Triethylamine (TEA) is a common industrial contaminant and an important indicator of seafood spoilage. Therefore, determination of rapid and selective TEA is of great significance. This review article critically compares tungsten oxide (WO3)- and molybdenum oxide (MoO3)-based chemiresistive sensors by relating their crystal structure, surface chemistry, defect states, morphology, and interfacial electronic properties to TEA-sensing performance. Pristine WO3- and MoO3-based sensors generally operate at approximately 133–325 °C and provide sub-ppm detection, whereas doping, noble-metal sensitization, heterojunction formation, and light activation can reduce the operating temperature to 100–180 °C and extend detection into the low-ppb range. WO3-based sensors have exhibited a response of 1100 to 20 ppm TEA at 160 °C, with an estimated detection limit of 5 ppb, whereas modified MoO3-based sensors have also achieved decent detection limit of 1.7 ppb. WO3 is particularly responsive to phase, facet, work-function, and catalytic-interface engineering, whereas α-MoO3 benefits from its anisotropic structure, variable Mo valence, and favorable Lewis acid–base interactions with amines. Noble metals enhance gas sensing through catalytic and electronic sensitization, dopants regulate adsorption and defect chemistry, and n-n or p-n heterojunctions amplify resistance changes through depletion-layer modulation. Despite considerable advances in sensitivity, humidity interference, high power consumption, slow recovery, baseline drift, and limited long term stability remain unresolved. Future advances may require standardized performance assessment, operando mechanistic studies, humidity-resistant low-power devices, and validation under realistic seafood-storage and industrial conditions.
Tapered optical fiber-based surface-enhanced Raman scattering (SERS) probes have emerged as promising miniaturized platforms for chemical and molecular sensing by integrating optical excitation, plasmonic enhancement, and Raman signal collection within a single fiber architecture. Their enhanced light–matter interaction, compact geometry, and remote interrogation capability make them particularly attractive for in situ sensing in confined and complex environments. This review systematically examines recent advances in tapered optical fiber SERS probes, covering enhancement mechanisms, taper fabrication, plasmonic hotspot engineering, and analytical applications. Particular emphasis is placed on how taper geometry and plasmonic nanostructure organization jointly influence sensing performance. Fabrication and hotspot-engineering strategies are critically compared in terms of sensitivity, reproducibility, stability, fabrication complexity, and scalability. Representative applications in biomedical analysis, food safety, and environmental monitoring are further evaluated. Despite these advances, practical implementation remains constrained by insufficient hotspot reproducibility, quantitative reliability in complex matrices, long-term stability and antifouling performance, as well as the limited scalability of current fabrication protocols. Future progress will require balancing analytical sensitivity with reproducibility, robustness, and real-sample compatibility, while advancing deterministic hotspot engineering, selective recognition interfaces, standardized performance evaluation, intelligent spectral analysis, and Lab-on-Fiber integration. Together, these developments could accelerate the transition of tapered optical fiber SERS from laboratory-scale demonstrations to field-deployable platforms for remote and in situ molecular sensing.
This study evaluates calibration strategies for predicting soluble solids content (SSC) in Ambrosia apples using a low-cost, portable short-wave near-infrared (SW-NIR) spectrometer (640–1050 nm) under interactance acquisitions. Replicate sampling spectral curves exhibited notable variability due to asymmetric illumination, peel color heterogeneity, and probe contact inconsistencies. Regression models were comparatively built on averaged spectra compared with those trained directly on multiply sampled replicate spectra, applying piecewise Savitzky–Golay smoothing and detrending as pretreatment. Variable selection was performed via uninformative variable elimination (UVE) and backward interval partial least squares (BiPLS). Models calibrated on replicate spectra demonstrated superior generalization to unseen replicate measurements, despite slightly higher cross-validation errors. The BiPLS model on replicate spectra achieved the best predictive performance (mean RMSEP = 0.677 °Brix, Rp = 0.796, RPD = 1.656), with improved trueness (lower relative absolute bias) and precision (lower relative standard deviation). For comparison, the BiPLS model on averaged spectra yielded a mean RMSEP = 0.899 °Brix, Rp = 0.593, RPD = 1.25; the replicate-spectra strategy thus reduced the RMSEP by 24.7% and increased Rp and RPD accordingly. This suggests that for low-cost NIR instruments, using replicate sampling spectral modeling combined with interval variable selection can provide better prediction performance and achieve the purpose of on-site sorting in food quality analysis.
