A volatile basic nitrogens-responsive tag based on aggregation-induced emission luminogen for real-time monitoring and In situ visualization of salmon freshness

Stacked long and short-term memory (SLSTM) - assisted terahertz spectroscopy combined with permutation importance for rapid red wine varietal identification

Mislabeling of low-value red wines as high-value ones is common, which seriously undermines consumer rights and interests. However, traditional sensory and chemical analysis methods have limitations, which highlights the need for novel detection techniques. To address above issues, terahertz time-domain spectroscopy (THz-TDS) combined with deep learning (DL) was employed to distinguish different red wine varieties quickly and non-destructively, contributing to correctly identifying red wine labels. Compared with the other models, the stacked long and short-term memory (SLSTM) model based on the first derivative (1-st der) spectra performed the best (Precision: 85.72 %, Recall: 85.61 %, F1-score: 85.59 %, Accuracy: 85.61 %). In addition, feature selection (FS) was used to explore the feasibility of improving model accuracy and reducing prediction time by eliminating redundant frequencies. Compared to full frequency, the 1-st der-SLSTM model based on permutation importance (PI) performed slightly lower (Precision: 84.42 %, Recall: 84.10 %, F1-score: 84.14 %, Accuracy: 84.18 %), but the prediction time was reduced by 2 s. Therefore, different models can be selected based on different detection needs by weighing accuracy and prediction time. In conclusion, the current research demonstrates that the SLSTM-assisted THz-TDS technology provides a novel approach for fast, accurate and non-destructive for fast, accurate and non-destructive discrimination of red wine labels, facilitating the maintenance of market discipline.

Intelligent pH-responsive ratiometric fluorescent nanofiber films for visual and real-time seafood freshness monitoring

In this study, polyvinyl alcohol (PVA)-based ratiometric fluorescence nanofiber films with salicylamide (SA) as the response signal, rhodamine B (RhB) as the internal reference were prepared using electrospinning technique, and reducing their water solubility by glutaraldehyde (G) crosslinked. The RhB and SA were successfully immobilized in the PVA substrate. Moreover, RhB and SA improved the water resistance, water vapor barrier, and mechanical strength of the nanofiber films, among the PVA nanofiber films containing RhB and 400 mg SA (PVA/RhB/4SA-G) showed the best packaging performance. Meanwhile, it exhibited high sensitivity to ammonia (limit of detection was 0.97 ppm), and could effectively distinguish the freshness states of shrimp stored at 4 and 25 °C by displaying a distinct shift from light pink to bright purple fluorescence. Moreover, the migration studies confirmed the safety of PVA/RhB/4SA-G. Therefore, the fabricated PVA/RhB/4SA-G can be utilized as a promising intelligent packaging for monitoring seafood freshness in real-time.

Rapid and simple fluorescent detection of chlorogenic acid in Aidi injection using aggregation-induced emission (AIE) nanoclusters

Chlorogenic acid (CGA) is a key component in Aidi injection, known for its anti-cancer properties and ability to reduce toxicity. Therefore, accurate detection of CGA levels in Aidi injection is essential for monitoring therapeutic efficacy and minimizing adverse effects. This study presents a rapid and simple fluorescent method for detecting CGA in Aidi injection using aggregation-induced emission (AIE) nanoclusters, i.e. D(-)-penicillamine (DPA)-capped bimetallic gold/copper nanoclusters (DPA-Au/CuNCs). Upon the addition of CGA, the aggregation state of DPA-Au/CuNCs was disrupted through hydrogen bond formation and ligand exchange, leading to fluorescence quenching. The prepared DPA-Au/CuNCs exhibited a rapid response time of 0.5 min, and demonstrated good sensitivity for CGA, with a limit of detection of 3.75 μg/mL, and a linear detection range of 12.5-200 μg/mL. This method was successfully applied for the analysis of CGA in Aidi injection and plasma with good recovery rates and minimal matrix effect, highlighting its potential for the applications in pharmaceutical products and clinical samples.

