Ag supraparticles with 3D hot spots to actively capture molecules for sensitive detection by surface enhanced Raman spectroscopy.

To achieve highly sensitive detection using surface-enhanced Raman spectroscopy (SERS), it is imperative to fabricate a substrate with a high density of hot spots and facilitate the entry of target molecules into these hot spot regions. However, steric hindrance arising from the presence of surfactants and ligands on the SERS substrate may impede the access of target molecules to the hot spots. Here, we fabricate non-close-packed three-dimensional (3D) supraparticles with high-density hot spots to actively capture molecules. The formation of 3D supraparticles is attributed to the minimization of free energy during the gradual contraction of the droplet. The numerous capillaries present in non-close-packed supraparticles induce the movement of target molecules into the hot spot region through capillary force along with the solution. The results demonstrate that the SERS enhancement effect of 3D supraparticles is at least one order of magnitude higher than that of multi-layered nanoparticle structures formed under natural drying conditions. In addition, the SERS performance of 3D supraparticles is evaluated with diverse target molecules, including antimicrobial agents and drugs. Hence, this work provides a new idea for the preparation of non-close-packed substrates for SERS sensitive detection.

Observing π-Au Interaction between Aromatic Molecules and Single Au Nanodimers with a Subnanometer Gap by SERS.

Interface interaction between aromatic molecules and noble metals plays a prominent role in fundamental science and technological applications. However, probing π-metal interactions under ambient conditions remains challenging, as it requires characterization techniques to have high sensitivity and molecular specificity without any restrictions on the sample. Herein, the interactions between polycyclic aromatic hydrocarbon (PAH) molecules and Au nanodimers with a subnanometer gap are investigated by surface-enhanced Raman spectroscopy (SERS). A cleaner and stronger plasmonic field of subnanometer gap Au nanodimer structures was constructed through solvent extraction. High sensitivity and strong π-Au interaction between PAHs and Au nanodimers are observed. Additionally, the density functional theory calculation confirmed the interactions of PAHs physically absorbed on the Au surface; the binding energy and differential charge further theoretically indicated the correlation between the sensitivity and the number of PAH rings, which is consistent with SERS experimental results. This work provides a new method to understand the interactions between aromatic molecules and noble metal surfaces in an ambient environment, also paving the way for designing the interfaces in the fields of catalysis, sensors, and molecular electronics.

Three-Phase Catassembly of 10 nm Au Nanoparticles for Sensitive and Stable Surface-Enhanced Raman Scattering Detection.

Interfacial self-assembly with the advantage of providing large-area, high-density plasmonic hot spots is conducive to achieving high sensitivity and stable surface-enhanced Raman scattering (SERS) sensing. However, rapid and simple assembly of highly repeatable large-scale multilayers with small nanoparticles remains a challenge. Here, we proposed a catassembly approach, where the "catassembly" means the increase in the rate and control of nanoparticle assembly dynamics. The catassembly approach was dropping heated Au sols onto oil chloroform (CHCl3), which triggers a rapid assembly of plasmonic multilayers within 15 s at the oil-water-air (O/W/A) interface. A mixture of heated sol and CHCl3 constructs a continuous liquid-air interfacial tension gradient; thus, the plasmonic multilayer film can form rapidly without adding functional ligands. Also, the dynamic assembly process of the three-phase catassembly ranging from cluster to interfacial film formation was observed through experimental characterization and COMSOL simulation. Importantly, the plasmonic multilayers of 10 nm Au NPs for SERS sensing demonstrated high sensitivity with the 1 nM level for crystal violet molecules and excellent stability with an RSD of about 10.0%, which is comparable to the detection level of 50 nm Au NPs with layer-by-layer assembly, as well as breaking the traditional and intrinsic understanding of small particles of plasmon properties. These plasmonic multilayers of 10 nm Au NPs through the three-phase catassembly method illustrate high SERS sensitivity and stability, paving the way for small-nanoparticle SERS sensing applications.

Real-Time Monitoring of a Single Molecule in Sub-nanometer Space by Dynamic Surface-Enhanced Raman Spectroscopy.

