectronic and optical sensing applications.
Keywords: Band gap; Ba₀.₅Sr₀.₅TiO₃ thin film; Chemical Solution Deposition; Manganese doping; Urbach energy.
Barium Strontium Titanate is a perovskite material known for its tuneable optoelectronic properties. Its optical properties are influenced by many factors including dopant incorporation and processing history. In this research, copped – doped Ba0.375Sr0.675TiO3 thin films were prepared from a seven – month old precursor. The study aims to investigate the combined effects of Cu incorporation and precursor aging on optical properties. Films with Cu concentrations of 0.0%, 0.5%, 1.0%, and 1.5% were evaluated for its thickness as well as utilizes UV – Vis spectroscopy to obtain bandgap and Urbach energy estimation, and X – Ray diffraction to evaluate the crystal structure of the film. Whilst film thickness may be influenced by aging, non – monotonic pattern was visible for increasing copper dopant concentration. Fresh undoped film showed a thickness of ~1386 nm whereas fresh undoped film showed a thickness of 22.91 nm. Additionally, the aged undoped film exhibited a direct optical bandgap of ~3.57 eV, higher than the non – aged undoped film at ~2.89 eV. Altered bandgap suggests that precursor aging may modify the optical properties of films. XRD results suggests that the films are tetragonal with a lattice parameter of a = 3.454 Å and c = 3.701 Å.
Ba₀.₇₅Sr₀.₂₅TiO₃ thin films doped with ruthenium (0.0% and 0.5%) were successfully fabricated on p-type Si (100) substrates using the Chemical Solution Deposition (CSD) method assisted by spin coating at a rotation speed of 3000 rpm for 30 seconds, with the coating process repeated three times. A precursor solution with a concentration of 0.5 M was employed and aged for seven months prior to the deposition process. The prepared thin films were subsequently characterized by measuring their thickness and investigating their optical and crystal structural properties. Whilst film thickness may be influenced by aging, non – monotonic pattern was visible for increasing Ruthenium concentration. Fresh undoped film showed a thickness of ~54.512 nm whereas fresh undoped film showed a thickness of 30.238 nm. Additionally, the aged undoped film exhibited a direct optical bandgap of ~3.57 eV, higher than the non – aged undoped film at ~3,09 eV. Altered bandgap suggests that precursor aging may modify the optical properties of films. XRD results suggests that the films are cubic with a lattice parameter of a = 3.830 Å.
Quantum cascade lasers (QCLs) are highly efficient sources of mid-infrared and terahertz radiation, but they require precise numerical modeling to optimize their performance. This study aims to comprehensively model the dynamics of QCLs by analytically deriving the Maxwell-Bloch equations. These coupled nonlinear equations are then discretized using the Finite Difference Time Domain (FDTD) method to simulate the propagation of electromagnetic fields and the dynamics of charge carriers in the time domain. Simulation results show that the evolution of the electric field (E) toward a steady state is strongly influenced by the pumping ratio parameter. In addition, the stability of the electric field is also significantly influenced by other physical parameters, such as the dephasing time, the upper state lifetime, cavity losses, and the transition dipole matrix elements. This modeling provides an in-depth understanding of the nonlinear dynamics in QCLs and can be used as a reference for experimental design to achieve stable and coherent laser emission.
Microfluidic systems have become an essential platform for biomedical and drug delivery applications due to their ability to precisely manipulate minute volumes of fluids within microscale channels. Conventionally, polydimethylsiloxane (PDMS) microfluidic devices are fabricated using SU-8 molds produced through photolithography, which requires cleanroom facilities, expensive chemicals, and complex fabrication procedures. This study presents a low-cost and simplified fabrication approach by replacing the conventional SU-8 mold with a stereolithography (SLA) 3D-printed mold for the fabrication of PDMS microchannels integrated with a non-contact electrode for electroosmotic flow (EOF) applications. The fabricated microchannel was experimentally evaluated using sodium chloride (NaCl) solutions at different concentrations to investigate its fluidic performance and electroosmotic characteristics. Leakage and flow stability were first assessed by varying the syringe pump flow rate from 2 to 12 mL/h, confirming stable fluid transport without leakage throughout the tested range. Electroosmotic performance was subsequently evaluated by applying voltages between 1 and 2 V revealed that the conventional contact electrode configuration exhibited a 15–30% reduction in flow velocity with increasing NaCl concentration. In contrast, the proposed non-contact electrode demonstrated a linear increase in electroosmotic flow velocity with increasing applied voltage, achieving an approximately 88% enhancement in flow performance compared with the contact electrode configuration. The improved electroosmotic performance is attributed to the elimination of direct electrode-fluid interaction, thereby minimizing electrode degradation and electrochemical reactions while maintaining efficient electroosmotic actuation. The proposed fabrication method provides a simple, cost-effective, and reliable alternative for developing electroosmotic microfluidic devices and demonstrates strong potential for integration into portable biomedical and controlled drug delivery systems.