During the experiments presented in this work, the electrochemical synthesis of ferrate ions was performed from high-purity iron electrode in a 45% (m/m) aqueous NaOH solution at different temperatures. The synthesis process was investigated using dual dynamic voltammetry (DDV), which involves applying independent potential–time waveforms (dynamic potential programs) simultaneously to the disk and ring electrodes of a rotating ring–disk electrode (RRDE, Pt-ring—Fe-disk) setup. This innovative technique facilitates the instantaneous measurement of the concentration of ferrate ions generated at the disk electrode. The effect of temperature on ferrate ion formation was examined, and the optimal potential range and applied current density at various temperatures were determined to maximize ferrate ion production and current efficiency. The results indicate that the rate of both ferrate ion production and oxygen evolution increases with temperature within the investigated temperature range (15–45 °C). It was found that there is an optimal potential range at each temperature where ferrate ion formation occurs at the highest rate (limited by other factors). The maximum current efficiency was determined at each temperature, with the highest value obtained at approximately 35 °C.
High-voltage wiring harnesses in new energy vehicles (NEVs) are susceptible to thermal aging in high-temperature environments, whereas conventional assessment methods are difficult to deploy rapidly. This study combines laser-induced breakdown spectroscopy (LIBS) with random forest (RF) classification to assess thermal aging levels in cross-linked polyethylene (XLPE) insulation. Thirteen laboratory-aged XLPE wiring harness samples were prepared, and 100 single-shot spectra were acquired at fresh positions for each aging level. The specific methodological contribution is the use of class-wise median absolute deviation (MAD) at each wavelength as a variable-selection criterion before RF training. Three models were compared: RF, principal component analysis (PCA) combined with RF (PCA–RF), and MAD combined with RF (MAD–RF). RF achieved 100% internal hold-out accuracy for the non-aged versus 60-day comparison and 84.23% across all 13 aging levels. PCA–RF increased the multiclass accuracy to 87.31%, whereas MAD–RF reached 95.00% under the original exploratory hold-out workflow. These results indicate that wavelength-wise robust dispersion can provide a compact, discriminative representation of the present LIBS dataset.
The nondestructive detection of sodium chloride in mural plaster layers is important for assessing salt-related deterioration in cultural heritage materials. However, the weak and indirect spectral response of sodium chloride makes accurate hyperspectral detection challenging. This study developed a hyperspectral regression framework centered on Sparrow Search Algorithm (SSA) optimization, in which continuous wavelet transform (CWT) was used to construct multiscale spectral representations and Pearson correlation analysis combined with the Successive Projections Algorithm (PCC-SPA) was used for compact variable selection. Partial least squares regression (PLSR), support vector regression (SVR), extreme gradient boosting (XGBoost), SSA-optimized SVR, and SSA-optimized XGBoost were evaluated under nested stratified specimen-grouped five-fold cross-validation. Feature selection and hyperparameter optimization were independently performed within each outer training fold, whereas the held-out specimens were reserved for performance evaluation. SVR-SSA maintained high predictive capability across both conventional and multiscale spectral representations. SG + SNV yielded an R2 of 0.8167 ± 0.0690 and an RMSE of 0.3701 ± 0.0636 percentage points. Scale 6 CWT achieved closely comparable R2 and RMSE values of 0.8100 ± 0.0812 and 0.3731 ± 0.0767 percentage points, respectively, together with a lower MAE of 0.2788 ± 0.0620 percentage points. Among the ten CWT scales, Scale 6 achieved the highest mean prediction accuracy, whereas Scale 2 provided the best comprehensive balance between predictive accuracy and fold-to-fold stability. These results demonstrate that the effectiveness of SSA optimization depends on the input feature representation and that CWT provides scale-resolved information beyond a single conventional spectral representation. The proposed framework provides methodological support for the nondestructive quantitative assessment of NaCl-related deterioration in mural plaster materials and establishes a basis for further application in mural conservation.