In biology and chemistry, the ultimate goal is to monitor single molecules without labels. However, long-term monitoring of label-free molecules remains a challenge. Here, on the basis of the photothermal effect of gold nanorods (GNRs), we developed a platform for monitoring of a single molecule employing surface-enhanced Raman spectroscopy (SERS). Laser re-irradiation forms 1.0 nm gaps between GNRs, allowing us to observe single crystal violet (CV) molecules blinking for up to 4 min with dynamic surface-enhanced Raman spectroscopy (D-SERS). Bianalyte experiments confirm single-molecule features at CV concentrations of 10-14 M. Combining density functional theory (DFT) calculations with a free CV molecule observed in millisecond D-SERS, we propose that CV molecules can be confined to sub-nanometer space and the orientation of an individual CV moving in the range of 50-90° can be dynamically captured by D-SERS. This will provide a novel idea for effective exploration of the temporal and spatial dynamic processes of different reactions.

Near-Neighbor Electron Orbital Coupling Effect of Single-Atomic-Layer Au Cluster Intercalated Bilayer 2H-TaS2 for Surface Enhanced Raman Scattering Sensing.

It is difficult to perfectly analyze the enhancement mechanism of two-dimensional (2D) materials and their combination with precious metals as surface enhanced Raman scattering (SERS) substrates using chemical enhancement mechanisms. Here, we propose a new mentality based on the coupling effect of neighboring electron orbitals to elucidate the electromagnetic field enhancement mechanism of single-atom-layer Au clusters embedded in double-layer 2H-TaS2 for SRES sensing. The insertion of Au atoms into the 2H-TaS2 interlayer was verified by XRD, AFM, and HRTEM, and a SERS signal enhancement of 2 orders of magnitude was obtained compared to the pure 2H-TaS2. XPS and micro-UV/vis-NIR spectra indicate that the outer electrons of neighboring Au and 2H-TaS2 overlap and migrate from Au to 2H-TaS2. First-principles calculations suggest strong electronic coupling between Au and 2H-TaS2. This study offers insights into SERS enhancement in nonprecious metal compounds and guides the development of new SERS substrates.

An autofocusing method for dynamic surface-enhanced Raman spectroscopy detection realized by optimized hill-climbing algorithm with long time stable hotspots.

In the manual dynamic surface-enhanced Raman spectroscopy (D-SERS) detection process, it is difficult to focus on sample drop due to the constantly changing hotspot and easy judgment method. In this paper, we proposed an automatic focusing method based on long time stable hotspot with aid of optimization of hill-climbing algorithm and achieved on a designed device. First, set up a high temperature accelerating evaporation process to obtain hotspot and then cool to a low temperature rapidly to maintain it. Then, the spectral intensity was used as a focus of feedback signal in optimized hill-climbing algorithm to drive the sample stage to move up and down to adjust the depth of the laser on the samples to realize automatic focusing. As a result, the hotspot can be maintained for 5 min, and the autofocusing result can be achieved within 9 s, while the sensitivity was improved with two orders of magnitude in D-SERS detection of crystal violet (CV) compared with manual focusing.

Quantification of the Real Plasmonic Field Transverse Distribution in a Nanocavity Using the Vibrational Stark Effect.

Quantifying the real plasmonic field strength experimentally has been long pursued in expanding the applications related to plasmonic enhancement. However, it is still an enormous challenge to determine the inhomogeneous plasmonic field distribution. Here, self-assembled monolayers (SAMs) of 4-mercaptobenzonitrile (MBN) are sandwiched as a gap spacer in a nanoparticle-on-mirror (NPoM) structure, effectively forming ultrahigh field enhancement to observe Stark shifts of the chemical bond. Transverse position-dependent Stark shifts of ν(C═C) and ν(C≡N) in the individual nanocavity measured by surface-enhanced Raman scattering (SERS) experiment combined with the Stark tuning rate by density functional theory (DFT) simulation accurately revealed the inhomogeneous plasmonic field transverse distribution and quantified the transverse plasmonic field strength up to ∼1.9 × 109 V/m, which matches the value predicted by finite element method (FEM) simulation. This work deepens the insight into plasmon-based technologies and will coordinate high-resolution techniques such as tip-enhanced Raman spectroscopy (TESR) to reveal the real plasmonic field distribution.

Multi-scale convolution neural network with residual modules for determination of drugs in human hair using surface-enhanced Raman spectroscopy with a gold nanorod film self-assembled by inverted evaporation.

