Manipulation of Contact Angle Hysteresis at Electrified Ionic Liquid-Solid Interfaces.

Room-temperature ionic liquids (RTILs) are intriguing fluids that have drawn much attention in applications ranging from tribology and catalysis to energy storage. With strong electrostatic interaction between ions, their interfacial behaviors can be modulated by controlling energetics of the electrified interface. In this work, we report atomic-force-microscope measurements of contact angle hysteresis (CAH) of a circular contact line formed on a micron-sized fiber, which is coated with a thin layer of conductive film and intersects an RTIL-air interface. The measured CAH shows a distinct change by increasing the voltage U applied on the fiber surface. Molecular dynamics simulations were performed to illustrate variations of the solidlike layer in the RTIL adsorbed at the electrified interface. The integrated experiments and computations demonstrate a new mechanism to manipulate the CAH by rearrangement of interfacial layers of RTILs induced by the surface energetics.

Research on the Effects of Drying Temperature for the Detection of Soil Nitrogen by Near-Infrared Spectroscopy.

Nitrogen nitrates play a significant role in the soil's nutrient cycle, and near-infrared spectroscopy can efficiently and accurately detect the content of nitrate-nitrogen in the soil. Accordingly, it can provide a scientific basis for soil improvement and agricultural productivity by deeply examining the cycle and transformation pattern of nutrients in the soil. To investigate the impact of drying temperature on NIR soil nitrogen detection, soil samples with different N concentrations were dried at temperatures of 50 °C, 65 °C, 80 °C, and 95 °C, respectively. Additionally, soil samples naturally air-dried at room temperature (25 °C) were used as a control group. Different drying times were modified based on the drying temperature to completely eliminate the impact of moisture. Following data collection with an NIR spectrometer, the best preprocessing method was chosen to handle the raw data. Based on the feature bands chosen by the RFFS, CARS, and SPA methods, two linear models, PLSR and SVM, and a nonlinear ANN model were then established for analysis and comparison. It was found that the drying temperature had a great effect on the detection of soil nitrogen by near-infrared spectroscopy. In the meantime, the SPA-ANN model simultaneously yielded the best and most stable accuracy, with Rc2 = 0.998, Rp2 = 0.989, RMSEC = 0.178 g/kg, and RMSEP = 0.257 g/kg. The results showed that NIR spectroscopy had the least effect and the highest accuracy in detecting nitrogen at 80 °C soil drying temperature. This work provides a theoretical foundation for agricultural production in the future.

Multimodal Bubble Microrobot Near an Air-Water Interface.

The development of multifunctional and robust swimming microrobots working at the free air-liquid interface has encountered challenge as new manipulation strategies are needed to overcome the complicated interfacial restrictions. Here, flexible but reliable mechanisms are shown that achieve a remote-control bubble microrobot with multiple working modes and high maneuverability by the assistance of a soft air-liquid interface. This bubble microrobot is developed from a hollow Janus microsphere (JM) regulated by a magnetic field, which can implement switchable working modes like pusher, gripper, anchor, and sweeper. The collapse of the microbubble and the accompanying directional jet flow play a key role for functioning in these working modes, which is analogous to a "bubble tentacle." Using a simple gamepad, the orientation and the navigation of the bubble microrobot can be easily manipulated. In particular, a speed modulation method is found for the bubble microrobot, which uses vertical magnetic field to control the orientation of the JM and the direction of the bubble-induced jet flow without changing the fuel concentration. The findings demonstrate a substantial advance of the bubble microrobot specifically working at the air-liquid interface and depict some nonintuitive mechanisms that can help develop more complicated microswimmers.

High-Precision Automatic Identification of Fentanyl-Related Drugs by Terahertz Spectroscopy with Molecular Dynamics Simulation and Spectral Similarity Mapping.