Abstract. Industrial drying is an energy-intensive process in which manual monitoring of process parameters limits the continuous assessment of thermal performance. At the investigated industrial facility, process parameters were manually recorded from instrument panels, resulting in discrete datasets that lim-ited the assessment of continuous energy performance. This study developed a physics-based Internet of Things system for live thermal energy assessment in an industrial drying process. The proposed framework integrates data from two con-trol panels to continuously monitor the hot-water circulation system and oven op-erating conditions. The acquired data were automatically processed to estimate thermal energy supply and drying performance in real time. The oven temperature remained stable at 75–80 °C, while the inlet–outlet water temperature difference confirmed effective heat transfer to the drying chamber. The theoretical energy requirement was estimated from laboratory experiments using sensible and latent heat calculations and used as the minimum energy threshold for industrial pro-duction. This threshold was continuously compared with the actual thermal ener-gy supplied, which was calculated in real time from the monitored hot-water tem-perature and flow-rate data. The average actual and theoretical thermal energy re-quirements were 151,291 and 115,920 kcal h⁻¹, respectively. The difference re-sults in an energy difference of 35,371 kcal h⁻¹, which is equivalent to 23.4% of the supplied thermal energy. This energy difference represents the thermal energy (heat) loss between the heating system and the drying process. The proposed framework enables continuous assessment of thermal performance, facilitating the identification of energy losses and supporting data-driven energy management in industrial drying systems.
Keywords: Heat loss; industrial drying; internet of things; live monitoring; ther-mal energy assessment
Chair: Assoc. Prof. Dr.Nurul Izza Md Nor
Co-chair: Dr. Mersi Kurniati
Luma J. Hamoody1 [0009-0007-9803-4813], Zainab T. Abdulhamied1 [0009-0000-1179-6217], Munira M. J. Al-Haji1 [0009-0005-9532-7528] Israa A. Ismael1 [0009-0007-7543-8356], and Ashwaq A. Jabor1 [0009-0003-6894-5617]
1Scientific Research Commission, Baghdad, Iraq
luma.jasim.hamoody@src.edu.iq
Abstract
The potential toxicity and environmental persistence of inorganic corrosion inhibitors have led researchers to pursue eco-friendly alternatives provide effective corrosion protection while reducing risks to human life and the environment. Corrosion is an electrochemical phenomenon that effect metallic structures worldwide resulting in crucial economic losses and environmental concerns. Traditional inorganic corrosion inhibitors ,including chromates, phosphates, and other heteroatoms –containing compounds ,have demonstrated excellent corrosion protection; nevertheless their toxicity and adverse environmental impacts have raised serious concerns regarding their widespread use, consequently , increasing attention has been directed toward the development of sustainable ,non-toxic, alternatives .Among these ,green corrosion inhibitors (GCIs) have emerged as promising candidates owing to their biodegradability, low toxicity, renewability, and high inhibition performance. This review provides mainly a comprehensive overview of recent studies on the application of (GCIs) for mild steel. It presents a concise discussion of the types of corrosion affecting mild steel , factors influencing the corrosion process and conventional corrosion prevention methods , including organic corrosion inhibitors .Furthermore , the review examines the classification ,inhibition mechanisms and practical application of green (GCIs) as well as specimen(plant extract) preparation procedures commonly employed in corrosion studies ,in addition a simplified overview of the principle electrochemical techniques and surface analysis used for corrosion evaluation is provided to facilitate a fundamental understanding of their role in corrosion investigations . finally , the review summarizes the most recent researches published over the past two years in a comprehensive table ,highlighting the types of green corrosion inhibitors investigated , experimental conditions , inhibition efficiencies and key findings.
Keywords: green corrosion inhibitors ,mild steel ,plant extracts
Nanobots have attracted significant attention for precision medicine due to their potential to improve the precision and effectiveness of medical treatments. One of the major challenges in intravascular nanobot applications is planning a safe and efficient navigation path within the highly constrained environment of blood vessels. This paper presents a preliminary study on dynamic path planning for nanobot navigation in blood vessels. The proposed framework considers vessel geometry and obstacle avoidance to generate feasible navigation paths. A distance-transform-based path planning approach is employed as the initial navigation strategy and evaluated through simulations on simplified vascular models. The study investigates the ability of the planner to adapt to dynamic environments while maintaining an efficient route toward a designated target. The final navigation path is generated using a linear combination strategy incorporating a caution factor to improve navigation safety near obstacles. Performance is evaluated in terms of path length and navigation feasibility under various obstacle configurations. Preliminary simulation results demonstrate that the proposed framework can generate adaptive navigation paths, highlighting its potential for future nanomedical applications. This work serves as an initial step toward the development of intelligent navigation systems for intravascular nanobots. Future work will incorporate realistic hemodynamic models, vessel elasticity, and multi-objective optimization to further enhance performance and robustness of nanobots navigation in complex vascular environments.
Gold nanorods (AuNRs) exhibit tuneable localized surface plasmon reso-nance (LSPR), making them valuable for sensing and nanophotonic. This study investigates the impact of ascorbic acid (AA) concentration on the plasmonic properties of AuNRs synthesized using the seed-mediated growth technique. AA concentrations ranging from 140 to 370 µL were used to produce nanorods with aspect ratios of 4-7. The synthesized AuNRs were characterized using UV-Visible spectroscopy, Fourier transform infrared spectroscopy (FTIR), and Field Emission Scanning Electron Microscopy (FESEM) to evaluate their optical and morphological properties. OptiFDTD simulations were employed to model LSPR responses, analysing field in-tensities and spectral shifts. Experimental results demonstrated that in-creasing AA concentration reduced the aspect ratio, causing a blue shift in the longitudinal LSPR peak observed within the range of 680–840 nm. Ad-ditionally, the plasmonic signal increased by up to 5.47% that show the op-timised AA volume increases gold nanorod optical characteristics. Simulations confirmed these results by highlighting improved electric field intensities at resonance frequencies. This study provides insightful analysis of how to manipulate the plasmonic behaviour of AuNRs and further its uses in plasmonic sensing and nanotechnology.