High sensitive Ratiometric fluorescent Aptasensor with AIE properties for Deoxynivalenol (DON) detection

An emerging fluorescent ratiometric aptasensor based on gold nanoclusters (AuNCs) with aggregation-induced emission (AIE) properties was prepared and studied for deoxynivalenol (DON) detection. The ratiometric aptasensor used red fluorescent AuNCs620 labelled with DON aptamer (Apt-AuNCs620) as an indicator and green fluorescent AuNCs519 modified by complementary DNA (cDNA) and magnetic beads (MBs) as internal reference, namely MBs-cDNA-AuNCs519. Under the optimal conditions, the aptasensor exhibited two good linear ranges of 0.1-50 and 50-5000 pg/mL for DON detection with coefficient of determination (R2) of 0.9937 and 0.9928, respectively, and the low detection limit (LOD) of 4.09 pg/mL was achieved. Furthermore, this aptasensor was feasible to detect DON in positive wheat samples, and the results were in line with those from HPLC and ELISA, thus providing a promising route to detect DON with high sensitivity in cereals, even for other mycotoxins by replacing the suitable aptamer and cDNA.

A fluorescent Aptasensor based on magnetic-separation strategy with gold nanoclusters for Deoxynivalenol (DON) detection

A highly sensitive method based on MBs-cDNA@Apt-AuNCs519 was developed for deoxynivalenol (DON) detection in wheat. The MBs-cDNA@Apt-AuNCs519 was established using green emission gold nanoclusters (AuNCs519) with aggregation-induced emission properties as signal probes and combining amino-modified DON-aptamer (Apt), biotin-modified DNA strand (the partially complementary to Apt (cDNA)), and streptavidin-modified magnetic beads (MBs). The Apt-AuNCs519 were well connected with MBs-cDNA without DON but dissociated from MBs-cDNA@Apt-AuNCs519 with the addition of DON, leading to a noticeable reduction in the fluorescent intensity of the aptasensor. Moreover, this fluorescence aptasensor showed two linear relationships in the concentration range of 0.1-50 ng/mL and 50-5000 ng/mL with a limit of detection of 3.73 pg/mL with good stability, reproducibility and specificity. The results were consistent with high-performance liquid chromatography and enzyme-linked immunosorbent assay methods, further indicating the potential of this method for accurate trace detection of DON in wheat.

A tailored dual core-shell magnetic SERS substrate with precise shell-thickness control for trace organophosphorus pesticides residues detection

For addressing the challenges of strong affinity SERS substrate to organophosphorus pesticides (OPs), herein, a rapid water-assisted layer-by-layer heteronuclear growth method was investigated to grow uniform UiO-66 shell with controllable thickness outside the magnetic core and provide abundant defect sites for OPs adsorption. By further assembling the tailored Au@Ag, a highly sensitive SERS substrate Fe3O4-COOH@UiO-66/Au@Ag (FCUAA) was synthesized with a SERS enhancement factor of 2.11 × 107. The substrate's suitability for the actual vegetable samples (cowpeas and peppers) was confirmed under both destructive and non-destructive detection conditions, showing a strong SERS response to fenthion and triazophos, with limits of detection of 1.21 × 10-5 and 2.96 × 10-3 mg/kg in the vegetables under destructive conditions, and 0.13 and 1.39 ng/cm2 for non-destructive detection, respectively. The FCUAA substrate had high SERS performance, effective adsorption capability for OPs, and demonstrated good applicability, thus exhibiting great potential for rapid detection of trace OPs residues in the food industry.