Fentanyl is a potent opioid analgesic with high bioavailability. It is the leading cause of drug addiction and overdose death. To better control the abuse of fentanyl and its derivatives, it is crucial to develop rapid and sensitive detection methods. However, fentanyl-related substrates undergo similar molecular structures resulting in similar properties, which are difficult to be identified by conventional spectroscopic methods. In this work, a method for the automatic identification of 8 fentanyl-related substances with similar spectral characteristics was developed using terahertz (THz) spectroscopy coupled with density functional theory (DFT) and spectral similarity mapping (SSM). To characterize the THz fingerprints of these fentanyl-related samples more accurately, the method of baseline estimation and denoising with sparsity was performed before revealing the unique molecular dynamics of each substance by DFT. The SSM method was proposed to identify these fentanyl analogs based on weighted spectral cosine-cross similarity and fingerprint discrete Fréchet distance, generating a matching list by stepwise searching the entire spectral database. The top matched list returned the identification results of the target fentanyl analogs with accuracies of 94.48~99.33%. Results from this work provide algorithms' increased reliability, which serves as an artificial intelligence-based tool for high-precision fentanyl analysis in real-world samples.

Rapid Detection of Available Nitrogen in Soil by Surface-Enhanced Raman Spectroscopy.

Soil-available nitrogen is the main nitrogen source that plants can directly absorb for assimilation. It is of great significance to detect the concentration of soil-available nitrogen in a simple, rapid and reliable method, which is beneficial to guiding agricultural production activities. This study confirmed that Raman spectroscopy is one such approach, especially after surface enhancement; its spectral response is more sensitive. Here, we collected three types of soils (chernozem, loess and laterite) and purchased two kinds of nitrogen fertilizers (ammonium sulfate and sodium nitrate) to determine ammonium nitrogen (NH4-N) and nitrate nitrogen (NO3-N) in the soil. The spectral data were acquired using a portable Raman spectrometer. Unique Raman characteristic peaks of NH4-N and NO3-N in different soils were found at 978 cm-1 and 1044 cm-1, respectively. Meanwhile, it was found that the enhancement of the Raman spectra by silver nanoparticles (AgNPs) was greater than that of gold nanoparticles (AuNPs). Combined with soil characteristics and nitrogen concentrations, Raman peak data were analyzed by multiple linear regression. The coefficient of determination for the validation (Rp2) of multiple linear regression prediction models for NH4-N and NO3-N were 0.976 and 0.937, respectively, which deeply interpreted the quantitative relationship among related physical quantities. Furthermore, all spectral data in the range of 400-2000 cm-1 were used to establish the partial least squares (PLS), back-propagation neural network (BPNN) and least squares support vector machine (LSSVM) models for quantification. After cross-validation and comparative analysis, the results showed that LSSVM optimized by particle swarm methodology had the highest accuracy and stability from an overall perspective. For all datasets of particle swarm optimization LSSVM (PSO-LSSVM), the Rp2 was above 0.99, the root mean square errors of prediction (RMSEP) were below 0.15, and the relative prediction deviation (RPD) was above 10. The ultra-portable Raman spectrometer, in combination with scatter-enhanced materials and machine learning algorithms, could be a promising solution for high-efficiency and real-time field detection of soil-available nitrogen.

Rapid Detection of Different Types of Soil Nitrogen Using Near-Infrared Hyperspectral Imaging.

Rapid and accurate determination of soil nitrogen supply capacity by detecting nitrogen content plays an important role in guiding agricultural production activities. In this study, near-infrared hyperspectral imaging (NIR-HSI) combined with two spectral preprocessing algorithms, two characteristic wavelength selection algorithms and two machine learning algorithms were applied to determine the content of soil nitrogen. Two types of soils (laterite and loess, collected in 2020) and three types of nitrogen fertilizers, namely, ammonium bicarbonate (ammonium nitrogen, NH4-N), sodium nitrate (nitrate nitrogen, NO3-N) and urea (urea nitrogen, urea-N), were studied. The NIR characteristic peaks of three types of nitrogen were assigned and regression models were established. By comparing the model average performance indexes after 100 runs, the best model suitable for the detection of nitrogen in different types was obtained. For NH4-N, R2p = 0.92, RMSEP = 0.77% and RPD = 3.63; for NO3-N, R2p = 0.92, RMSEP = 0.74% and RPD = 4.17; for urea-N, R2p = 0.96, RMSEP = 0.57% and RPD = 5.24. It can therefore be concluded that HSI spectroscopy combined with multivariate models is suitable for the high-precision detection of various soil N in soils. This study provided a research basis for the development of precision agriculture in the future.

