Good Detection Research Articles

Bifunctional SnO2/NiO/rGO nanocomposite electrode for hydrogen evolution reaction and detection of winter wheat cold-resistant RNA.

Aconvenient, non-toxic, and low-cost tin dioxide (SnO2)/nickel oxide (NiO)/reduced graphene oxide (rGO) nanocatalytic electrode was investigated, which can effectively promote the hydrogen evolution reaction and can also be used to construct a sensitive winter wheat cold-tolerant RNA sensor. Due to the good synergy and photocurrent generation between SnO2 and NiO, which accelerates the electron transfer and catalyzes the hydrolysis reaction, the resultant data for the hydrogen evolution reaction in alkaline environment of this material are very impressive, with cathodic current densities of up to 10mAcm-2. The marvelous heterostructures and photovoltaic properties form a signal amplification system, which enables the nanocomposites to have an excellent linear sensitivity in low-concentration RNA solutions. The single-stranded complementary RNA of the target RNA was immobilized on the surface of the nanocomposites, which enabled the materials to recognize the target RNA under enzyme-free conditions. The test results showed that the modified electrode materials had good single detection performance in a wide linear range from 0.1nM to 2mM, with the limit of detection (LOD) of 0.078nM. The developed nanocomposite electrode has good performance in hydrogen evolution and winter wheat cold-resistant RNA detection, and is a bifunctional device with superior performance.

A portable fluorescence sensing system for timely onsite perfluorooctane sulfonate detection based on an aggregate induced emission fluorescence sensor.

Perfluorooctane sulfonate (PFOS), a ubiquitous persistent organic pollutant, has aroused growing concern due to its adverse effects on human health. Timely onsite monitoring of PFOS in heavily contaminated areas is crucial for effective pollution management and prevention of its spread. However, relevant PFOS detection methods have rarely been reported. Herein, we developed a fluorescence sensing system capable of achieving timely onsite detection of PFOS under outdoor conditions. First, aggregate induced emission (AIE) fluorescence sensors, TPE-PAs, were synthesized. The optimized sensor could selectively interact with PFOS through electrostatic attraction and hydrogen bonding and exhibited prominent fluorescence enhancement after treating with PFOS. There was a good linear relationship between the fluorescence enhancement and PFOS concentration in the range of 0.05-30 ppm, and the limit of detection was measured to be 0.047 ppm. In addition, owing to the AIE fluorescence mechanism and high concentration of TPE-PAs in the sensing medium, the sensor demonstrated excellent anti-interference performance. Second, we developed a portable fluorometer, by modifying the power supply and sample cell of a tiny fluorometer, and further integrated this modified fluorometer, the prepared fluorescence sensor, standard PFOS solutions and other consumables into a portable test system. This test system showed good detection accuracy and reliability and successfully achieved timely onsite PFOS detection in real water samples.

Smartphone motion sensor based detection of atrial fibrillation in populations referred to cardiology

Abstract Introduction The motion sensors of a common smartphone when placed on chest can be used to detect vibrations caused by cardiac motion. The concept has been shown to detect atrial fibrillation (AFib) in clinical setting with 96% accuracy. Purpose We sought to examine how the smartphone motion sensor based AFib detection performs in various populations referred to cardiology pooled from 3 different studies addressing arrhythmias, heart failure or suspected coronary artery disease. Methods From a total of 1 524 patient recordings 748 recordings were from men (49%). The mean age of the patients was 64 years (range 20-94 years) and mean BMI was 28.9kg/m2 (range 15.2-63.7kg/m2). The signals were collected by placing a smartphone on chest of the patients in supine position and then analyzed with the CardioSignal solution. From the recordings two 60 second strips were analyzed. A simultaneous 5-lead or 1-lead ECG data was collected for reference. The ECG signals were analyzed for quality and rhythm diagnostics by 2 physicians blinded for the motion sensor AFib detection result. Finally, a head-to-head comparison was conducted for the motion sensor AFib detection result and the ECG data. Results From the 1524 patient recordings the final analysis set included 237 individuals with AFib, whereas the rest had sinus rhythm. The sensitivity to detect AFib was 91.9% and specificity 98.7%. The positive predictive value was 91.0% (88.3% to 94.2%) and the negative predictive value 98.3% (95.2% to 99.1%). The accuracy was 97.0% (94.7% to 97.7%). Conclusions A smartphone-based assessment of AFib using the embedded motion sensors seem to yield very good detection results, especially specificity. The accuracy of the results is comparable to available ECG and PPG based AFib detection solutions.