The detection of organic carbon in wastewater is essential for process monitoring and regulatory assessment. Yet conventional chemical oxygen demand (COD) and total organic carbon (TOC) methods remain reagent-dependent, slow, and unsuitable for inline operation. Photoelectrochemical (PEC) sensing based on TiO2 offers a reagent-free alternative, but its response to wastewater-relevant dissolved organic matter (DOM) and real effluent matrices is still poorly understood. In this study, a TiO2-based PEC system was systematically evaluated using four representative model compounds—glucose, potassium hydrogen phthalate, L-tryptophan, and urea—covering major fractions typically present in municipal wastewater. For the first time, representative wastewater-associated organic compound classes, conductivity effects, and the transferability of the PEC response to real wastewater effluent were systematically investigated. The photocurrent response showed distinct, highly linear concentration–signal relationships for each substance, suggesting a dominant contribution of surface-associated electronic effects. Conductivity variations across a relevant range had no measurable influence on sensitivity or photocurrent magnitude, indicating that the PEC response is not governed by bulk ionic transport but primarily is an interfacial process at the site of TiO2. When applied to real wastewater effluent, the sensor exhibited an excellent linear correlation with dilution level (R2 = 0.9954), demonstrating a linear response within a defined matrix and an LOD of 1.12 mg L−1 COD. For the investigated model compounds, LOD values ranged from 1.06 to 3.00 mg L−1 COD, while a linear response was maintained up to approximately 80–100 mg L−1 COD. These findings establish TiO2-based PEC sensing as a promising platform for the reagent-free, online monitoring of organic loads in wastewater treatment.
Tea quality is fundamentally determined by the complex biochemical transformations occurring during manufacturing. Traditional sensory evaluation, while essential, suffers from inherent subjectivity and cannot meet the demands of modern, industrial-scale production monitoring. This review critically examines the application of electronic nose (E-nose) technology throughout the tea-processing pipeline, covering multiple transducer technologies including metal oxide semiconductor (MOS) sensors, quartz crystal microbalance (QCM) sensors, conducting polymer (CP) sensors, and emerging chemiresistive platforms. Particular emphasis is placed on the E-nose’s capacity for real-time, non-destructive monitoring of key processing stages, including withering, rolling, fermentation, and drying. We analyze how optimized sensor arrays capture dynamic volatile organic compound (VOC) evolution, enabling the precise identification of optimal processing endpoints, especially in black tea fermentation control. Furthermore, this review evaluates how advanced pattern recognition algorithms (such as deep learning models) and multi-sensor data fusion strategies enhance the robustness and accuracy of process monitoring. By correlating E-nose response patterns with critical biochemical markers and traditional taster metrics, this paper demonstrates the technology’s pivotal role in transitioning tea manufacturing from experience-based craftsmanship to data-driven automation, ultimately ensuring superior product consistency and efficiency.