Analyzing cadmium-phytochelatin2 complexes in plant using terahertz and circular dichroism information.

Phytochelatins are plants' small metal-binding peptides which chelate internal heavy metals to form nontoxic complexes. Detecting the complexes in plants would simplify identification of cultivars with both high tolerance and enrichment capabilities for heavy metals which represent phytoextraction performance. Thus, a terahertz spectroscopy combined with density functional theory, chemometrics and circular dichroism was used for characterization of phytochelatin2 (PC2), Cd-PC2 mixture standards, and pak choi (Brassica chinensis) leaves as a plant model. Results showed PC2 chelates Cd2+ in a 2:1 ratio to form Cd(PC2)2 complex; Cd connected to thoils of PC2 and changed ß-turn and random coil of PC2 peptide chain to ß-Sheet which presented as terahertz vibrations of PC2 around 1.03 and 1.71 THz being suppressed; the best models for detecting the complex in pak choi were obtained by partial least squares regression modeling combined with successive projections algorithm selection; the models used PC2 as a natural probe for visualizing and quantifying chelated Cd in pak choi leaf and achieved a limit of detection up to 1.151 ppm. This study suggested that terahertz information of the heavy metal-PCs complexes is qualified for representing a simpler alternative to classical index for evaluating phytoextraction performance of plant; it provided a general protocol for structure analysis and detection of heavy metal-PCs complexes in plant by terahertz absorbance.

Hyperspectral Imaging (HSI) Technology for the Non-Destructive Freshness Assessment of Pearl Gentian Grouper under Different Storage Conditions.

This study used visible/near-infrared hyperspectral imaging (HSI) technology combined with chemometric methods to assess the freshness of pearl gentian grouper. The partial least square discrimination analysis (PLS-DA) and competitive adaptive reweighted sampling-PLS-DA (CARS-PLS-DA) models were used to classify fresh, refrigerated, and frozen-thawed fish. The PLS-DA model achieved better classification of fresh, refrigerated, and frozen-thawed fish with the accuracy of 100%, 96.43%, and 96.43%, respectively. Further, the PLS regression (PLSR) and CARS-PLS regression (CARS-PLSR) models were used to predict the storage time of fish under different storage conditions, and the prediction accuracy was assessed using the prediction correlation coefficients (Rp2), root mean squared error of prediction (RMSEP), and residual predictive deviation (RPD). For the prediction of storage time, the CARS-PLS model presented the better result of room temperature (Rp2 = 0.948, RMSEP = 0.255, RPD = 4.380) and refrigeration (Rp2 = 0.9319, RMSEP = 1.188, RPD = 3.857), while the better prediction of freeze was by obtained by the PLSR model (Rp2 = 0.9250, RMSEP = 2.910, RPD = 3.469). Finally, the visualization of storage time based on the PLSR model under different storage conditions were realized. This study confirmed the potential of HSI as a rapid and non-invasive technique to identify fish freshness.

Trace Identification and Visualization of Multiple Benzimidazole Pesticide Residues on Toona sinensis Leaves Using Terahertz Imaging Combined with Deep Learning.