A Multi-Tiered Collaborative Network for Optical Remote Sensing Fine-Grained Ship Detection in Foggy Conditions

Ship target detection faces the challenges of complex and changing environments combined with the varied characteristics of ship targets. In practical applications, the complexity of meteorological conditions, uncertainty of lighting, and the diversity of ship target characteristics can affect the accuracy and efficiency of ship target detection algorithms. Most existing target detection methods perform well in conditions of a general scenario but underperform in complex conditions. In this study, a collaborative network for target detection under foggy weather conditions is proposed, aiming to achieve improved accuracy while satisfying the need for real-time detection. First, a collaborative block was designed and SCConv and PCA modules were introduced to enhance the detection of low-quality images. Second, the PAN + FPN structure was adopted to take full advantage of its lightweight and efficient features. Finally, four detection heads were used to enhance the performance. In addition to this, a dataset for foggy ship detection was constructed based on ShipRSImageNet, and the mAP on the dataset reached 48.7%. The detection speed reached 33.3 frames per second (FPS), which is ultimately comparable to YOLOF. It shows that the model proposed has good detection effectiveness for remote sensing ship images during low-contrast foggy days.

Investigation of a piezoelectric ultrasonic surface wave transducer that minimizes structural scattering

Abstract The Structural of ultrasonic surface wave transducers affects structural scattering. A numerical model of an ultrasonic surface wave transducer was established. Numerical simulations and dynamic acoustic analysis were performed to systematically assess, the effects of the transducer parameters, such as the front and back angles, matching layer, and front wedge shape on structural scattering. The results show that the acoustic amplitude of the transducer structure scattered waves is basically stable when the back angle of the wedge is 64° -68 °and the front angle is 65°-85°. The amplitude obtained from a transducer with orthogonal slots and a front uniform matching layer is 61% lower for the primary structure scattering, 56% lower for the secondary structure scattering.
When equipped with the upper matching layer, the amplitude of the secondary structural scattering echo is reduced by 78.2%. The experimental results show that the transducer with orthogonal slots on the wedge and a matching layer at the front and upper edges has a signal-to-noise ratio of 19.6dB when detecting the echo signal of a ϕ8 through-hole in a welded steel plate, indicating good detection capability. The experiment validates the numerical simulation results. The proposed transducer can be used for structural monitoring of welded structures and detecting flaws.

Background-Free Imaging of Food Freshness Using Curcumin-Functionalized Upconversion Reversible Hydrogel Patch.

Functionalized upconversion nanomaterials can overcome the drawbacks faced of strong background interference, photodamage, and spectral overlap by conventional optical labeling. Here, curcumin-functionalized upconversion hydrogel patch is designed with background-free and reversible for food freshness monitoring by ultra-sensitive response to biogenic amines. By loading the probes onto hydrogel patch, utilizing the good ductility to solve the problem of non-smooth surface coverage, thus accurately capturing biogenic amines. The presence of biogenic amines leads to the conversion of the diketone group on the probe to enolate ions, which triggers fluorescence resonance energy transfer (FRET) and ultimately causes the upconverted fluorescence to gradually change from green to red. The probe exhibits good detection capability for biogenic amines with a low limit of detection (LOD) of 2.73 µm. Interestingly, the patch can be restored to its initial state after water rinsing, realizing reversible detection of biogenic amines. Additionally, combining the color recognition system of smartphone can convert the imaging signal into a data signal to achieve quantitative analysis and show a reliable assessment comparable to the results of high performance liquid chromatography (HPLC). This study demonstrates the practical applicability in real-time monitoring of freshness, suggests great potential in developing optical nano-sensing strategy to ensure food safety.

Fabrication of polyoxometalate dispersed cobalt oxide nanowires for electrochemically monitoring superoxide radicals from Hela cell mitochondria