Fast real-time monitoring of meat freshness based on fluorescent sensing array and deep learning: From development to deployment

A fluorescent sensor array (FSA) combined with deep learning (DL) techniques was developed for meat freshness real-time monitoring from development to deployment. The array was made up of copper metal nanoclusters (CuNCs) and fluorescent dyes, having a good ability in the quantitative and qualitative detection of ammonia, dimethylamine, and trimethylamine gases with a low limit of detection (as low as 131.56 ppb) in range of 5 ∼ 1000 ppm and visually monitoring the freshness of various meats stored at 4 °C. Moreover, SqueezeNet was applied to automatically identify the fresh level of meat based on FSA images with high accuracy (98.17 %) and further deployed in various production environments such as personal computers, mobile devices, and websites by using open neural network exchange (ONNX) technique. The entire meat freshness recognition process only takes 5 ∼ 7 s. Furthermore, gradient-weighted class activation mapping (Grad-CAM) and uniform manifold approximation and projection (UMAP) explanatory algorithms were used to improve the interpretability and transparency of SqueezeNet. Thus, this study shows a new idea for FSA assisted with DL in meat freshness intelligent monitoring from development to deployment.

Red Emitting Solid-State CDs/PVP with Hydrophobicity for Latent Fingerprint Detection

Fluorescent carbon dots (CDs) are a new type of photoluminescent nanomaterial. Solid-state CDs usually undergo fluorescence quenching due to direct π-π* interactions and superabundant energy resonance transfer. Therefore, the preparation of solid-state fluorescent CDs is a challenge, especially the preparation of long wavelength solid-state CDs. In this research, long wavelength emission CDs were successfully synthesized by solvothermal methods, and the prepared CDs showed good hydrophobicity. The composite solid-state CDs/PVP (Polyvinyl pyrrolidone) can emit strong red fluorescence, and the quantum yield (QY) of the CDs/PVP powder reaches 18.9%. The prepared CDs/PVP solid-state powder was successfully applied to latent fingerprint detection. The results indicate that the latent fingerprints developed by CDs/PVP powder have a fine definition and high contrast visualization effect, which proves that the prepared CDs/PVP has great application potential in latent fingerprint detection. This study may provide inspiration and ideas for the design of new hydrophobic CDs.

Terahertz spectra reconstructed using convolutional denoising autoencoder for identification of rice grains infested with Sitophilus oryzae at different growth stages

Rice grains are often infected by Sitophilus oryzae due to improper storage, resulting in quality and quantity losses. The efficacy of terahertz time-domain spectroscopy (THz-TDS) technology in detecting Sitophilus oryzae at different stages of infestation in stored rice was employed in the current research. Terahertz (THz) spectra for rice grains infested by Sitophilus oryzae at different growth stages were acquired. Then, the convolutional denoising autoencoder (CDAE) was used to reconstruct THz spectra to reduce the noise-to-signal ratio. Finally, a random forest classification (RFC) model was developed to identify the infestation levels. Results showed that the RFC model based on the reconstructed second-order derivative spectrum with an accuracy of 84.78%, a specificity of 86.75%, a sensitivity of 86.36% and an F1-score of 85.87% performed better than the original first-order derivative THz spectrum with an accuracy of 89.13%, a specificity of 91.38%, a sensitivity of 88.18% and an F1-score of 89.16%. In addition, the convolutional layers inside the CDAE were visualized using feature maps to explain the improvement in results, illustrating that the CDAE can eliminate noise in the spectral data. Overall, THz spectra reconstructed with the CDAE provided a novel method for effective THz detection of infected grains.

La-Ce-MOF nanocomposite coated quartz crystal microbalance gas sensor for the detection of amine gases and formaldehyde

Trimethylamine (TMA), Dimethylamine (DMA), Ammonia (NH3) and formaldehyde (HCHO) are typical volatile gases and able to cause great damage to the environment and the human body, and they may appear along in some particular cases such as marine meat spoilage. However, gas sensors can detect all the 4 hazardous gases simultaneously have rarely been reported. In this study, a quartz crystal microbalance (QCM) gas sensor modified with La-Ce-MOF was employed for the detection of 4 target gases (TMA, DMA, NH3 and HCHO). The sensor exhibited excellent stability (63 days), selectivity (3.51 Hz/(μmoL/L) for TMA, 4.19 Hz/(μmoL/L) for DMA, 3.14·Hz/(μmoL/L) for NH3 and 3.08·Hz/(μmoL/L) for HCHO), robustness and sensitivity towards target gases detection. Vienna Ab-initio Simulation Package calculations showed that this superior sensing performance was attributed to the preferential adsorption of target gas molecules onto the nanomicrospheres via hydrogen bond. The adsorption energy was - 0.4329 eV for TMA, - 0.5204 eV for DMA, - 0.6823 eV for NH3 and - 0.7576 eV for HCHO, all of which are physically adsorbed. In the detection of hazardous gases, sensor surface active sites were often susceptible to environmental factors and interfering substances, leading to a decrease in the sensitivity of the gas sensor, which in turn affects the signal accuracy in practical applications. This issue has been effectively addressed and the sensor has been implemented for the assessment of the salmon meat freshness, which may contribute to further advancements in the development of QCM gas sensors for monitoring food quality, human beings health and environment safety.