Molecular spectroscopy has been widely used to identify pesticides. The main limitation of this approach is the difficulty of identifying pesticides with similar molecular structures. When these pesticide residues are in trace and mixed states in plants, it poses great challenges for practical identification. This study proposed a state-of-the-art method for the rapid identification of trace (10 mg·L-1) and multiple similar benzimidazole pesticide residues on the surface of Toona sinensis leaves, mainly including benzoyl (BNL), carbendazim (BCM), thiabendazole (TBZ), and their mixtures. The new method combines high-throughput terahertz (THz) imaging technology with a deep learning framework. To further improve the model reliability beyond the THz fingerprint peaks (BNL: 0.70, 1.07, 2.20 THz; BCM: 1.16, 1.35, 2.32 THz; TBZ: 0.92, 1.24, 1.66, 1.95, 2.58 THz), we extracted the absorption spectra in frequencies of 0.2-2.2 THz from images as the input to the deep convolution neural network (DCNN). Compared with fuzzy Sammon clustering and four back-propagation neural network (BPNN) models (TrainCGB, TrainCGF, TrainCGP, and TrainRP), DCNN achieved the highest prediction accuracies of 100%, 94.51%, 96.26%, 94.64%, 98.81%, 94.90%, 96.17%, and 96.99% for the control check group, BNL, BCM, TBZ, BNL + BCM, BNL + TBZ, BCM + TBZ, and BNL + BCM + TBZ, respectively. Taking advantage of THz imaging and DCNN, the image visualization of pesticide distribution and residue types on leaves was realized simultaneously. The results demonstrated that THz imaging and deep learning can be potentially adopted for rapid-sensing detection of trace multi-residues on leaf surfaces, which is of great significance for agriculture and food safety.

Rapid Quantitative Detection of Deltamethrin in Corydalis yanhusuo by SERS Coupled with Multi-Walled Carbon Nanotubes.

With the increase in demand, artificially planting Chinese medicinal materials (CHMs) has also increased, and the ensuing pesticide residue problems have attracted more and more attention. An optimized quick, easy, cheap, effective, rugged and safe (QuEChERS) method with multi-walled carbon nanotubes as dispersive solid-phase extraction sorbents coupled with surface-enhanced Raman spectroscopy (SERS) was first proposed for the detection of deltamethrin in complex matrix Corydalis yanhusuo. Our results demonstrate that using the optimized QuEChERS method could effectively extract the analyte and reduce background interference from Corydalis. Facile synthesized gold nanoparticles with a large diameter of 75 nm had a strong SERS enhancement for deltamethrin determination. The best prediction model was established with partial least squares regression of the SERS spectra ranges of 545~573 cm-1 and 987~1011 cm-1 with a coefficient of determination (R2) of 0.9306, a detection limit of 0.484 mg/L and a residual predictive deviation of 3.046. In summary, this article provides a new rapid and effective method for the detection of pesticide residues in CHMs.

Multiband terahertz absorber and selective sensing performance.

Multiband terahertz absorbers are essential photonic components for responding to, manipulating, and modulating terahertz waves. In this work, improved electric split resonant ring arrays are used to demonstrate multiband terahertz wave absorption. The proposed design strategy is simple, practical, and significant. Experiments and simulations reveal perfect absorption at 0.918 THz and 1.575 THz for the transverse magnetic (TM) polarization and at 0.581, 1.294, and 1.556 THz for the transverse electric (TE) polarization. In addition, the weak resonant peaks that occurred in the experiments in both polarization states have been verified by the simulations. Furthermore, five concentration gradients of 2, 4-dichlorophenoxyacetic acid solutions and six concentration gradients of chlorpyrifos have been detected using the absorber. The lowest detectable concentration that could be monitored was 0.1 ppm. The absorption, intensity, and frequency shift values for the different solution concentrations at the resonant peaks were analyzed. The highest linear regression coefficients were 0.9862 and 0.9565 for the TE and TM polarizations, respectively. This multi-band absorber was demonstrated to be highly efficient in detecting pesticides for food safety applications.

Rapid Determination of Chlorogenic Acid, Luteoloside and 3,5-O-dicaffeoylquinic Acid in Chrysanthemum Using Near-Infrared Spectroscopy.