Detection of illegal drug users is crucial in controlling drug-related crimes, reducing drug prevalence, and protecting human lives to ensure social stability. In this study, surface-enhanced Raman spectroscopy (SERS) and deep learning networks were employed to determine methamphetamine, ketamine, and morphine in human hair. Drugs were obtained from hair through alkaline hydrolysis and liquid-liquid extraction, and gold nanorods were employed to prepare the self-assembled film as the SERS substrate by inverted evaporation. The film showed good uniformity and excellent sensitivity, with a relative standard deviation of 15.6% and a detection limit of at least 10-10 M in the SERS detection of crystal violet. The spectra of methamphetamine, ketamine, and morphine at 0.05-1.0, 0.1-2.0, and 0.1-2.0 ng/mg were obtained, and the three drugs could be detected. Inception, a multi-scale feature extraction network, was combined with residual modules (Inception-ResNet) to develop the identification models of drugs, and the effect of spectral input form as a vector or matrix was explored. Inception-ResNet with input form of matrix outweighed other methods with 100.00%, 100.00%, and 99.23% accuracies in the training, validation, and prediction sets, respectively. In brief, SERS and Inception-ResNet with the spectra in matrix form provide an efficient and accurate determination of drugs in human hair, enabling the retrospective evaluation of drug use, and the method will be anticipated to detect excitant, poison, and toxic chemicals in human hair.

Insight into the Heterogeneity of Longitudinal Plasmonic Field in a Nanocavity Using an Intercalated Two-Dimensional Atomic Crystal Probe with a ∼7 ŠResolution.

Quantitative measurement of the plasmonic field distribution is of great significance for optimizing highly efficient optical nanodevices. However, the quantitative and precise measurement of the plasmonic field distribution is still an enormous challenge. In this work, we design a unique nanoruler with a ∼7 Šspatial resolution, which is based on a two-dimensional atomic crystal where the intercalated monolayer WS2 is a surface-enhanced Raman scattering (SERS) probe and four layers of MoS2 are a reference layer in a nanoparticle-on-mirror (NPoM) structure to quantitatively and directionally probe the longitudinal plasmonic field distribution at high permittivity by the quantitative SERS intensity of WS2 located in different layers. A subnanometer two-dimensional atomic crystal was used as a spacer layer to overcome the randomness of the molecular adsorption and Raman vibration direction. Combined with comprehensive theoretical derivation, numerical calculations, and spectroscopic measurements, it is shown that the longitudinal plasmonic field in an individual nanocavity is heterogeneously distributed with an unexpectedly large intensity gradient. We analyze the SERS enhancement factor on the horizontal component, which shows a great attenuation trend in the nanocavity and further provides precise insight into the horizontal component distribution of the longitudinal plasmonic field. We also provide a direct experimental verification that the longitudinal plasmonic field decays more slowly in high dielectric constant materials. These precise experimental insights into the plasmonic field using a two-dimensional atomic crystal itself as a Raman probe may propel understanding of the nanostructure optical response and applications based on the plasmonic field distribution.

The rapid SERS detection of succinylcholine chloride in human plasma is based on the high affinity between quaternary ammonium salt structures.

Succinylcholine chloride (SCC) is a common poison that threatens human life. At present, there is a lack of research on its on-site rapid detection methods. In this work, the use of gold nanorods as an enhanced substrate based on the high affinity between the quaternary ammonium salt structure can achieve rapid SERS detection of SCC in plasma. The long alkane chain structure of cetyltrimethylammonium bromide (CTAB) and the quaternary ammonium salt structure of SCC have a high molecular affinity, so that the target molecule can show a strong and obvious characteristic signal of SERS. Combined with a simple pretreatment method, acetonitrile is used as a protein precipitation agent to effectively remove matrix interference. The constructed SERS substrate can achieve the sensitive detection of 2 × 10-8 M level of SCC in plasma samples and has high detection reproducibility. The entire pre-processing and testing process can be completed within 7 min, which can be used as an important technical basis for the preliminary identification of on-site SCC-related drug cases. The research results provide an effective solution for the establishment of SCC analysis strategies in complex matrices, and can provide new ideas for solving the problems of difficult identification of common poisons in the field and the lack of rapid detection methods on site.

Field determination of hazardous chemicals in public security by using a hand-held Raman spectrometer and a deep architecture-search network.