Curdlan is an industrially important β-1,3-glucan with applications in the food, pharmaceutical, and biomaterial industries. However, the identification of high-yielding curdlan-producing strains is hindered by the absence of rapid and efficient screening methods. To address this limitation, we developed a systematically optimized integrated microscale workflow combining 48-well plate fermentation with a quantitative aniline blue-based colorimetric assay in 96-well plates for high-throughput screening of curdlan-producing strains. Fermentation was miniaturized using 48-well plates, while curdlan quantification was performed in 96-well plates through formation of a curdlan–aniline blue complex. Key parameters were systematically optimized. Under optimal conditions, curdlan dissolved in 1.0 mol/L NaOH was reacted with 2.0 mg/mL aniline blue in 0.5 mol/L phosphate buffer (pH 7.0) for 90 min, and absorbance was measured at 550 nm. The assay demonstrated excellent linearity between curdlan concentration and absorbance (y = 1.0008x + 0.1536, R2 = 0.9957, p < 0.001). To validate the method, curdlan yields from nine mutants derived from ATCC31749 were determined using both gravimetric and colorimetric approaches, revealing a strong correlation (R2 = 0.8683, p < 0.001), that confirmed the assay’s reliability for rapid screening. Application of this platform to 132 UV-mutagenized strains identified nine mutants with enhanced curdlan production. The best-performing strain, UV150824-02, produced 46.8 ± 0.08 g/L curdlan, an 11.4% increase over the wild-type strain ATCC31749 (41.6 ± 1.54 g/L), and maintained stable curdlan production over nine laboratory passages (coefficient of variation = 2.72%). This method significantly improves the efficiency of mutagenesis-based strain screening and provides a practical and efficient tool for accelerating strain improvement in industrial polysaccharide fermentation.
In this study, an electrochemical immunosensor based on polyaniline/nano-Fe3O4 (PANI/Nano-Fe3O4) nanocomposite (PANI/Nano-Fe3O4/Anti-FLA/BSA/GCE) was developed for the highly sensitive and selective detection of trace levels of fluoranthene (FLA) in marine environments. Fluoranthene antibodies (Anti-FLA) were covalently immobilized on a glassy carbon electrode (GCE) modified with PANI/Nano-Fe3O4 via an EDC/NHS activation strategy, enabling specific recognition of FLA based on the antigen–antibody binding mechanism. The performance of the sensor was systematically optimized using cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), linear sweep voltammetry (LSV), and differential pulse voltammetry (DPV). The results demonstrated a linear inverse relationship between peak current (Ip) and FLA concentration in the range of 0.5~80 ng/mL, with a regression equation of I = −1.55C + 174.602 (R2 = 0.996). The limit of detection (LOD) was as low as 0.354 ng/mL (S/N = 3). In real seawater sample analysis, spiked recovery tests at three representative sites in the Maowei Sea, Guangxi, yielded recoveries of 95.44%~97.51%, with RSDs below 3%, confirming the sensor’s resistance to matrix interference. The synergistic effect of the porous conductive network of PANI and the high specific surface area of Nano-Fe3O4 significantly amplified the electrochemical signal, while the molecular specificity of the antibody ensured targeted recognition. This sensor provides a novel and effective approach for the on-site rapid detection of polycyclic aromatic hydrocarbon (PAH) pollutants in complex marine environments, offering both high sensitivity and selectivity.
Hormones regulate numerous physiological processes and are essential for maintaining metabolic homeostasis. Accurate hormone quantification is crucial for the diagnosis and monitoring of endocrine and metabolic disorders. Electrochemical biosensors have recently emerged as promising platforms for hormone detection, offering simplicity, rapid response, cost-effectiveness, and high sensitivity compared to conventional techniques such as chromatography and mass spectrometry. This review summarizes the advances in electrochemical biosensors for detecting clinically relevant hormones, including cortisol, estrogen, progesterone, thyroid-stimulating hormone, parathyroid hormone, prolactin, and insulin, since 2010. Particular attention has been paid to developments in electrode modification strategies, including nanomaterials, redox enzymes, and novel recognition elements, which significantly improve the sensitivity and selectivity. These advances enable hormone detection at lower concentrations in various biological and environmental matrices. Despite these promising developments, challenges related to sensor stability, fabrication costs, and regeneration procedures limit their large-scale commercialization. Future research should focus on improving robustness, optimizing immobilization strategies, and integrating innovative materials to enhance the analytical performance. Continued collaboration among researchers, engineers, and healthcare professionals is essential. With ongoing technological progress, electrochemical biosensors are expected to play an important role in clinical diagnosis, point-of-care testing, and personalized medicine.