The feasibility of near-infrared spectroscopy (NIR) to detect chlorogenic acid, luteoloside and 3,5-O-dicaffeoylquinic acid in Chrysanthemum was investigated. An NIR spectroradiometer was applied for data acquisition. The reference values of chlorogenic acid, luteoloside, and 3,5-O-dicaffeoylquinic acid of the samples were determined by high-performance liquid chromatography (HPLC) and were used for model calibration. The results of six preprocessing methods were compared. To reduce input variables and collinearity problems, three methods for variable selection were compared, including successive projections algorithm (SPA), genetic algorithm-partial least squares regression (GA-PLS), and competitive adaptive reweighted sampling (CARS). The selected variables were employed as the inputs of partial least square (PLS), back propagation-artificial neural networks (BP-ANN), and extreme learning machine (ELM) models. The best performance was achieved by BP-ANN models based on variables selected by CARS for all three chemical constituents. The values of rp2 (correlation coefficient of prediction) were 0.924, 0.927, 0.933, the values of RMSEP were 0.033, 0.018, 0.064 and the values of RPD were 3.667, 3.667, 2.891 for chlorogenic acid, luteoloside, and 3,5-O-dicaffeoylquinic acid, respectively. The results indicated that NIR spectroscopy combined with variables selection and multivariate calibration methods could be considered as a useful tool for rapid determination of chlorogenic acid, luteoloside, and 3,5-O-dicaffeoylquinic acid in Chrysanthemum.

Gold Nanoparticles with Different Particle Sizes for the Quantitative Determination of Chlorpyrifos Residues in Soil by SERS.

Chlorpyrifos (CPF) is widely used in the prevention and control of crop pests and diseases in agriculture. However, the irrational utilization of pesticides not only causes environmental pollution but also threatens human health. Compared with the conventional techniques for the determination of pesticides in soil, surface-enhanced Raman spectroscopy (SERS) has shown great potential in ultrasensitive and chemical analysis. Therefore, this paper reported a simple method for synthesizing gold nanoparticles (AuNPs) with different sizes used as a SERS substrate for the determination of CPF residues in soil for the first time. The results showed that there was a good linear correlation between the SERS characteristic peak intensity of CPF and particle size of the AuNPs with an R2 of 0.9973. Moreover, the prepared AuNPs performed great ultrasensitivity, reproducibility and chemical stability, and the limit of detection (LOD) of the CPF was found to be as low as 10 µg/L. Furthermore, the concentrations ranging from 0.01 to 10 mg/L were easily observed by SERS with the prepared AuNPs and the SERS intensity showed a good linear relationship with an R2 of 0.985. The determination coefficient (Rp2) reached 0.977 for CPF prediction using the partial least squares regression (PLSR) model and the LOD of CPF residues in soil was found to be as low as 0.025 mg/kg. The relative standard deviation (RSD) was less than 3.69% and the recovery ranged from 97.5 to 103.3%. In summary, this simple method for AuNPs fabrication with ultrasensitivity and reproducibility confirms that the SERS is highly promising for the determination of soil pesticide residues.

Gold Nanoparticles for Qualitative Detection of Deltamethrin and Carbofuran Residues in Soil by Surface Enhanced Raman Scattering (SERS).

The residues of deltamethrin (DM) and carbofuran (CBF) in soil is becoming an intractable problem causing soil hardening and environmental pollution. This paper reports a very simple method via improved reduction of chloroauric acid by the trisodium citrate method for fabricating gold nanoparticles (AuNPs), which were used as a surface enhanced Raman scattering (SERS) active colloids with the advantages of ultrasensitivity, reproducibility and chemical stability. The results demonstrated that the limits of detection (LODs) of the DM and CBF were found to be as low as 0.01 mg/L. The SERS intensity showed a good linear relationship with DM (R² = 0.9908) and CBF (R² = 0.9801) concentration from 0.01 to 10 mg/L. In a practical application, DM and CBF residues in soil were easily detected by SERS with the flexible AuNPs colloids, and the LODs of DM and CBF were found to be as low as 0.056 mg/kg and 0.053 mg/kg, respectively. Moreover, DM in soil could be qualitatively detected by the characteristic peaks at 560 and 1000 cm-1, and CBF in soil could be qualitatively detected by the characteristic peaks at 1000 and 1299 cm-1. The determination coefficient (R²p) for DM and CBF reached 0.9176 and 0.8517 in partial least squares (PLS) model. Overall, it is believed that the prepared AuNPs can provide technical support for the accurate detection of pesticide residues in soil by SERS technique.