With the advanced development of miniaturization and integration of instruments, Raman spectroscopy (RS) has demonstrated its great significance because of its non-invasive property and fingerprint identification ability, and extended its applications in public security, especially for hazardous chemicals. However, the fast and accurate RS analysis of hazardous chemicals in field test by non-professionals is still challenging due to the lack of an effective and timely spectral-based chemical-discriminating solution. In this study, a platform was developed for the field determination of hazardous chemicals in public security by using a hand-held Raman spectrometer and a deep architecture-search network (DASN) incorporated into a cloud server. With the Raman spectra of 300 chemicals, DASN stands out with identification accuracy of 100% and outweighs other machine learning and deep learning methods. The network feature maps for the spectra of methamphetamine and ketamine focus on the main peaks of 1001 and 652 cm-1, which indicates the powerful feature extraction capability of DASN. Its receiver operating characteristic (ROC) curve completely encloses the other models, and the area under the curve is up to 1, implying excellent robustness. With the well-built platform combining RS, DASN, and cloud server, one test process including Raman measurement and identification can be performed in tens of seconds. Hence, the developed platform is simple, fast, accurate, and could be considered as a promising tool for hazardous chemical identification in public security on the scene.

A novel SERS selective detection sensor for trace trinitrotoluene based on meisenheimer complex of monoethanolamine molecule.

Trinitrotoluene (TNT) is a primary component in chemical explosives, making them a common focus in public safety detection. However, it is very difficult to achieve selective and sensitive detection of the TNT molecule in practical application. In the present study, a simple surface enhanced Raman scattering (SERS) sensing based on monoethanolamine (MEA) - modified gold nanoparticles (Au NPs) was expanded for high selectivity and sensitive detecting of TNT in an envelope, luggage, lake water, and clothing through a quickly sampling and detection process. The monoethanolamine molecule based on Meisenheimer complex lights up ultra-high Raman scattering of a nonresonant molecule on the superficial coat of gold nanoparticles. Using this detection sensor, a molecular bridge can be established to selectively detect trinitrotoluene with a detection limit of 21.47 pM. We were able to rapidly identification trinitrotoluene molecule with a powerful selective over the familiar interfering substances nitrophenol, picric acid, 2,4-dinitrophenol, and 2,4-dinitrotoluene. The outcome in this work supply an efficient solution to the test of trinitrotoluene and to establishing a SERS sensor analytical strategy. The studies have demonstrated that the MEA-Au NPs based SERS sensing can be potentially used in field detection the trace amount of chemical explosives for public security.

Detection of Pirimiphos-Methyl in Wheat Using Surface-Enhanced Raman Spectroscopy and Chemometric Methods.

Pesticide residue detection is a hot issue in the quality and safety of agricultural grains. A novel method for accurate detection of pirimiphos-methyl residues in wheat was developed using surface-enhanced Raman spectroscopy (SERS) and chemometric methods. A simple pretreatment method was conducted to extract pirimiphos-methyl residue from wheat samples, and highly effective gold nanorods were prepared for SERS measurement. Raman peaks assignment was calculated using density functional theory. The Raman signal of pirimiphos-methyl can be detected when the concentrations of residue in wheat extraction solution and contaminated wheat is as low as 0.2 mg/L and 0.25 mg/L, respectively. Quantification of pirimiphos-methyl was performed by applying regression models developed by partial least squares regression, support vector machine regression and random forest with principal component analysis using different preprocessed methods. As for the contaminated wheat samples, the relative deviation between gas chromatography-mass spectrometry value and predicted value is in the range of 0.10%-6.63%, and predicted recovery is 94.12%-106.63%, ranging from 23.93 mg/L to 0.25 mg/L. Results demonstrated that the proposed SERS method is an effective and efficient analytical tool for detecting pirimiphos-methyl in wheat with high accuracy and excellent sensitivity.

Rapid Detection of Pesticide Residues in Paddy Water Using Surface-Enhanced Raman Spectroscopy.

Pesticide residue in paddy water is one of the main factors affecting the quality and safety of rice, however, the negative effect of this residue can be effectively prevented and reduced through early detection. This study developed a rapid detection method for fonofos, phosmet, and sulfoxaflor in paddy water through chemometric methods and surface-enhanced Raman spectroscopy (SERS). Residue from paddy water samples was directly used for SERS measurement. The obtained spectra from the SERS can detect 0.5 mg/L fonofos, 0.25 mg/L phosmet, and 1 mg/L sulfoxaflor through the appearance of major characteristic peaks. Then, we used chemometric methods to develop models for the intelligent analysis of pesticides, alongside the SERS spectra. The classification models developed by K-nearest neighbor identified all of the samples, with an accuracy of 100%. For the quantitative analysis, the partial least squares regression models obtained the best predicted performance for fonofos and sulfoxaflor, and the support vector machine model provided optimal results, with a root-mean-square error of validation of 0.207 and a coefficient of determination of validation of 0.99952, for phosmet. Experiments for actual contaminated samples also showed that the above models predicted the pesticide residue values with high accuracy. Overall, using SERS with chemometric methods provided a simple and convenient approach for the detection of pesticide residues in paddy water.