Keywords: Gold nanorods, seed mediated growth, localized surface plas-mon resonance, Ascorbic acid, OptiFDTD
Rice grain exposed to excessive or insufficient moisture is susceptible to quality deterioration and microbial contamination, highlighting the need for a highly sensitive moisture sensing system to ensure food safety. This study presents the development of a graphene oxide (GO)-coated optical fiber sensor based on a hybrid Single-Mode Fiber–Multimode Fiber–Single-Mode Fiber (SMF–MMF–SMF) structure integrated with a Fiber Bragg Grating (FBG) for rice moisture level detection. Rice samples with varying moisture levels were prepared through controlled drying and steaming processes. To evaluate the influence of the hybrid fiber configuration, the MMF length was varied from 3.0 cm to 5.0 cm, while single-, double-, and triple-layer GO coatings were deposited on the spliced regions between the MMF and FBG. The sensor performance was investigated at operating wavelengths of 1310 nm and 1550 nm using an optical power meter and an optical spectrum analyzer (OSA), with the sensing probe directly immersed in the rice samples. Material and optical characterizations of the GO coating were conducted using X-ray diffraction (XRD), X-ray fluorescence (XRF), and UV–Vis spectroscopy. The results demonstrate that the sensor incorporating a 5 cm MMF section produced the highest output power of -65 dBm. Furthermore, the combination of a double-layer GO coating and a 1550 nm operating wavelength exhibited the optimum sensing performance, yielding a 9.11% higher output power than the 1310 nm wavelength when detecting rice samples with different moisture levels. These findings demonstrate the potential of the proposed GO-coated hybrid SMF–MMF–SMF–FBG sensor as a reliable and cost-effective approach for rapid rice moisture monitoring, contributing to improved food quality assurance and safety.
Metal-oxide-based nanostructured materials continue to receive considerable attention because of their chemical stability, surface reactivity, and potential use in environmental and biological applications. Nevertheless, many commonly employed synthesis methods require controlled processing condi-tions, expensive equipment, or surface-modifying agents that may restrict their use in resource-limited laboratories. In the present study, Zn-, Cu-, Fe-, and Mn-based oxide materials were prepared through a unified aqueous precipitation–calcination route using metal sulfate precursors and ammonium bicarbonate as the precipitating agent. Two additional samples were prepared as physical mixtures of the Zn- and Cu-based materials and of the Zn- and Fe-based materials. The precipitated intermediates were washed, dried at 90 °C, and calcined at 450 °C for 3 h. Fourier transform infrared spectroscopy revealed low-wavenumber absorption features consistent with metal–oxygen vibrations, together with broad hydroxyl-related bands attributed to surface-bound hydroxyl groups and adsorbed moisture. Scanning electron microscopy showed extensively agglomerated structures whose organization differed among the investigated metal systems. The Fe-based material exhibited particularly heterogeneous and flake-like aggregates, whereas the Zn-, Cu-, and Mn-based materials showed more compact or granular assemblies. Antimicrobial activity was evaluated by the agar well diffusion method against Staphylococcus aureus, Enterococcus faecalis, Escherichia coli, Acinetobacter baumannii, and Candida albicans. The Cu-based material produced the largest inhibition zone against A. baumannii at 16 mm, while the Cu-based material and the Zn/Cu physical mixture each produced a 14 mm zone against E. coli. The Fe- and Mn-based individual samples produced no detectable inhibition zones under the applied test conditions. The Zn/Fe mixture exhibited limited activity against selected microorganisms, indicating that physical mixing altered the observed response but did not produce broad-spectrum enhancement. The results therefore provide a preliminary comparison based on nominal metal composition, FTIR features, morphology, and agar diffusion behavior rather than confirmed phase-specific properties..
Sequential pulsed laser ablation in liquid (PLAL) controlled the interfacial architec-ture of WO₃/g-C₃N₄ nanohybrids through two pre-optimized reciprocal routes. R1 formed WO₃@g-C₃N₄ by ablating g-C₃N₄ at 20 J cm⁻² in a preformed WO₃ colloid, whereas R2 formed g-C₃N₄@WO₃ by ablating WO₃ at 70 J cm⁻² in a preformed g-C₃N₄ colloid. These target-specific second-step fluences were established through prior work and preliminary optimization. XRD confirmed retention of monoclinic WO₃ in both hybrids. Relative to pristine WO₃, R1 reflections shifted 0.29–0.31° toward higher 2θ, compared with 0.14–0.15° for R2, while both retained a mean coherent-domain length of approximately 47 nm. Electron microscopy revealed WO₃ cores surrounded by carbon-nitride-rich shells in R1 and discrete WO₃-rich domains distributed over g-C₃N₄ sheets in R2. Their zeta potentials were −46.3 and −41.7 mV, respectively. AFM mean lateral feature sizes were 74.1 and 82.5 nm, respectively. Direct-transition Tauc analysis resolved energies of 1.80 and 3.04 eV for R1 and 1.80 and 2.95 eV for R2. R2 exhibited lower transmittance, whereas R1 delivered stronger photocatalysis. Under xenon-lamp irradiation, R1 achieved 83% methylene-blue degradation after 180 min with kₐₚₚ = 1.00 × 10⁻² min⁻¹, exceeding R2 at 71% and 7.03 × 10⁻³ min⁻¹. R1’s superior performance results from its continuous core–shell interface, carbon-nitride-rich adsorption surface, rougher topography, and stronger electrostatic attraction toward cationic dye molecules. These findings estab-lish ablation order and target-specific fluence as coupled controls of nanohybrid morphology, optical response, and photocatalytic performance.