Rapid and Quantitative Determination of Sildenafil in Cocktail Based on Surface Enhanced Raman Spectroscopy.

Sildenafil (SD) and its related compounds are the most common adulterants found in herbal preparations used as sexual enhancer or man's virility products. However, the abuse of SD threatens human health such as through headache, back pain, rhinitis, etc. Therefore, it is important to accurately detect the presence of SD in alcoholic beverages. In this study, the Opto Trace Raman 202 (OTR 202) was used as a surface-enhanced Raman spectroscopy (SERS) active colloids to detect SD. The results demonstrated that the limit of detection (LOD) of SD was found to be as low as 0.1 mg/L. Moreover, 1235, 1401, 1530, and 1584 cm-1 could be qualitatively determined as SD characteristic peaks. In a practical application, SD in cocktail could be easily detected using SERS based on OTR 202. Also, there was a good linear correlation between the intensity of Raman peaks at 1235, 1401, 1530, and 1584 cm-1 and the logarithm of SD concentration in cocktail was in the range of 0.1-10 mg/L (0.9822 < R2 < 0.9860). The relative standard deviation (RSD) was less than 12.7% and the recovery ranged from 93.0%-105.8%. Moreover, the original 500-1700 cm-1 SERS spectra were pretreated and the partial least squares (PLS) was applied to establish the prediction model between SERS spectra and SD content in cocktail and the highest determination coefficient (Rp2) reached 0.9856. In summary, the SD in cocktail could be rapidly and quantitatively determined by SERS, which was beneficial to provide a rapid and accurate scheme for the detection of SD in alcoholic beverages.

Research on the Effects of Drying Temperature on Nitrogen Detection of Different Soil Types by Near Infrared Sensors.

Soil is a complicated system whose components and mechanisms are complex and difficult to be fully excavated and comprehended. Nitrogen is the key parameter supporting plant growth and development, and is the material basis of plant growth as well. An accurate grasp of soil nitrogen information is the premise of scientific fertilization in precision agriculture, where near infrared sensors are widely used for rapid detection of nutrients in soil. However, soil texture, soil moisture content and drying temperature all affect soil nitrogen detection using near infrared sensors. In order to investigate the effects of drying temperature on the nitrogen detection in black soil, loess and calcium soil, three kinds of soils were detected by near infrared sensors after 25 °C placement (ambient temperature), 50 °C drying (medium temperature), 80 °C drying (medium-high temperature) and 95 °C drying (high temperature). The successive projections algorithm based on multiple linear regression (SPA-MLR), partial least squares (PLS) and competitive adaptive reweighted squares (CARS) were used to model and analyze the spectral information of different soil types. The predictive abilities were assessed using the prediction correlation coefficients (RP), the root mean squared error of prediction (RMSEP), and the residual predictive deviation (RPD). The results showed that the loess (RP = 0.9721, RMSEP = 0.067 g/kg, RPD = 4.34) and calcium soil (RP = 0.9588, RMSEP = 0.094 g/kg, RPD = 3.89) obtained the best prediction accuracy after 95 °C drying. The detection results of black soil (RP = 0.9486, RMSEP = 0.22 g/kg, RPD = 2.82) after 80 °C drying were the optimum. In conclusion, drying temperature does have an obvious influence on the detection of soil nitrogen by near infrared sensors, and the suitable drying temperature for different soil types was of great significance in enhancing the detection accuracy.