Triethylamine (TEA), a widely used volatile organic compound (VOC), poses severe threats to environmental safety and human health upon accidental leakage, making the development of high-performance TEA detection techniques urgently needed. Herein, we report a Sn-based metal-organic framework (Sn-MOF) constructed from 4,5-dichloroimidazole ligands synthesized via a solvothermal approach. The resulting MOF-derived SnO2 materials were obtained by calcination at 400-600 degrees C, yielding SnO2 with tunable specific surface area and surface defect-site density. Structural and surface characterizations revealed that the materials consist of primary nanoparticles in the range of 10-50 nm, forming aggregated particles of 1-2 & micro;m. The gas sensing performance toward TEA was systematically evaluated. The SnO2-400 degrees C sensor exhibited the highest response (S = 85.0) to 100 ppm TEA at 190 degrees C, with a low detection limit of 1 ppm, superior selectivity, good repeatability, and excellent long-term stability. The observed performance variation was attributed to the combined effects of specific surface area, abundant defect-associated surface sites, and suitable mesoporous structure. This work not only provides a high-performance TEA sensor for industrial and food safety monitoring but also offers a rational strategy for designing MOF-derived metal oxide gas sensors with tailored microstructures and surface defect chemistry.
A pyranochromene-based ligand, 2-amino-4-(4-chlorophenyl)-5-oxo-4H,5H-pyrano[3,2-c]chromene-3-carbonitrile (ACLPh-PC-3-CN), was employed as a chelating modifier for the electrochemical determination of Cd(II) in water samples. ACLPh-PC-3-CN was co-immobilized with Nafion on a glassy carbon electrode to form a stable ACLPh-PC-3-CN/Nafion film that combines ligand-based coordination with cation-exchange-assisted preconcentration of Cd2+ at the electrode surface. The Cd(II) response at the modified electrode was characterized by cyclic voltammetry and differential pulse anodic stripping voltammetry, and the data support a predominantly 1:1 Cd(II)-ligand interaction at the interface under the selected conditions. At an optimized pH of 6.0, the sensor provided a linear calibration range from 16.21 to 56.72 mu M, with a detection limit of 0.60 mu M and a quantification limit of 2.0 mu M, and showed good precision (repeatability 2.3% RSD, reproducibility 3.1% RSD) and short-term stability (94% of the initial response after 14 days). The ACLPh-PC-3-CN/Nafion-modified electrode tolerated common inorganic ions and surfactant species (<= 5% signal change) and was successfully applied to the determination of Cd(II) in tap water and Red Sea water, affording recoveries between 98.7% and 101%. While the current detection limit is higher than typical guideline values for Cd in drinking water, the proposed sensor compares favorably with several reported electrochemical Cd(II) sensors in terms of simplicity, precision, and matrix tolerance, and represents a useful platform for coordination-based electrochemical sensing of cadmium in environmental water samples.
Metalloporphyrins play an important role in biomedicine, catalysis, and energy, among other fields, due to their structural complexity and functional diversity. In this study, GO was used as the precursor support and chitosan was employed to reduce and functionalize GO into chitosan-functionalized rGO. Furthermore, metalloporphyrins were covalently linked to the amino side chains of chitosan via an amide crosslinking method, and a series of metalloporphyrin-chitosan-functionalized rGO nanocomposites were designed and synthesized. A set of poly(metalloporphyrin-chitosan)-functionalized rGO working electrodes was constructed by drop-coating onto glassy carbon electrodes, and their electrocatalytic performance toward dopamine was investigated in PBS solution. Finally, zinc(II) porphyrin, with the best performance, was selected as the core catalytic unit to fabricate an enzyme-free dopamine sensor. Under optimal working conditions, the sensor exhibited a sensitivity of 0.30 mA mM(-1)cm(-2), a linear detection range of 0.001 similar to 1.0 mM, and a low detection limit of 0.05 mu M (S/N = 3). The sensor showed anti-interference ability against various interfering ions and electroactive substances, as well as good stability and repeatability.