Assembly-Induced Emission of Copper Nanoclusters: Revealing the Sensing Mechanism for Detection of Volatile Basic Nitrogen in Seafood Freshness On-Site Monitoring

Total volatile basic nitrogen (TVB-N) is a vital indicator for assessing seafood freshness and edibility. Rapid on-site detection of volatile basic nitrogen (VBN) is of significant importance for food safety monitoring. In this study, highly luminescent self-assembled copper nanoclusters (Cu NCs@p-MBA), synthesized using p-mercaptobenzoic acid (p-MBA) as the ligand, were utilized for the sensitive detection of VBNs. Under acidic conditions, Cu NCs@p-MBA formed compact and well-organized nanosheets through noncovalent interactions, accompanied by intense orange fluorescence emission (651 nm). The benzene carboxylic acid part of Cu NCs@p-MBA provided the driving force for supramolecular assembly and exhibited a strong affinity for amines, particularly low-molecular-weight amines such as ammonia (NH3) and trimethylamine (TMA). The quantitative determination of NH3 and TMA showed the detection limits as low as 0.33 and 0.81 ppm, respectively. Cu NCs@p-MBA also demonstrated good responsiveness to putrescine and histamine. Through density functional theory (DFT) calculations and molecular dynamics (MD) simulations, the precise atomic structure, assembly structure, luminescent properties, and reaction processes of Cu NCs@p-MBA were studied, revealing the sensing mechanism of Cu NCs@p-MBA for highly sensitive detection of VBNs. Based on the self-assembled Cu NCs@p-MBA nanosheets, portable fluorescent labels were developed for semiquantitative, visual, and real-time monitoring of seafood freshness. Therefore, this study exemplified the high sensitivity of self-assembly induced emission (SAIE)-type Cu NCs@p-MBA for VBNs sensing, offering an efficient solution for on-site monitoring of seafood freshness.

A durian-shaped multilayer core-shell SERS substrate for flow magnetic detection of pesticide residues on foods

A new type of durian-shaped Fe3O4@Au@Ag@Au (DFAAA) multilayer core-shell composite was prepared as an efficient surface-enhanced Raman scattering (SERS) substrate. The optimization process and SERS enhancement mechanism of the substrate were further explained with finite-difference time-domain simulation. The dense and uniform spiny array on the DFAAA surface had abundant "hot spots", greatly improving sensitivity, uniformity and reproducibility, with a Raman enhancement factor of 3.01 × 107 and storage-life of 30 d. A "flow magnetic detection method" was proposed to realize rapid and flexible detection of pesticide residues on the surface of different foods including fish and apple. The limit of detection of malachite green and thiram on the fish and apple surfaces were 0.13 and 0.18 ng/cm2, respectively. With its high SERS performance and good magnetic, the DFAAA possessed great application prospects as a facile SERS substrate for rapid and non-destructive detection of trace pesticide residues on foods.