Spectral Analysis and Sensitive Waveband Determination Based on Nitrogen Detection of Different Soil Types Using Near Infrared Sensors.

Compared with the chemical analytical technique, the soil nitrogen acquisition method based on near infrared (NIR) sensors shows significant advantages, being rapid, nondestructive, and convenient. Providing an accurate grasp of different soil types, sensitive wavebands could enhance the nitrogen estimation efficiency to a large extent. In this paper, loess, calcium soil, black soil, and red soil were used as experimental samples. The prediction models between soil nitrogen and NIR spectral reflectance were established based on three chemometric methods, that is, partial least squares (PLS), backward interval partial least squares (BIPLS), and back propagation neural network (BPNN). In addition, the sensitive wavebands of four kinds of soils were selected by competitive adaptive reweighted sampling (CARS) and BIPLS. The predictive ability was assessed by the coefficient of determination R² and the root mean square error (RMSE). As a result, loess ( 0.93 < R p 2 < 0.95 , 0.066 g / kg < RMSE p < 0.075 g / kg ) and calcium soil ( 0.95 < R p 2 < 0.96 , 0.080 g / kg < RMSE p < 0.102 g / kg ) achieved a high prediction accuracy regardless of which algorithm was used, while black soil ( 0.79 < R p 2 < 0.86 , 0.232 g / kg < RMSE p < 0.325 g / kg ) obtained a relatively lower prediction accuracy caused by the interference of high humus content and strong absorption. The prediction accuracy of red soil ( 0.86 < R p 2 < 0.87 , 0.231 g / kg < RMSE p < 0.236 g / kg ) was similar to black soil, partly due to the high content of iron-aluminum oxide. Compared with PLS and BPNN, BIPLS performed well in removing noise and enhancing the prediction effect. In addition, the determined sensitive wavebands were 1152 nm-1162 nm and 1296 nm-1309 nm (loess), 1036 nm-1055 nm and 1129 nm-1156 nm (calcium soil), 1055 nm, 1281 nm, 1414 nm-1428 nm and 1472 nm-1493 nm (black soil), 1250 nm, 1480 nm and 1680 nm (red soil). It is of great value to investigate the differences among the NIR spectral characteristics of different soil types and determine sensitive wavebands for the more efficient and portable NIR sensors in practical application.

Rapid Determination of Thiabendazole Pesticides in Rape by Surface Enhanced Raman Spectroscopy.

Thiabendazole is widely used in sclerotium blight, downy mildew and black rot prevention and treatment in rape. Accurate monitoring of thiabendazole pesticides in plants will prevent potential adverse effects to the Environment and human health. Surface Enhanced Raman Spectroscopy (SERS) is a highly sensitive fingerprint with the advantages of simple operation, convenient portability and high detection efficiency. In this paper, a rapid determination method of thiabendazole pesticides in rape was conducted combining SERS with chemometric methods. The original SERS were pretreated and the partial least squares (PLS) was applied to establish the prediction model between SERS and thiabendazole pesticides in rape. As a result, the SERS enhancing effect based on silver Nano-substrate was better than that of gold Nano-substrate, where the detection limit of thiabendazole pesticides in rape could reach 0.1 mg/L. Moreover, 782, 1007 and 1576 cm−1 could be determined as thiabendazole pesticides Raman characteristic peaks in rape. The prediction effect of thiabendazole pesticides in rape was the best ( R p 2 = 0.94, RMSEP = 3.17 mg/L) after the original spectra preprocessed with 1st-Derivative, and the linear relevance between thiabendazole pesticides concentration and Raman peak intensity at 782 cm−1 was the highest (R² = 0.91). Furthermore, five rape samples with unknown thiabendazole pesticides concentration were used to verify the accuracy and reliability of this method. It was showed that prediction relative standard deviation was 0.70–9.85%, recovery rate was 94.71–118.92% and t value was −1.489. In conclusion, the thiabendazole pesticides in rape could be rapidly and accurately detected by SERS, which was beneficial to provide a rapid, accurate and reliable scheme for the detection of pesticides residues in agriculture products.