Nanomaterials are increasingly being used in areas including environmental remediation, biomedical engineering, and advanced manufacturing. However, this growth presents a continuous challenge: traditional in vitro assays used to evaluate nanoparticle toxicity are time-consuming and costly, and scale poorly as new nanomaterials continue to emerge. This study investigates whether machine learning approaches may provide a rapid and scalable alternative by predicting toxicity outcomes based on physicochemical properties, without relying on direct biological experiments.
Here, we use a publicly available nanoparticle dataset containing core size, hydrodynamic size, surface charge, surface area, exposure dose, and exposure time. Previous work on this dataset often stopped at reporting accuracy, with little discussion of why a model makes a given prediction or how such results would generalise beyond the research setting. In this work, four classification algorithms are compared: Random Forest, Support Vector Machine, K-Nearest Neighbors, and Extreme Gradient Boosting. SHAP (SHapley Additive exPlanations) is then applied to the best-performing model to identify which physicochemical properties most influence its predictions.
The aim is a screening tool that is not only accurate but interpretable, one that researchers could use in early-stage safety assessment without having to justify a black-box result.
Abstract. Nano-fertilizer performance can differ sharply across vegetative, reproductive and biochemical traits, particularly when crops are exposed to salinity. This exploratory field screening evaluated the Q21 genotype of quinoa (Chenopodium quinoa Willd.) at Jurf Al-Naddaf, Baghdad. Q21 was selected because a previous Iraqi multi-site evaluation reported the highest mean grain yield among four tested genotypes and comparatively early maturity, providing a locally relevant basis for its use under central Iraqi conditions. Six foliar formulations were compared: control, humic acid, Ti + Zn + humic acid, Ti + humic acid, Ti + Ag + Zn, and Zn + humic acid. Before sowing, the field soil had pH 7.0 and EC 5.5 dS m−1; irrigation-water EC was 5.0 dS m−1 at establishment and 4.5 dS m−1 in February. Ti + Zn + humic acid had the greatest recorded plant height (137.4 cm), and Ti + humic acid had the highest inflorescence number (33.8 plant−1) and seed number per inflorescence (1,435). The highest recorded treatment-level seed-yield value (1,000.0 g m−2) and SPAD mean occurred in the archived Ti + Ag + Zn formulation. Humic acid had the highest IAA, myo-inositol and biotin values, whereas Ti + Zn + humic acid had the highest GA value. Because independent plot replication was not recoverable for all endpoints, treatment comparisons are descriptive. The results define distinct nano-fertilizer response signatures in Q21 rather than a single uniformly superior formulation.
Chair: Dr. Ir. Asif Ali Zamzani
Co-chair: Assoc. Prof. Dr. Mohammad Nuzaihan Md Nor
Flexible capacitive pressure sensors are critical for wearable technologies, but traditional designs struggle with low sensitivity in low-pressure ranges, making the choice of electrode materials a crucial factor for achieving optimal electrical performance. This work presents a comparative study on the characterization of silver (Ag) and titanium/gold (Ti/Au) electrodes for the implementation of a zinc oxide (ZnO)-based flexible pressure sensor. The electrode–semiconductor interface plays a critical role in determining charge transport behavior, sensitivity, and stability of pressure sensors, particularly in low-pressure wearable applications. Fabricated on flexible Polyvinyl Chloride (PVC) substrates within a Metal-Insulator-Metal (MIM) architecture, the design utilizes ZnO interlayers to significantly amplify capacitance changes through inherent piezoelectric and polarization effects. Morphological evaluations confirmed that drop-casted Ag electrodes exhibit a granular structure that increases effective surface area, whereas sputtered Ti/Au electrodes present a highly uniform and smooth surface. Electrical characterization revealed that Ag electrodes exhibited linear current–voltage characteristics, indicating natural Ohmic contact behavior. Conversely, unoptimized Ti/Au electrodes demonstrated nonlinear responses associated with Schottky-type interfaces, impeding initial charge flow. A subsequent annealing treatment significantly reduced these interface barriers and improved the linearity and charge transport of the Ti/Au contacts. Ultimately, the findings highlight the profound influence of electrode material selection on the electrical performance and suitability of ZnO-based pressure sensors, guiding future developments in reliable wearable electronics.
Graphene-based textile electrodes have attracted considerable attention for wearable energy storage applications due to their lightweight structure, flexibility, and excellent electrical properties. However, conventional fabrication methods often suffer from poor coating uniformity, limited scalability, and inadequate durability. In addition, structural defects and graphene agglomeration can adversely affect the electrical conductivity and mechanical performance of the electrodes. This study investigates the effect of dip-dry coating cycles on the uniformity and structure of graphene oxide (GO) and reduced graphene oxide (rGO) coatings on cotton fabric electrodes. The 24 mm diameter circular cotton fabric substrates were coated using a simple and scalable dip-dry process with 2 and 10 coating cycles, employing GO and rGO aqueous dispersions. The influence of coating cycles on graphene deposition, coating uniformity, and structural characteristics was systematically evaluated. Morphological and structural characterization were performed using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and Raman spectroscopy. The results show that increasing the number of dip-dry cycles enhances graphene coverage on the cotton fibers and promotes the formation of a more continuous and inter-connected coating network. Furthermore, the structural differences between GO and rGO coatings were confirmed by Raman spectroscopy. These findings demonstrate that coating cycle number plays a critical role in determining the structural quality of graphene-coated textile electrodes, providing valuable in-sights for the development of flexible and wearable supercapacitor applications.