An ultrasensitive electrochemical sensor is constructed by electrostatically adsorbing negatively charged hourglass-shape Cu-Polyoxometalate (POM) onto a positively charged CoO nanowires modified carbon cloth. The petaloid CoO nanowires have a large specific surface area that can well disperse open-structured Cu-POM to form Cu-POM@CoONWs@CC, which can maximumly expose catalytic active centers (Co2+ and Cu2+) and accelerate mass/charge transfer. In addition to the above advantages, the excellent electron exchange ability of Cu-POM and good conductivity of CoONWs@CC endow the sensor with good detection capability to H2O2 including a linear detection range of 0.05–1.4 μA μM−1, a low detection limit of 0.022 μM, high sensitivity of 110.48 μA μM−1, good selectivity and long-term stability. Due to the fast transformation of superoxide anion (O2∙‐) to H2O2, the sensor can indirectly monitor the electron leakage resulting in the formation of O2∙‐ via detecting H2O2. Afterwards, Hela cell mitochondria were extracted from the living cells that cultured with different mitochondrial inhibitors and the release of O2∙‐ from the corresponding mitochondrial complexes was monitored by the sensor. Through comparing the current signals, we determined that complex I is probably the main electron leakage site. This work could provide meaningful information for the diagnosis of certain oxidative stress diseases.

Road object detection method based on improved YOLOv5 algorithm

Aiming at the existing network's poor recognition of distant targets in road traffic scenes, insufficient expression of target features, and inaccurate target positioning, a road target detection method based on the improved YOLOv5 algorithm is proposed. Firstly, the feature extraction structure of the YOLOv5 algorithm is summarized, and the shortcomings of the original network structure are analyzed; secondly, a small target detection layer is added on the basis of the original network, and the recognition ability of the network for distant targets is improved by supplementing the fusion feature layer and introducing an additional detection head; thirdly, the original detection head is decoupled, and the expression ability of the network for target features is improved by changing the bounding box regression and target classification process into two branches; then, the prior frame is re-clustered, and the height-width ratio of the prior frame is adjusted by the K-means++ algorithm to enhance the network's positioning ability for the target; finally, ablation, comparison and visualization verification experiments are carried out with AP, mAP and FPS as evaluation indicators. The results show that the detection speed of the proposed algorithm on the BDD100K dataset is 95.2 frames/second, and the average accuracy reaches 55.6%, which is higher than that of the YOLOv5 algorithm 6.7%. It can be seen that the improved YOLOv5 algorithm has good target detection accuracy while meeting the real-time requirements of detection. It is suitable for road target detection tasks in complex traffic environments and has guiding significance for improving the visual perception ability of autonomous vehicles.

Leveraging Classifier Performance Using Heuristic Optimization for Detecting Cardiovascular Disease from PPG Signals.

Photoplethysmography (PPG) signals, which measure blood volume changes through light absorption, are increasingly used for non-invasive cardiovascular disease (CVD) detection. Analyzing PPG signals can help identify irregular heart patterns and other indicators of CVD. This research involves a total of 41 subjects sourced from the CapnoBase database, consisting of 21 normal subjects and 20 CVD cases. In the initial stage, heuristic optimization algorithms, such as ABC-PSO, the Cuckoo Search algorithm (CSA), and the Dragonfly algorithm (DFA), were applied to reduce the dimension of the PPG data. Next, these Dimensionally Reduced (DR) PPG data are then fed into various classifiers such as Linear Regression (LR), Linear Regression with Bayesian Linear Discriminant Classifier (LR-BLDC), K-Nearest Neighbors (KNN), PCA-Firefly, Linear Discriminant Analysis (LDA), Kernel LDA (KLDA), Probabilistic LDA (ProbLDA), SVM-Linear, SVM-Polynomial, and SVM-RBF, to identify CVD. Classifier performance is evaluated using Accuracy, Kappa, MCC, F1 Score, Good Detection Rate (GDR), Error rate, and Jaccard Index (JI). The SVM-RBF classifier for ABC PSO dimensionality reduced values outperforms other classifiers, achieving the highest accuracy of 95.12% along with the minimum error rate of 4.88%. In addition to that, it provides an MCC and kappa value of 0.90, a GDR and F1 score of 95%, and a Jaccard Index of 90.48%. This study demonstrated that heuristic-based optimization and machine learning classification of PPG signals are highly effective for the non-invasive detection of cardiovascular disease.

Development of a multi-layer high-efficiency GEM-based neutron detector for spallation sources

Neutron detection is nowadays mostly based on 3He gas detectors, but its shortage and the continuous upgrades of the neutron facilities require new devices to perform experiments with maximum performances. This work presents a new detector based on the Gas Electron Multiplier (GEM) combined with several boron layers. This detector combines the features of GEM technology with the properties of boron as a neutron converter and the device is produced to sustain high neutron fluxes with high detection efficiency. The detector has been characterised at the ISIS Pulsed Neutron and Muon Source (UK). Based on the analysis of our results, the detector has shown a good response to thermal and epithermal neutrons reaching a detection efficiency of 16% at 1.8 Å (25 meV). The good detection efficiency (even increasable with the addition of further boron GEM foils) and the good time resolution, make the detector a unique device for the neutron techniques. In particular, its use can easily be envisaged in techniques involving neutron transmission measurements, that require high fluxes impinging on the detectors, with the added bonus of a 2D-resolved capability due to the padded anode.