Quantitative Determination of Thiabendazole in Soil Extracts by Surface-Enhanced Raman Spectroscopy.

Thiabendazole (TBZ) is widely used in sclerotium blight, downy mildew as well as root rot disease prevention and treatment in plant. The indiscriminate use of TBZ causes the excess pesticide residues in soil, which leads to soil hardening and environmental pollution. Therefore, it is important to accurately monitor whether the TBZ residue in soil exceeds the standard. For this study, density functional theory (DFT) was used to theoretically analyze the molecular structure of TBZ, gold nanoparticles (AuNPs) were used to enhance the detection signal of surface-enhanced Raman spectroscopy (SERS) and the TBZ residue in red soil extracts was quantitatively determined by SERS. As a result, the theoretical Raman peaks of TBZ calculated by DFT were basically consistent with the measured results. Moreover, 784, 1008, 1270, 1328, 1406 and 1576 cm-1 could be determined as the TBZ characteristic peaks in soil and the limits of detection (LOD) could reach 0.1 mg/L. Also, there was a good linear correlation between the intensity of Raman peaks and TBZ concentration in soil (784 cm-1: y = 672.26x + 5748.4, R² = 0.9948; 1008 cm-1: y = 1155.4x + 8740.2, R² = 0.9938) and the limit of quantification (LOQ) of these two linear models can reach 1 mg/L. The relative standard deviation (RSD) ranged from 1.36% to 8.02% and the recovery was ranging from 95.90% to 116.65%. In addition, the 300⁻1700 cm-1 SERS of TBZ were analyzed by the partial least squares (PLS) and backward interval partial least squares (biPLS). Also, the prediction accuracy of TBZ in soil (Rp² = 0.9769, RMSEP = 0.556 mg/L, RPD = 5.97) was the highest when the original spectra were pretreated by standard normal variation (SNV) and then modeled by PLS. In summary, the TBZ in red soil extracts could be quantitatively determined by SERS based on AuNPs, which was beneficial to provide a new, rapid and accurate scheme for the detection of pesticide residues in soil.

Density Functional Theory Analysis of Deltamethrin and Its Determination in Strawberry by Surface Enhanced Raman Spectroscopy.

Deltamethrin is widely used in pest prevention and control such as red spiders, aphids, and grubs in strawberry. It is important to accurately monitor whether the deltamethrin residue in strawberry exceeds the standard. In this paper, density functional theory (DFT) was used to theoretically analyze the molecular structure of deltamethrin, gold nanoparticles (AuNPs) and silver nanoparticles (AgNPs) were used to enhance the surface enhanced Raman spectroscopy (SERS) detection signal. As a result, the theoretical Raman peaks of deltamethrin calculated by DFT were basically similar to the measured results, and the enhancing effects based on AuNPs was better than that of AgNPs. Moreover, 554, 736, 776, 964, 1000, 1166, 1206, 1593, 1613, and 1735 cm−1 could be determined as deltamethrin characteristic peaks, among which only three Raman peaks (736, 1000, and 1166 cm−1) could be used as the deltamethrin characteristic peaks in strawberry when the detection limit reached 0.1 mg/L. In addition, the 500⁻1800 cm−1 SERS of deltamethrin were analyzed by the partial least squares (PLS) and backward interval partial least squares (BIPLS). The prediction accuracy of deltamethrin in strawberry (Rp2 = 0.93, RMSEp = 4.66 mg/L, RPD = 3.59) was the highest when the original spectra were pretreated by multiplicative scatter correction (MSC) and then modeled by BIPLS. In conclusion, the deltamethrin in strawberry could be qualitatively analyzed and quantitatively determined by SERS based on AuNPs enhancement, which provides a new detection scheme for deltamethrin residue determination in strawberry.