Food waste is often driven by consumer uncertainty about the spoilage of stored food, especially for cooked meal leftovers where microbial growth is the main concern. We analyzed whether metal oxide semiconductor (MOS) gas sensors placed inside ordinary food containers can monitor the edibility of leftovers, specifically cooked meatballs. Sensors were operated using temperature cycling to enhance selectivity, and cycle-aligned features were extracted. A prior calibration campaign produced information used to map cycle-aligned features into estimated gas concentrations for relevant VOCs. Total viable counts, which represent the growth of total number of spoilage microorganisms, were analyzed on days 0, 5 and 7 to determine the food’s freshness. Both the raw sensor features and the calibration-derived gas concentration estimates were analyzed with principal component analysis (PCA) and evaluated with a leave-one-sensor-out (LOSO) binary classifier for multiple food containers. PCA on the calibrated gas estimates revealed a dominant axis that consistently tracks food degradation over time across various containers. LOSO classification accuracy improved from 81.7% using raw sensor features to 87.8% using calibrated gas concentration estimates. These findings represent a proof of principle that calibrated MOS sensor systems can robustly support in situ edibility assessment for cooked food.
Short-wave near-infrared (SW-NIR) spectroscopy provides a rapid and nondestructive sensing route for monitoring bread staling, but formulation-dependent moisture redistribution and starch retrogradation can make pooled spectral regression unstable. This study investigated a stratified SW-NIR modeling strategy for bread staling prediction using 324 spectra from control bread (CR) and two maltogenic α-amylase treatments (EZ1 and EZ2). A global full-spectrum partial least squares (PLS) model was compared with bread-type-specific PLS models; competitive adaptive reweighted sampling (CARS), support vector machine recursive feature elimination (SVM-RFE), and multiple feature-spaces ensemble LASSO (MFE-LASSO) were then each coupled with PLS and evaluated within each bread type. The pooled benchmark achieved a root mean square error of prediction (RMSEP) of 2.28 days, whereas stratified full-spectrum PLS reduced this to 1.86, 2.14, and 2.15 days for CR, EZ1, and EZ2, respectively. In repeated wavelength-selection runs, MFE-LASSO was the most consistently competitive method across bread types. In the representative best-model comparison, MFE-LASSO-PLS yielded the strongest performance for CR (RMSEP = 1.71 days) and EZ1 (RMSEP = 1.43 days), while CARS-PLS gave the lowest RMSEP for EZ2 (2.00 days). An exploratory position-specific analysis within the CR subset further suggested that the middle crumb region carried stronger staling-related spectral information than the top and bottom regions. These results indicate that formulation-aware SW-NIR spectroscopic sensing is a practical strategy for nondestructive bread-staling assessment and that the optimal wavelength-selection method is bread-type-dependent.
Hydrogen sulfide (H2S) is a toxic and biologically relevant gas, necessitating sensitive and interference-resistant detection methods for environmental monitoring. Here, we develop a donor-acceptor molecular platform incorporating a polarized conjugated double bond bridge and demonstrate its application, using YG2 as the representative probe, as a dual-peak ratiometric UV-Vis sensor for H2S. UV-Vis spectroscopy, supported by H-1 NMR analysis, indicates HS--induced interaction with the conjugated linkage, leading to disruption of pi-conjugation, suppression the intramolecular charge-transfer (ICT) band at 409 nm, and enhancing the locally excited (LE) band at 279 nm. The ratiometric parameter log(Abs(279)/Abs(409)) affords a linear response over the concentration range of 1.0 x 10(-6)-1.0 x 10(-4) M with a detection limit of 8.3 x 10(-7) M, providing approximately an order-of-magnitude improvement in analytical sensitivity compared with single-wavelength methods, and the reaction reaches completion within similar to 10 s. YG2 exhibits excellent selectivity toward H2S over common anions and enables accurate quantification in real water samples, with recoveries of 95.43-105.86% and relative standard deviations (RSDs) of 0.56-9.58%. These results suggest that YG2 is a rapid, self-calibrating, and spectroscopically interpretable ratiometric probe suitable for reliable H2S detection in complex aqueous environments.