Keywords: Graphene oxide (GO), Reduced graphene oxide (rGO), Dip-dry coating, Cotton fabric electrode, Wearable supercapacitor.
The widespread presence of polystyrene nanoplastics (PS-NPs) in aquatic environments presents critical environmental and health challenges, driving the demand for highly sensitive detection platforms. This work presents a silver nanoparticle-functionalized silicon wire (AgNP-SiW) sensor for the label-free detection of PS-NPs in water samples. The sensor is fabricated via a top-down approach utilizing a silicon-on-insulator (SOI) substrate, standard photolithography, and plasma etching processes. The physical structure of the fabricated SiW and the successful electrodeposition of AgNPs were verified using Field Emission Scanning Electron Microscopy (FESEM), UV-Vis spectroscopy, and Energy-Dispersive X-ray (EDX) spectroscopy. The sensing mechanism relies on surface charge modulation and localized electrical property changes occurring when PS-NPs interact with the AgNP-functionalized SiW interface. Electrical characterization demonstrates that the device exhibits systematic, progressive shifts in its current-voltage transport curves across baseline, control, and varying concentrations of 100 nm PS-NPs. Each concentration yields an incremental change in the current response, allowing the sensor to effectively detect and differentiate varied concentration levels of nanoplastics in aqueous samples. These findings underline the sensor's potential as a high-performance tool for environmental monitoring and water quality assessment.
The efficiency of Dye-Sensitized Solar Cells (DSSC) as one of solar energy harvesting technology is highly dependent on the optical and electronic properties of the dye compounds used. Galangin as a natural flavonoid compound that is abundant in ginger plants has that potential, but its direct application is hindered by a wide energy gap and an absorption peak that is still in the ultraviolet region, even though its LUMO energy level is already above the conduction band TiO2. This research aims to optimize the properties of galangin computationally using the DFT method at the B3LYP/def2-SVP level of theory. Structure engineering is done through the variation of π-spacer (thiophene and furan) and acceptor group (4-ethynyl benzoic acid and cyanoacetic acid). Simulation results show that the combination of thiophene and cyanoacetic acid (GTS) provides the best optoelectronic performance. The efficient electron delocalization properties of thiophene, combined with the strong electron attraction of cyanoacetic acid, succeeded in narrowing the GTS energy gap significantly. Furthermore, the effect of the π-spacer chain length was explored by varying the number of thiophene units (Tn) of GTS. The extension of the thiophene-conjugated bridge significantly shifts the absorption spectrum toward longer wavelengths (redshift). Among all the evaluated groups, the GTS group exhibits the largest maximum absorption wavelength (max), effectively expanding the light absorption capacity in the visible spectrum. The T4 molecule produces the highest total dipole moment of 6.70 D, indicating the most effective charge separation between the donor and acceptor. The synergy between the maximum max shift, high oscillator strength, and superior dipole moment enhances the intramolecular charge transfer, enhances interfacial charge injection, and suppresses electron recombination at the TiO2/sensitizer interface. Therefore, the T4 molecule is recommended as the most optimal and high-potential sensitizer candidate for DSSC applications.
Nanomedicine is currently a rapidly growing interdisciplinary field that integrates nanotechnology with medical science, including drug delivery systems, diagnostics, and nanoparticle-based therapies. In addition, research trends in the field of nanomedicine are increasingly difficult and time-consuming if done manually. This study proposes a machine learning approach to classify nanomedicine research trends automatically. In the process, the research was conducted using a dataset of article abstracts collected from the PubMed database through the NCBI E-utilities API, with search keywords that include nanomedicine, drug delivery nanoparticle, and related terms. The collected abstract data were processed through standard natural language processing stages, including text cleaning, tokenization, stopword removal, and vectorization. Then, classification was carried out using machine learning including Support Vector Machine, Radom Forest, and Logistic Regression, trained to group abstracts into nanomedicine subcategories (e.g., cancer drug delivery, nanotoxicology, nanodiagnostics, and regenerative nanotherapy). Model performance was evaluated using metrics of accuracy, precision, recall, and F1-score, with comparisons between algorithms to determine the best classification model. The results of this study are expected to provide a scalable and efficient framework for automatically mapping nanomedicine literature, thereby assisting researchers, healthcare practitioners, and policymakers in identifying research development directions and innovation opportunities in the field of nanomedicine. This research is expected to contribute to the multidisciplinary field of Natural Language Processing (NLP) and biomedicine.
Abstract. Graphene-based metal-organic frameworks (MOFs) composites and their defective counterparts were synthesised by combining electrochemically exfoliated graphene with solvothermal synthesis. To generate missing linker defects within the framework, formic acid was used as a modulator. The suc-cessful integration of G10v within the synthesised MOFs was characterised by textural analysis and spectroscopic techniques. The pristine MOFs, G10v, and their composite counterparts, were evaluated for their adsorption properties toward widely detected pharmaceuticals in wastewater, such as carbamazepine (CBZ) and sulfamethoxazole (SMX). The adsorption capacity and removal ef-ficiency of these materials were investigated using screening experiments. The De-UiO-66/G10v and De-UiO-66NH2/G10v exhibited the highest removal ef-ficiencies for SMX, reaching 74% and 72%, respectively. For CBZ, both De-UiO-66/G10v and De-UiO-66NH2/G10v achieved a removal efficiency of 58%. These values represent an enhanced removal efficiency compared to their non-defective (UiO-66/G10v and UiO-66NH2/G10v) counterparts and pristine materials (UiO-66, UiO-66NH2, and G10v). Defect formation within the MOF framework increased the material surface area and created additional active sites, promoting stronger adsorption interactions with CBZ and SMX mole-cules. NMR relaxation experiments were conducted using water as a probe molecule to investigate the strength of surface interaction in MOF/G10v com-posites. The findings show that De-UiO-66/G10v and De-UiO-66NH2/G10v exhibited a higher T1/T2 ratio compared to UiO-66/G10v and UiO-66NH2/G10v and their pristine counterparts (UiO-66, UiO-66NH2, and G10v), indicating stronger surface interactions with water molecules, highlighting the benefits of defects in MOFs/graphene composites in improving adsorption ca-pacity for removing pharmaceutical contaminants from water.