Fluorescent polythymidine-templated copper nanoclusters aptasensor for sensitive detection of tropomyosin in processed shrimp products

Tropomyosin (TM) is the main allergen in shellfish. Developing a novel, simple and accurate method to track and detect TM in food products is necessary. In this work, a label-free fluorescent aptasensor based on polythymidine (poly(T))-templated copper nanoclusters (CuNCs) was designed for sensitive detection of TM in processed shrimp products. Magnetic beads (MBs), aptamer and cDNA were used to construct an MBs-aptamer@cDNA complex as a detection probe, and with the presence of TM, the poly(T)-templated CuNCs attached at the end of the cDNA as the fluorescent signal was released from the complex to turn on the fluorescence. Under optimal conditions, the poly(T)-templated CuNCs aptasensor achieved a linear range from 0.1 to 50 μg/mL (R2 = 0.9980), a low limit of detection of 0.0489 μg/mL and an excellent recovery percentage of 105.29%-108.91% in the complex food matrix, providing a new approach for food safety assurance.

A three-dimensional gold nanoparticles spherical liquid array for SERS sensitive detection of pesticide residues in apple

High-performance plasmonic substrates have recently attracted much research attention. Herein, a three-dimensional gold nanoparticles (AuNPs) spherical liquid array (SLA) with high "hot spots" and tunable nanometer gap by optimizing the proportion of AuNPs colloids over chloroform was synthesized based on a water-oil interfacial self-assembly strategy. The substrate demonstrated excellent surface-enhanced Raman scattering (SERS) performance using tetrathiafulvalene and rhodamine 6G (R6G) as probe molecules. With a simple extraction and soaking pretreatment process, the SLA exhibited high sensitivity for analysing triazophos on apple peels, with a limit of detection (LOD) of 0.005 µg/mL and recovery ranging from 96 to 110 %. Particularly, the chloroform produced an inherent characteristic peak at 665 cm-1, which was used as the internal standard to correct SERS signal fluctuation, leading to an improvement of the corresponding coefficient R2 from 0.97 to 0.99, thus improving the reproducibility. Therefore the SLA substrate possesses the potential for quantitative analysis of food contaminants.

A Novel Indicator Based on Polyacrylamide Hydrogel and Bromocresol Green for Monitoring the Total Volatile Basic Nitrogen of Fish

An intelligent indicator was developed by immobilizing bromocresol green (BCG) within the polyacrylamide (PAAm) hydrogel matrix to monitor the total volatile basic nitrogen (TVB-N) content of fish. The FTIR analysis indicated that BCG was effectively incorporated into the PAAm through the formation of intermolecular hydrogen bonds. A thermogravimetric analysis (TGA) showed that the PAAm/BCG indicator had a mere 0.0074% acrylamide monomer residue, meanwhile, the addition of BCG improved the thermal stability of the indicator. In vapor tests with various concentrations of trimethylamine, the indicator performed similarly at both 4 °C and 25 °C. The total color difference values (ΔE) exhibited a significant linear response to TVB-N levels ranging from 4.29 to 30.80 mg/100 g at 4 °C (R2 = 0.98). Therefore, the PAAm/BCG indicator demonstrated stable and sensitive color changes based on pH variations and could be employed in smart packaging for real-time assessment of fish freshness.

Applications of Gas Sensing in Food Quality Detection: A Review

Food products often face the risk of spoilage during processing, storage, and transportation, necessitating the use of rapid and effective technologies for quality assessment. In recent years, gas sensors have gained prominence for their ability to swiftly and sensitively detect gases, making them valuable tools for food quality evaluation. The various gas sensor types, such as metal oxide (MOX), metal oxide semiconductor (MOS) gas sensors, surface acoustic wave (SAW) sensors, colorimetric sensors, and electrochemical sensors, each offer distinct advantages. They hold significant potential for practical applications in food quality monitoring. This review comprehensively covers the progress in gas sensor technology for food quality assessment, outlining their advantages, features, and principles. It also summarizes their applications in detecting volatile gases during the deterioration of aquatic products, meat products, fruit, and vegetables over the past decade. Furthermore, the integration of data analytics and artificial intelligence into gas sensor arrays is discussed, enhancing their adaptability and reliability in diverse food environments and improving food quality assessment efficiency. In conclusion, this paper addresses the multifaceted challenges faced by rapid gas sensor-based food quality detection technologies and suggests potential interdisciplinary solutions and directions.