Elucidating structure–activity relationships in semiconductor photocatalysis has been significantly impeded by the inherent limitations of ensemble-averaged characterization techniques, which obscure the spatiotemporal heterogeneity intrinsic to catalytic surfaces. Single-molecule fluorescence microscopy (SMFM) surmounts this bottleneck by offering nanometer-scale spatial resolution coupled with the capacity to resolve single-turnover events. Herein, we provide a comprehensive overview of the State-of-the-Art applications of fluorogenic probe-coupled SMFM in deciphering the microscopic mechanisms governing photocatalysis. We begin by delineating the operational principles of total internal reflection fluorescence (TIRF) microscopy and categorizing the response mechanisms of three distinct classes of fluorogenic probes: oxidative (e.g., Amplex Red, APF), reductive (e.g., Resazurin, DN-BODIPY), and acidic (e.g., furfuryl alcohol, thiophene) reporters. Subsequently, we highlight seminal studies wherein SMFM has been leveraged to visualize facet-dependent charge separation on model photocatalysts—including TiO2, BiOBr, and InSe—to map the dynamic activity associated with surface defects and to precisely locate active sites during photoelectrochemical water splitting. Finally, we critically assess the prevailing technical challenges, such as limitations in probe specificity and background interference, while offering a perspective on prospective avenues for methodological refinement. This review is intended to serve as a methodological cornerstone for advancing mechanistic understanding in photocatalysis and for guiding the rational design of high-performance catalysts.
In this work, we propose a hyperbolic metamaterials (HMMs)-based coreless fiber surface plasmon resonance (SPR) sensor. Leveraging the absence of a core in coreless fibers, the evanescent waves at the cladding-external solution interface couple more effectively into the solution, enabling surface plasmon resonance without any additional processing. To enhance sensitivity, we adopted a multimode-coreless-multimode (MCM) structure and grew layered hyperbolic metamaterials as the SPR-excitation-sensitive layer within the coreless region. Through finite element simulations, we optimized HMM parameters and fabricated high-performance HMM-SPR sensors. Test results demonstrate that the fabricated HMM-SPR sensor achieves an optimal refractive index sensitivity of 3703.33 nm/RIU, representing a 49.68% improvement over single-layer gold film SPR sensors. It successfully detects glucose solutions at varying concentrations with a sensitivity of 2671.25 nm/RIU. The high-sensitivity, structurally simple HMM-SPR sensor we proposed demonstrates broad application prospects in biosensing, environmental monitoring, food safety, and other fields.
Metal oxide semiconductor (MOS) gas sensors are an important part of electronic nose technology because they are sensitive, cheap, and work well with microfabrication for system integration. But sensor drift makes them less useful for long-term, continuous gas monitoring. Changes in how sensors respond over time make pattern recognition models that were trained at first less accurate. This review looks at new ways to deal with sensor drift, with a focus on transfer learning and deep learning methods that have been developing continuously in the last five years. It emphasizes the shift from conventional recalibration and component correction to sophisticated methodologies, including deep domain adaptation, contrastive representation learning, and attention-based models. The review does not just list these methods; it also analyzes their pros and downsides, especially in situations where there is not much labeled data, drift is hard to anticipate, or the computational resources are limited, which is often the case with edge sensors.