Abstract. Analytical approaches to food contamination need to be rapid, sensitive, minimally destructive, and capable of detecting molecular adsorption at interfaces. Nonlinear optical spectroscopy, especially second-harmonic generation (SHG), third-harmonic generation (THG), and rotational-anisotropy measurements, is very sensitive to surface and is symmetry selective. This extended abstract summarizes the progress in the application of the bond model and the simplified bond hyperpolarizability model (SBHM) to the contaminant sensing. The nonlinear response is described as a coherent sum of radiation from an-harmonically driven chemical bonds. Measured angular patterns are related to molecular orientation, surface coverage, adsorption-induced electronic redistribution and substrate symmetry. The effects of nanoscale organic contaminants and malachite green are investigated revealing concentration dependent nonlinear signals which can be interpreted in terms of bond level parameters. Further improved nonlinear conversion and selective adsorption are supported by advances in semiconductors, perovskite and two-dimensional substrates. This leads to a promising route to compact, label-free and quantitative food-safety platforms by combining physically interpretable bond modelling with machine learning.
Keywords: food contamination, nonlinear optics, simplified bond hyperpolarizability model, second-harmonic generation, optical sensing, machine learning.
Introduction
Confirmatory food analysis still relies on chromatographic, mass-spectrometric, immunological and electrochemical methods, but many of these workflows involve extensive sample preparation, laboratory infrastructure and trained operators. Nonlinear optical methods offer a complementary approach as the harmonic generation is intrinsically sensitive to symmetry-breaking, interfacial bonding, molecular orientation and local electric fields. SBHM expresses the macroscopic nonlinear polarization as a coherent superposition of bond contributions rather than treating the susceptibility tensor as a purely phenomenological object. The original formulation reproduced SHG from silicon interfaces using bond directions and effective hyperpolarizabilities [1], and later work demonstrated its consistency with group theory and Neumann’s principle [2].
Bond-Model Basis for Contaminant Detection
In the bond description, the local electric field in each bond transports the charge along the bond. Anharmonic restoring forces generate radiation at multiples of the fundamental frequency. The far field SHG or THG signal is obtained by summing bond dipoles with phase, orientation, Fresnel transmission and polarization factors . Extensions to electric-field induced SHG and THG show that a single microscopic formulation can describe several nonlinear channels [3]. We apply to zincblende and wurtzite structures and show that a small number of physically meaningful parameters can reproduce rotational-anisotropy patterns [4,5]. For sensing the adsorbed molecules perturb the interface by new bonds, charge redistribution, local symmetry breaking and changes in effective hyperpolarizability. Thus the angular pattern is a molecular finger print and the fitted bond parameters give a mechanistic insight into contaminant-surface interactions.
Fig. 1. Reflection-mode SHG from a Rhodamine B monolayer adsorbed on a Si(001) substrate. The incident fundamental beam kω produces reflected second-harmonic radiation k2ω; θi and θo denote the incidence and detection angles.
Recent Advances and Food-Safety Translation
A direct proof of concept was reported for malachite green deposited on silicon, distinguishing concentration-dependent responses via bond-model analysis and nonlinear optical measurements [6]. Later the approach was generalized to the case of nanoscale organic contaminants and correlated the molecular deposition with changes in the harmonic intensity and anisotropy [7]. The studies further extend SBHM from a structural model of crystalline interfaces to a sensing framework for complex adsorbates. Selectivity can be used not only with total intensity, but also by using polarization combinations, rotational harmonics, fitted bond angles and effective hyperpolarizabilities. ZnO is an attractive material due to its non-centrosymmetric wurtzite structure, surface activity and strong nonlinear response [5, 8]. Hybrid perovskites offer large electronic polarizability and phase-dependent harmonic generation [9,10], while two-dimensional materials exhibit adsorption-driven changes in hyperpolarizability and mechanisms of charge transfer [11]. In centrosymmetric media, we must also consider the spatial-dispersion and quadrupolar contributions if we want to not attribute all the SHG to the interface [12].
Outlook and Conclusion
Practical systems should integrate SBHM with machine learning without compromising physical interpretability. A suitable workflow in-cludes; polarization resolved or rotational-anisotropy SHG/THG, baseline correction, extraction of Fourier components, peak positions, inten-sity ratios and fitted bond parameters. Outputs of bond models constrain feature space and remove nonphysical correlations. Supervised models can then classify contaminants or estimate concentration. Calibration for real foods must account for scattering, fluorescence, hetero-geneous adsorption, moisture, lipids, proteins, and competing residues. Surface functionalized silicon, ZnO, perovskite or two dimensional platforms could selectively capture phenolic compounds, dyes, pesticides, antibiotics or spoilage metabolites. SBHM-based harmonic sensing can be a fast, label-free complement to traditional food contamination analysis with matrix-aware validation and explainable machine learning.