Preparation of Fe3O4@UiO-66(Zr)@Ag NPs core-shell-satellite structured SERS substrate for trace detection of organophosphorus pesticides residues

Surface-enhanced Raman spectroscopy (SERS) technology has been revived and developed with the introduction of metal-organic frameworks (MOFs), while more valuable properties of MOFs for SERS substrates remain largely unexplored. This work constructed a new SERS substrate Fe₃O₄@UiO-66(Zr)@Ag nanoparticles (FUAs) with excellent SERS detection sensitivity, uniformity, reproducibility and stability, exhibiting a high Raman enhancement factor (5.62 × 10⁶), low limit of detection (LOD, 2.11 × 10^-11 M) and RSD (12.41 %) for 4-NBT, and maintaining 81 % SERS activity within 60 days. The FUAs took full advantage of the strong affinity of UiO-66(Zr) for organophosphorus pesticides (OPs) to realize trace OPs detection. The LODs of phoxim, triazophos and methyl parathion in apple juice were 0.041, 0.021 and 0.0031 mg/L, respectively, with good linearities ranging from 0.02 or 0.1-50 mg/L, meeting the requirements of the food control standards, indicating that the potentials and prospects of the FUAs SERS substrate for trace detecting OPs in foods.

Distinguishing pericarpium citri reticulatae of different origins using terahertz time-domain spectroscopy combined with convolutional neural networks

The geographical indication of pericarpium citri reticulatae (PCR) is very important in grading the quality and price of PCRs. Therefore, terahertz time-domain spectroscopy (THz-TDS) technology combined with convolutional neural networks (CNN) was proposed to distinguish PCRs of different origins without damage in this study. The one-dimensional CNN (1D-CNN) model with an accuracy of 82.99% based on spectral data processed with SNV was established. The two-dimensional image features were transformed from unprocessed spectral data using the gramian angular field (GAF), the Markov transition field (MTF) and the recurrence plot (RP), which were used to build a two-dimensional CNN (2D-CNN) model with an accuracy of 78.33%. Further, the CNN models with different fusion methods were developed for fusing spectra data and image data. In addition, the adding spectra and images based on the CNN (Add-CNN) model with an accuracy of 86.17% performed better. Eventually, the Add-CNN model based on ten frequencies extracted using permutation importance (PI) achieved the identification of PCRs from different origins. Overall, the current study would provide a new method for identifying PCRs of different origins, which was expected to be used for the traceability of PCRs products.

A ratiometric fluorescent tag based on dual-emissive hydrophobic carbon dots for in-situ monitoring of seafood freshness

A ratiometric fluorescent tag based on dual-emissive hydrophobic carbon dots (H-CDs) with well volatile base nitrogens (VBNs)-response was fabricated, offering the in-situ, real-time, and visual evaluation of seafood freshness. The presented H-CDs aggregates exhibited a sensitive response to VBNs, with a limit of detection of 7 μM and 137 ppb for spermine and ammonia hydroxide, respectively. Subsequently, a ratiometric tag was successfully fabricated by depositing dual-emissive CDs on cotton paper. Upon the treatment with ammonia vapour, the presented tag exhibited highly recognizable colour variations from red to blue under UV light. In addition, the cytotoxicity was explored by CCK8 assay, and the results proved the nontoxicity of the presented H-CDs. To our knowledge, this is the first ratiometric tag based on dual-emissive CDs with aggregation-induced emission properties for recognition of VBNs and seafood freshness in a real-time and visual manner.

Discrimination of Pericarpium Citri Reticulatae in different years using Terahertz Time-Domain spectroscopy combined with convolutional neural network

Pericarpium Citri Reticulatae (PCR) in longer storage years possess higher medicinal values, but their differentiation is difficult due to similar morphological characteristics. Therefore, this study investigated the feasibility of using terahertz time-domain spectroscopy (THz-TDS) combined with a convolutional neural network (CNN) to identify PCR samples stored from 1 to 20 years. The absorption coefficient and refractive index spectra in the range of 0.2-1.5 THz were acquired. Partial least squares discriminant analysis, random forest, least squares support vector machines, and CNN were used to establish discriminant models, showing better performance of the CNN model than the others. In addition, the output data points of the CNN intermediate layer were visualized, illustrating gradual changes in these points from overlapping to clear separation. Overall, THz-TDS combined with CNN models could realize rapid identification of different year PCRs, thus providing an efficient alternative method for PCR quality inspection.