Quantitative Determination of Chlormequat Chloride Residue in Wheat Using Surface-Enhanced Raman Spectroscopy.

A simple and sensitive method for detection of chlormequat chloride residue in wheat was developed using surface-enhanced Raman spectroscopy (SERS) coupled with chemometric methods on a portable Raman spectrometer. Pretreatment of wheat samples was performed using a two-step extraction procedure. Effective and uniform active substrate (gold nanorods) was prepared and mixed with the sample extraction solution for SERS measurement. The limit of detection for chlormequat chloride in wheat extracting solutions and wheat samples was 0.25 mg/L and 0.25 µg/g, which was far below the maximum residual value in wheat of China. Then, support vector regression (SVR) and kernel principal component analysis (KPCA), multiple linear regression, and partial least squares regression were employed to develop the regression models for quantitative analysis of chlormequat chloride residue with spectra around the characteristic peaks at 666, 713, and 853 cm-1. As for the residue in wheat, the predicted recovery of established optimal model was in the range of 94.7% to 104.6%, and the standard deviation was about 0.007 mg/L to 0.066 mg/L. The results demonstrated that SERS, SVR, and KPCA can provide the accurate and quantitative determination for chlormequat chloride residue in wheat.

Fast detection of fenthion on fruit and vegetable peel using dynamic surface-enhanced Raman spectroscopy and random forests with variable selection.

Dynamic surface-enhanced Raman spectroscopy (D-SERS) based on the state change of the substrate not only significantly enhances but also provides a highly reproducible Raman signal. Hence, we develop a fast and accurate method for the detection of fenthion on fruit and vegetable peel using D-SERS and random forests (RF) with variable selection. With uniform Ag nanoparticles, the dynamic spectra of fenthion solution at different concentrations were obtained using D-SERS, and fenthion solution greater than or equal to 0.05mg/L can be detected. Then, the quantitative analysis models of fenthion were developed by RF with variable selection for spectra of different range. The model of best performance is developed by RF and spectra of characteristic range with higher RF importance (top 40%), and the root mean square error of cross-validation is 0.0101mg/L. Moreover, the fenthion residue of tomato, pear, and cabbage peel were extracted by a swab dipped in ethanol and analyzed using the above method to further validate the practical effect. Compared to gas chromatography, the maximal relative deviation is below 12.5%, and the predicted recovery is between 87.5% and 112.5%. Accordingly, D-SERS and RF with variable selection can realize the fast, simple, ultrasensitive, and accurate analysis of fenthion residue on fruit and vegetable peel.

Fast and Quantitative Analysis of Ediphenphos Residue in Rice Using Surface-Enhanced Raman Spectroscopy.

Detection of residual farm chemicals in agricultural crops is a hot topic in the field of food safety. In this study, ediphenphos residue in rice was detected using surface-enhanced Raman spectroscopy (SERS) on a portable Raman spectrometer. A simple pretreatment method for rice samples was developed, and uniform gold nanorods were used for SERS measurement. Characteristic signals can still be detected when ediphenphos concentration in rice extraction solution was higher than or equal to 0.1 mg/L. Quantitative analysis of ediphenphos was conducted by regression models developed using partial least-squares regression, random forest and kernel principal component analysis, and root-mean-square error of cross validation, coefficient of determination and relative predicted deviation of optimal model were 0.022 mg/L, 0.9967 and 297.45, which indicated the proposed method can predict ediphenphos concentration with high precision. To validate the feasibility of practical application further, rice samples spiked with 10, 5, 1, 0.5, and 0.1 µg/g ediphenphos residue were analyzed using the above method. The predicted recovery was in the range of 93.4% to 102%, and the predicted error was small for residue of each concentration. These results demonstrated that the presented method could be used for accurate and quantitative detection of ediphenphos residue in rice. PRACTICAL APPLICATION: This study developed a surface-enhanced Raman spectroscopy (SERS) method for detection of ediphenphos in rice coupled with simple extraction protocol and gold nanorods on a portable Raman spectrometer. SERS is a rapid and accurate method which can be applied in agricultural grain safety inspection.

Dynamic surface-enhanced Raman spectroscopy and Chemometric methods for fast detection and intelligent identification of methamphetamine and 3, 4-Methylenedioxy methamphetamine in human urine.

