Speaker
Description
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.
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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.
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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
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