Development of CadR-based cadmium whole cell biosensor for visual detection of environmental Cd2+

BackgroundAs a threat to human health and public health, cadmium (Cd) pollution has received widespread social concern. Our previously constructed CadR-based bacterial whole cell biosensor (WCB) epCadR5 showed high sensitivity and specificity in cadmium detection. However, the application of the sensor is still hindered by the need for laboratory equipment to read the fluorescence signal output. In this study, we aimed to optimizing the sensor to make it available for visual detection of environmental cadmium and simplify the detection process to advance practical application of the sensor. ResultsBy replacing the constitutive promoter with J110, the fluorescence signal output of the sensor was significantly increased and the fluorescence leakage was decreased. In addition, the fluorescence signal output of green fluorescence protein (GFP) was enhanced by the addition of a 5′ untranslated region (5′-UTR) mlcR10. The fluorescence signal output of the WCB is sufficiently robust to be visible and distinguishable to the naked eye, which is of paramount importance for visual detection. The sensor readout can be conveniently recorded by mobile phone camera and quantified. For ease of on-site application, the WCB's visual detection procedures and conditions were further optimized and simplified. The WCB demonstrated good linearity and detection limit (1.81 μg/L) for visual detection of Cd2+ without the assistance of bulky laboratory equipment. For the detection of real environmental samples, the WCB visual detection results were close to those of WCB-flow cytometry (FACS) and graphite furnace atomic absorption spectroscopy (GFAAS). SignificanceIn this work, we developed an easy-to-use, on-site and visual detection biosensor for monitoring environmental Cd2+. It will advance the utilization of cadmium WCBs in practical settings. The optimization and simplification strategy in the study also provide new insights into the visualization of other bacterial biosensors, and will advance the practical application of WCBs.

Nanoclay composites in electrochemical sensors

Nanoclays are layered structures in the nanoscale range with widespread application due to their unique properties such as swelling, cation exchange capacity, and ease of functionalisation using metals, metal oxides, and organic compounds such as carbon paste, polymers, and other biomolecules that form nanoclay composites. Nanoclay functionalisation with silver (Ag), zinc oxide (ZnO), and bimetallic silver–gold (Ag–Au) using hydrophilic and hydrophobic clays is here evaluated and discussed. The composites’ synthesis and morphological, crystallinity, and electroactive properties in comparison with pure nanoclay are also assessed. The layered structure and crystallinity of all these nanoclay composites were slightly changed. The clumped layered structures on the surface of the nanocomposites had dispersed white spots that indicated possible surface modification. The nano-films of the composites’ electroactivity were comparatively high, as seen from the increase in current in the cyclic voltammetry characterisation voltammograms and the differential pulse voltammograms of the pharmaceutical detection. Efavirenz, nevirapine, and zidovudine detection was improved by modification of the nanocomposite with human serum albumin (HSA), as shown by the higher current, thus indicating improved conductivity of the composites compared to the pure nanoclays. Applying HAS-modified nanocomposites in the analysis of efavirenz, nevirapine, and zidovudine on a glassy carbon electrode (GCE) showed good linearity and acceptable detection limits comparable to those of previous studies. Therefore, it has potential for application in pharmaceutical quality control and environmental monitoring.

Composite Material from Waste ABS 3D Filaments and Graphite: A Cost‐Effective and Sustainable Alternative for Electrochemical Sensor Manufacturing and Voltammetric Analysis of Acebutolol

AbstractAdditive manufacturing technology is renowned for its ability to produce components on demand. Presently, the development of sustainable materials has been promoted by Green Chemistry, leading to an increase in the recycling of polymers and other materials for use in 3D printing. In this work, we proposed the construction of a 3D printed electrochemical device using a conductive paste made from 3D printing residues of acrylonitrile butadiene styrene (ABS) filaments and graphite to develop a sustainable sensor for acebutolol detection using square wave voltammetry method. The electrode was characterized using imaging and electrochemical techniques, demonstrating superior electrochemical performance compared to a commercial device. The method exhibited excellent analytical performance with a linear working range from 0.98 to 51.8 μmol L−1 and good detectability (detection limit) of 0.28 μmol L−1. Furthermore, the recycled sensor demonstrated good precision with a relative standard deviation of less than 8.4 % and good accuracy with recovery percentages ranging from 93 to 107 % for monitoring acebutolol in urine and tap water samples. Additionally, the device was free from interference from the matrix and other commonly used drugs, making it suitable for electroanalytical applications and extremely promising.