Analysis and detection using novel terahertz spectroscopy technique in dietary carbohydrate-related research: Principles and application advances

As one of the main functional substances, carbohydrates account for a large proportion of the human diet. Conventional analysis and detection methods of dietary carbohydrates and related products are destructive, time-consuming, and labor-intensive. In order to improve the efficiency of measurement and ensure food nutrition and consumer health, rapid and nondestructive quality evaluation techniques are needed. In recent years, terahertz (THz) spectroscopy, as a novel detection technology with dual characteristics of microwave and infrared, has shown great potential in dietary carbohydrate analysis. The current review aims to provide an up-to-date overview of research advances in using the THz spectroscopy technique in analysis and detection applications related to dietary carbohydrates. In the review, the principles of the THz spectroscopy technique are introduced. Advances in THz spectroscopy for quantitative and qualitative analysis and detection in dietary carbohydrate-related research studies from 2013 to 2022 are discussed, which include analysis of carbohydrate concentrations in liquid and powdery foods, detection of foreign body and chemical residues in carbohydrate food products, authentication of natural carbohydrate produce, monitoring of the fermentation process in carbohydrate food production and examination of crystallinity in carbohydrate polymers. In addition, applications in dietary carbohydrate-related detection research using other spectroscopic techniques are also briefed for comparison, and future development trends of THz spectroscopy in this field are finally highlighted.

Synthesis of recyclable SERS platform based on MoS2@TiO2@Au heterojunction for photodegradation and identification of fungicides

Surface-enhanced Raman spectroscopy (SERS) substrates based on metal/semiconductors have attracted much attention due to their excellent photocatalytic activity and SERS performance. However, they generally exhibit low light utilization and photocatalytic efficiencies. Herein, molybdenum disulfide coated titanium dioxide modified with gold nanoparticles (MoS₂@TiO₂@Au) as a heterojunction-based recyclable SERS platform was fabricated for the efficient determination of fungicides. Results showed that the MoS₂@TiO₂@Au platform could rapidly degrade 90.7% crystal violet in 120 min under solar light irradiation and enable reproducible and sensitive SERS analysis of three fungicides (methylene blue, malachite green, and crystal violet) and in-situ monitor of the photodegradation process. The platform could also be reused five times due to the unique integrated merits of the MoS₂@TiO₂@Au heterojunction. Meanwhile, experiments in determining methylene blue in prawn protein solution achieved a limit of detection of 1.509 μg/L. Therefore, it is hoped that this work could expand detection applications of photocatalytic materials.

Mesoporous silica coated core-shell nanoparticles substrate for size-selective SERS detection of chloramphenicol

With the growing popularity of the non-destructive technique, surface-enhanced Raman spectroscopy (SERS) demands a highly sensitive and reproducible plasmonic nanoparticles substrate. In this study, a novel bimetallic core-shell nanoparticles (Au@Ag@mSiO₂NP) substrate consisting of a gold core, silver shell, and a mesoporous silica coating was synthesized. The mesoporous coating structure was created by employing template molecules such as surfactant and their subsequent removal allowing selective screening based on the size of analyte molecules. Results showed that the plasmonic substrate could selectively enhance small molecules by preventing large macromolecules to reach the exciting zone of the substrate core, achieving the detection of chloramphenicol in milk samples with a detection limit of 6.68 × 10^-8 M. Moreover, the mesoporous coating provided additional stability to the Au@Ag nanoparticles, leading to the reusability of the substrate. Thus, this work offered a simple and smart Au@Ag@mSiO₂NP substrate for effective SERS detection of analytes.