References
[1] G. D. Powell, J.-F. Wang, and D. E. Aspnes, “Simplified bond-hyperpolarizability model of second harmonic generation,” Phys. Rev. B 65, 205320 (2002). https://doi.org/10.1103/PhysRevB.65.205320
[2] A. Alejo-Molina, H. Hardhienata, and K. Hingerl, “Simplified bond-hyperpolarizability model of second harmonic generation, group theory, and Neumann’s principle,” J. Opt. Soc. Am. B 31, 526–533 (2014). https://doi.org/10.1364/JOSAB.31.000526
[3] A. Alejo-Molina, K. Hingerl, and H. Hardhienata, “Model of third harmonic generation and electric-field-induced optical second harmonic using SBHM,” J. Opt. Soc. Am. B 32, 562–570 (2015). https://doi.org/10.1364/JOSAB.32.000562
[4] H. Hardhienata et al., “Bulk dipolar contribution to second-harmonic generation in zincblende,” J. Opt. Soc. Am. B 33, 195–201 (2016). https://doi.org/10.1364/JOSAB.33.000195
[5] H. Hardhienata et al., “Bond model of second-harmonic generation in wurtzite ZnO(0002) structures with twin boundaries,” J. Opt. Soc. Am. B 36, 1127–1137 (2019). https://doi.org/10.1364/JOSAB.36.001127
[6] M. Ahyad et al., “A novel sensing method to detect malachite green contaminant on silicon substrate using nonlinear optics,” Micromachines 15, 1227 (2024). https://doi.org/10.3390/mi15101227
[7] H. Hardhienata et al., “Nanoscale organic contaminant detection at the surface using nonlinear bond model,” Surfaces 8, 11 (2025). https://doi.org/10.3390/surfaces8010011
[8] H. Hardhienata et al., “Third harmonic generation in ZnO semiconductor using the simplified bond hyperpolarizability model,” J. Nonlinear Opt. Phys. Mater. 27, 1850025 (2018). https://doi.org/10.1142/S021886351850025X
[9] H. Hardhienata et al., “Bond model of second harmonic generation in tetragonal and orthorhombic perovskite structures,” J. Opt. Soc. Am. B 40, 2773–2781 (2023). https://doi.org/10.1364/JOSAB.499753
[10] H. Hardhienata et al., “Third-harmonic generation in tetragonal methylammonium lead iodide perovskite,” J. Opt. Soc. Am. B 42, 2103–2115 (2025). https://doi.org/10.1364/JOSAB.566006
[11] T.-Y. Yen et al., “Gas adsorption mechanism on 2D materials: Hyperpolarizability evolution analyzed by nonlinear optics,” Adv. Funct. Mater. 34, 2406005 (2024). https://doi.org/10.1002/adfm.202406005
[12] H. Hardhienata et al., “Spatial dispersion contribution to second harmonic generation in inversion-symmetric materials,” Phys. Rev. B 103, 125410 (2021). https://doi.org/10.1103/PhysRevB.103.125410
Chair: Dr. Suhailah Hayaza
Co-chair: Dr. Norizah Abd Karim
Natural hydroxyapatite (HAp) derived from bovine bone waste is a promising sustainable dielectric material for humidity sensing , but its sensing performance can be tuned through simple post-synthesis processing such as mechanical milling. This study examines the influence of mechanical milling duration on its structure and humidity-sensing performance. HAp was extracted by deproteinization and calcination at 900 °C, then mechanically milled for 0, 1, or 2 h. X-ray diffraction confirmed a hexagonal HAp phase with lattice parameters in >99% agreement with the ICDD reference, a crystallite size of ~49 nm, and crystallinity of 78.1%, while scanning electron microscopy showed submicron agglomerates with morphology preserved across all milling conditions. Despite this structural similarity at the bulk level, milling was expected to alter surface area and particle packing within the sensing film, factors that directly govern moisture adsorption and charge transport. To test this, HAp was blended with 10 wt% PVA, drop-cast onto interdigital copper electrodes, and characterized by impedance and capacitance measurements from 0.1 to 1000 kHz under humidity conditions generated by MgCl₂ and MgSO₄ salt exposure, spanning approximately 35–79% RH. All samples showed decreasing impedance and increasing capacitance with rising humidity, with the strongest response at 0.1 kHz. The 1-h milled sample exhibited the highest capacitive sensitivity (9.4%) with a linear response, while further milling to 2 h reduced the response, indicating that an intermediate milling duration provides the optimal sensing performance. These findings support waste-derived natural HAp as a viable platform for low-cost humidity sensor applications.
Ischemic stroke is a leading cause of neurological disability, and early detection of its lesions on non-contrast CT (NCCT) images remains challenging due to their subtle appearance. This study aims to analyze the localization of ischemic stroke lesions on NCCT images and evaluate its performance using the integration of a Residual Network and a Convolutional Block Attention Module (ResNet-CBAM). The AISD dataset comprising 2,380 slices from 388 patients was used with a patient-level split. Preprocessing included HU windowing (level 35, width 35), resizing to 224×224, ImageNet normalization, and ±5% translation augmentation. The ResNet50 architecture was modified with AdaptiveAvgPool2d(2) and a regression head for bounding box regression, with three variants: Baseline, CBAM Layer4, and CBAM All Block. Models were trained using Smooth L1 Loss, the Adam optimizer, and early stopping. Evaluation used Mean IoU, Center Localization Error (CLE), Generalized IoU (GIoU), and a paired t-test. Results on 336 test slices showed that CBAM All Block achieved the highest Mean IoU (0.1426) with 14.58% of predictions exceeding IoU > 0.5, followed by CBAM Layer4 (0.1339) and Baseline (0.1265). Under a single paired test, the difference between CBAM All Block and Baseline showed p = 0.0211 (p < 0.05), but after Bonferroni correction, this difference was no longer significant. The lowest CLE was attained by CBAM Layer4 (29.50 pixels), while GIoU values across the three models were practically equivalent (−0.2625 to −0.2727). Thus, no single model consistently outperformed across all evaluation metrics. The low absolute performance reflects the difficulty of localizing subtle early ischemic lesions on NCCT, particularly for small lesions.