Electrochemical biosensor based on copper sulfide/reduced graphene oxide/glucose oxidase construct for glucose detection

Due to the current increase in the number of people suffering from diabetes worldwide, how to monitor the blood glucose level in the human body has become an urgent problem to be solved nowadays. The electrochemical sensor method can be used for real-time glucose monitoring due to its advantages of real-time monitoring capability and high sensitivity. Reduced graphene oxide (rGO) has great potential for application in the field of sensors due to its advantages of large specific surface area, high stability, and good electrical and thermal conductivity. Meanwhile, the synergistic effect between two-dimensional transition metal sulfides and graphene can improve the electrochemical performance of materials due to their similar mechanical flexibility and strength. This article uses flake graphite, copper sulfate, and glucose oxidase (GOx) as raw materials to prepare CuS/rGO/GOx/GCE electrodes, and explores the performance of electrode electrocatalysis for glucose. The results showed that the prepared sensor was characterized by a low detection limit (1.75 nM) and a wide linear range (0.1-100 mM) for glucose detection, displaying a good overall detection performance, and its sensing mechanism and dynamic process were also investigated. In addition, the sensor has outstanding selectivity, anti-interference, repeatability, reproducibility and practicality.

Performance of large-scale [formula omitted]Li-doped pulse-shape discriminating plastic scintillators

A 6Li-doped plastic scintillator with pulse-shape discrimination capabilities, commercially identified as EJ-299-50, has been developed and produced at the kilogram-scale. A total of 44 kg-scale bars of dimensions 5.5 cm x 5.5 cm x 50 cm of this material have been characterized. Optical properties like light output and effective attenuation length have been found to be comparable to 6Li-doped liquid scintillators. The scintillator EJ-299-50 shows good neutron detection capabilities with an effective efficiency for capture on 6Li of approximately 85%. Stability tests performed on two formulation variations showed no intrinsic degradation in the material or optical properties during several months of observations.

The Development of Remotely Operated Underwater Vehicle for Plastic Waste Detection with Raspberry Pi

Abstract Underwater Plastic waste has had a serious impact on the environment. Unfortunately, Detecting and collecting underwater plastic waste is still a challenge, especially in Indonesia because of its position in the water, and the water turbidity. In previous research, a computer algorithm for detecting underwater plastic waste has been developed with Yolov3. To improve the research, we developed a Remotely Operated Underwater Vehicle (ROUV) with Raspberry Pi through the Research and Development (R&D) method. This ROUV is like a submarine that operates in the water and is integrated with a camera and computer algorithm to detect plastic waste. To get close to the real condition, we created an artificial water environment in a pond with various turbidity (from 20 to 200 Nephelometric Turbidity Unit). We use 30 pieces of plastic waste in several colors: white, red, black, and transparent. The plastic waste dipped in the water and was then detected by the ROUV camera. The result shows that the effective threshold for object detection is around 100 Nephelometric Turbidity Unit (NTU). Below 100 NTU, although the number of detected plastic images decreases as the turbidity increases, the visibility and contrast still allow for reasonably good object detection. The average confidence value for detection at turbidity levels below 100 NTU is 77%. At high turbidity conditions, the detection model may require adjustment or retraining with data that reflects those turbidity conditions.

Label-Free Detection of Programmed Death-Ligand 1 for Prognosis Using a Truncated Aptamer and SYBR Green I.

The rapid and accurate detection of programmed death-ligand 1 (PD-L1) expression is of great value in the diagnosis and treatment of tumors. ELISA-based traditional method is the gold standard for protein detection, but there are still some shortcomings, especially the antigen-antibody dependence, greatly increased the detection time and cost. This work constructed a label-free fluorescent probe for rapid and sensitive detection of PD-L1 using a truncated aptamer as recognition molecules and double-stranded DNA specific dyes (SYBR Green I) as signal units. After a series of optimization conditions, this probe has good detection capability for PD-L1 in buffer solution with the detection limit as low as 0.68 ng/mL. Due to the specific recognition ability of aptamer and target, this method also has good selectivity for PD-L1 detection. The recovery of PD-L1 in human serum samples ranges from 86.20 to 96.36%. Compared with other methods, this strategy does not need to be marked, and does not need other complex design and purification process, but simple operation process and strong anti-interference ability. The whole detection process can be completed within 20min and has good application prospect. This work will provide reference for drug dosage and prognosis evaluation of specific tumor therapy.