Conventional Surface-Enhanced Raman Spectroscopy (SERS) for fast detection of drugs in urine on the portable Raman spectrometer remains challenges because of low sensitivity and unreliable Raman signal, and spectra process with manual intervention. Here, we develop a novel detection method of drugs in urine using chemometric methods and dynamic SERS (D-SERS) with mPEG-SH coated gold nanorods (GNRs). D-SERS combined with the uniform GNRs can obtain giant enhancement, and the signal is also of high reproducibility. On the basis of the above advantages, we obtained the spectra of urine, urine with methamphetamine (MAMP), urine with 3, 4-Methylenedioxy Methamphetamine (MDMA) using D-SERS. Simultaneously, some chemometric methods were introduced for the intelligent and automatic analysis of spectra. Firstly, the spectra at the critical state were selected through using K-means. Then, the spectra were proposed by random forest (RF) with feature selection and principal component analysis (PCA) to develop the recognition model. And the identification accuracy of model were 100%, 98.7% and 96.7%, respectively. To validate the effect in practical issue further, the drug abusers'urine samples with 0.4, 3, 30ppm MAMP were detected using D-SERS and identified by the classification model. The high recognition accuracy of >92.0% can meet the demand of practical application. Additionally, the parameter optimization of RF classification model was simple. Compared with the general laboratory method, the detection process of urine's spectra using D-SERS only need 2 mins and 2µL samples volume, and the identification of spectra based on chemometric methods can be finish in seconds. It is verified that the proposed approach can provide the accurate, convenient and rapid detection of drugs in urine.

Detection and direct readout of drugs in human urine using dynamic surface-enhanced Raman spectroscopy and support vector machines.

A new, novel, rapid method to detect and direct readout of drugs in human urine has been developed using dynamic surface-enhanced Raman spectroscopy (D-SERS) with portable Raman spectrometer on gold nanorods (GNRs) and a classification algorithm called support vector machines (SVM). The high-performance GNRs can generate gigantic enhancement and the SERS signals obtained using D-SERS on it have high reproducibility. On the basis of this feature of D-SERS, we have obtained SERS spectra of urine and urine containing methamphetamine (MAMP). SVM model was built using these data for fast identified and visual results. This general method was successfully applied to the detection of 3, 4-methylenedioxy methamphetamine (MDMA) in human urine. To verify the accuracy of the model, drug addicts' urine containing MAMP were detected and identified correctly and rapidly with accuracy more than 90%. The detection results were displayed directly without analysis of their SERS spectra manually. Compared with the conventional method in lab, the method only needs a 2 µL sample volume and takes no more than 2 min on the portable Raman spectrometer. It is anticipated that this method will enable rapid, convenient detection of drugs on site for the police.

Polystyrene/Ag nanoparticles as dynamic surface-enhanced Raman spectroscopy substrates for sensitive detection of organophosphorus pesticides.

We report the use of Polystyrene/Ag (PS/Ag) nanoparticles as dynamic surface-enhanced Raman spectroscopy (dynamic-SERS) substrates for sensitive detection of low levels of organophosphorus pesticides. The PS particles clearly observed using Raman microscopy provide the masterplate for in situ growth of Ag NPs, leading to multiple active sites for SERS measurements. Besides obtaining the fingerprints of target molecules and recording time-resolved Raman spectra, this dynamic-SERS method can be used as an ultra-sensitive analytical technique which can enhance 1-2 orders of magnitude the signals of analytes in comparison to that of the traditional methods. On the other hand, importantly, it shows much better correlations between concentration and intensity than does the conventional SERS technique so that it can build the foundation for quantitative analysis of analytes. The as-prepared individual PS/Ag nanoparticle has been demonstrated for the sensitive detection of organophosphorus paraoxon and sumithion. SERS spectra are acquired at different concentrations of each pesticide and linear calibration curves are obtained by monitoring the strongest intensity value of bands arising from stronger stretching mode as a function of analyte concentration. The limits of detection and limits of quantitation are reported for two pesticides. The limit of detection for paraoxon is 96 nM (0.026 ppm) and for sumithion is 34 nM (0.011 ppm). The limits of quantitation are 152 nM (0.042 ppm) and 57 nM (0.016 ppm) for paraoxon and sumithion, respectively. It can be seen that these two organophosphorus pesticides can be detected in the low nM range based on this dynamic-SERS analytical method. Also, in the real sample experiments of paraoxon and sumithion, the results confirm that this dynamic-SERS technique would have potential applicability for quantitative analysis with slight interference.