Keywords: ischemic stroke; NCCT; localization; ResNet50; CBAM; bounding box; deep learning.
In human body tissue, post-operative adhesion spontaneously develops as a result of wound healing. Adhesion can impair organ and tissue function and, in certain situations, endanger the patient's life. One in three individuals who have surgery on their abdomen may require continuation care due to adhesion. Physical barriers can hinder adhesion and have a major impact, according to several studies on chemical compounds. In order to prevent digestive post-operative adhesion, this study attempts to synthesize and describe the alginate-chitosan membrane as a potential physical barrier. membrane produced by mixing solutions of alginate and chitosan at various concentrations until the mixture is homogenous, after which it is dried and submerged in CaCl2 solution. SEM, FTIR, swelling, degradation, MTT Assay, and water contact tests are then used to evaluate the final membrane. According to the characterization, the variation with the best alginate-chitosan membrane properties is the 1.5% concentration, which has 73.36% cell viability, 90.75% swelling rate, and 96.71% disintegration rate on the tenth day. Alginate-chitosan membranes have the ability to act as a physical barrier to stop digestive post-operation adhesion, according to this research.
Key Words : alginate, chitosan, membrane, physical barrier, anti adhesion
Cu2ZnSnS4 (CZTS) is a promising thin-film solar cell absorber, but its low power conversion efficiency (PCE) remains a major challenge. In this study, Cu2(Zn(1-x)/Mn(x))SnS4 with X = 0, 0.25, 0.75, and 1, was synthesized via a solvothermal method to investigate the effects of Mn incorporation on its structural, optical, magnetic, and photovoltaic properties. XRD analysis confirmed successful Mn incorporation into the CZTS lattice, accompanied by lattice distortion and the formation of secondary phases at high Mn concentrations. SEM revealed reduced particle size at low Mn concentration and particle agglomeration at higher concentrations. Mn doping modified the optical properties by reducing the band gap and enhanced the magnetic response, with ferromagnetic behavior observed at x = 1. Photovoltaic measurements showed that Mn-doped CZTS produced higher voltage output than the undoped sample, while an external magnetic field further improved device performance by suppressing carrier recombination. However, excessive Mn incorporation promoted secondary-phase formation, resulting in reduced photovoltaic performance. These results demonstrate that controlled Mn doping is an effective approach to improve the multifunctional properties of CZTS, with low Mn concentrations providing the most favorable photovoltaic performance.
Screen-printed electrodes (SPEs) are widely used as biosensing platforms due to their simple and cost-effective fabrication. They are commonly produced on virgin PET or ceramic substrates; however, with increasing concern over plastic waste, alternative sustainable materials are needed. This study investigates the use of recycled polypropylene (R-PP) sourced from discarded mineral water bottles as a substrate for SPE fabrication, offering a low-cost and environmentally responsible approach. Collected PP bottles were cleaned, cut, extruded, and hot-pressed into uniform sheets suitable for electrode printing. SPEs were then fabricated on R-PP, PE, PVC, and PET substrates using screen-printing technique with graphite ink as the working and counter electrodes while Ag/AgCl ink for the reference electrodes. Surface characterization performed using Scanning Electron Microscopy (SEM) and 3D profilometry revealed the morphological and topographical features of each substrate, including roughness, texture, and uniformity which are critical parameters influencing the ink adhesion and electrode stability during the application test. Electrochemical performance was evaluated using current–voltage (I–V) measurements across five concentrations of ferricyanide/ferrocyanide solutions. The results show that substrate materials with polar molecule structures like PET and PVC will perform better as SPE biosensor due to stronger bonding for the adhesion of conductive carbon inks to the substrate surfaces. Even though the performance of SPE made on the R-PP substrate was not really good in the sensitivity test at 5 different concentrations of ferricyanide/ferrocyanide solution, its capability in showing a rising trend in detecting the target when the concentrations were increased is still acceptable. A few solutions will be suggested at the end of this study to improve the performance of R-PP as the potential SPE substrate
In this paper we report the computational approach on the adsorption of some drug waste contaminants by a graphene oxide nanosheet. Some popular drug molecules such as metformin (for diabetic), ofloxacin (antibiotics), paracetamol and diclofenac (analgesic) were evaluated in this computational approach. The results show that ofloxacin, diclofenac, paracetamol, and metformin bind spontaneously to graphene oxide with binding affinity of -9.4 kcal/mol, -7.4 kcal/mol, -6.6 kcal/mol, and -4.0 kcal/mol, respectively. Detail of molecular interactions and type of interactions were also discussed in this paper. We further developed a predictive statistical model using machine learning to predict the binding affinity based on the data of molecular weight and other chemical parameters.
Chair: Assoc. Prof. Dr. Ramzan Mat Ayub
Chair: Assoc. Prof. Dr. Ramzan Mat Ayub
MC: Dr. Adilah Ayoib