Persistent luminescent nanoparticles with enhanced afterglow through polydopamine modification for separation-free and visual detection of iron ions in water

Fluorescence analysis is one of the most popular methods in metal ions detection, but the background interference is an unavoidable obstacle in the fluorescence analysis of complex samples. In this work, a persistent luminescence nanoprobe was designed to detect iron ions in real samples by avoiding background interference. First, the Sr4Al14O25:Eu2+, Dy3+ persistent luminescent nanoparticles (PLNPs) were prepared by solid-state reaction protocols and polydopamine (PDA) encapsulation. After modification, these PLNPs@PDA showed better afterglow performance. More importantly, the PLNPs@PDA are highly selective toward iron ions, and could serve as an afterglow “turn off” nanoprobe for selective detection of iron ions through a dynamic quenching mechanism, with a detection limit of 2.86 μM. To further realize rapid and on-site detection, a test strip based on PLNPs@PDA was prepared and applied, which also showed good detection performance in real water samples. Compared with other detection methods, this afterglow imaging detection does not depend on expensive instruments, and can effectively shield the interference of background fluorescence from complex water samples, which has a good application prospects in the actual water sample. This work opens a new avenue for developing afterglow probe for the detection of metal ion in water samples without background interference.

Detection of walnut internal quality via improved YOLOv5 and x‐ray imaging

AbstractThis study proposes a walnut target detection algorithm based on improved YOLOv5 and x‐ray imaging to meet the demand for internal quality detection and removal in the Xinjiang walnut industry. By replacing the C3 module in the backbone layer with the C2f module and the couple‐head in the head layer with the decouple‐head, the algorithm reduces computational complexity, enhances robustness and generalizability, and retains more spatial information, thereby improving the performance of multicategory small target detection. In addition, this paper replaces the original CIOU loss function with the EIOU loss function to improve the convergence speed of the algorithm's accuracy and boundary aspect ratio. Compared with the original model, the improved model, improved YOLOv5, maintains the same average precision for normal walnuts while increasing the average precision for shriveled walnuts and empty‐shell walnuts by 8.2% and 0.4%, respectively. Compared with other mainstream models, such as VGG16, ResNet50, YOLOv5s, YOLOv7, YOLOv8s, YOLOv9s, and YOLOv10s, this model achieves the highest detection accuracy and good detection performance, with a single‐image detection time of 11.9 ms, meeting the requirements for real‐time detection. This work lays a foundation for automatic robot detection of the internal quality and removal of walnuts, showing practical application potential.Practical applicationsXinjiang stands as a prominent producer of walnuts; however, due to the decentralized nature of its cultivation and management processes, a notable prevalence of internal empty shells and shrinkage is observed, which substantially diminishes their commercial value. The integration of YOLOv5 with x‐ray imaging technology promises to enhance the precision of internal quality assessments of walnuts. The improved YOLOv5 model, as delineated in this study, exhibits the highest detection accuracy when benchmarked against a multitude of other models. It achieves a detection latency of merely 11.9 ms per image, thereby satisfying the stringent demands for real‐time detection applications. This model is designed for integration into x‐ray systems with an adjunctive sorting mechanism, which facilitates the inspection and exclusion of defective walnuts on conveyor belts.

An Effective and Controllable Platform with Ag-Stepped Nanocone Arrays Serving as Stable SERS Substrate

Facile, controllable and efficient SERS substrate preparation has always been the focus project in the SERS field, especially the solid-state nanoarray substrate. Herein, a simple and convenient method for preparing a series of homogeneous Ag-stepped nanocone arrays (Ag-SNCAs) using polymer nanocompression printing is presented. By analyzing the 1162[Formula: see text]cm[Formula: see text] characteristic peak of crystal violet (CV), the relative standard deviation (RSD) of the Raman intensity was only 4.71%. The detection limit of malachite green (MG) reached 0.001[Formula: see text]ppm, and it also has good SERS detection results for drug molecules such as melamine, methadone, morphine, methamphetamine and thiabendazole. This work provides a large area uniform and controllable preparation method of sharp cone nanostructure array, which has important application in plasmonic photocatalysis and deep light field regulations.