Facile coupling of plasmonic Au-NPs on ZnS NFs as a robust SERS substrate for toluidine blue detection and degradation.

BACKGROUND: The robustness and sensitivity of the surface-enhanced Raman spectroscopy (SERS) technique heavily relies on the development of SERS active materials. A hybrid of semiconductor and plasmonic metals is highly effective as a SERS substrate, which enables the trace level detection of various organic pollutants. RESULTS: This approach demonstrates the photodeposition of plasmonic gold nanoparticles (Au-NPs) on the surface of semiconductor-zinc sulfide nanoflowers (ZnS NFs), grown via the hydrothermal route. The synergistic contribution of the charge-transfer phenomenon and localized surface plasmon resonance of the Au-NPs/ZnS NFs makes it an ideal SERS substrate for the detection of organic pollutants, toluidine blue (TB). The proposed material has a high SERS enhancement factor (109), low limit of detection (10-11 M), good reproducibility, selectivity and strong anti-interference ability. Furthermore, the practicability of the Au-NPs/ZnS NFs is explored in real-time water samples, which are obtained with the satisfactory recovery rates. Additionally, the UVC light illumination on the Au-NPs/ZnS NFs has efficiently degraded TB within a time period of 150 min. SIGNIFICANCE AND NOVELTY: These finding demonstrate the significance of the proposed Au-NPs/ZnS NFs for SERS based detection and degradation of organic pollutants in real-time samples, highlighting their potential in monitoring and treating water pollutants in wastewater.

Improved deep neural network (EnhanceNet) for real-time detection of some publicly prohibited items.

Public safety is a critical concern, typically addressed through security checks at entrances of public places, involving trained officers or X-ray scanning machines to detect prohibited items. However, many places like hospitals, schools, and event centres lack such resources, risking security breaches. Even with X-ray scanners or manual checks, gaps can be exploited by individuals with malicious intent, posing significant security risks. Additionally, traditional methods, relying on manual inspections and conventional image processing techniques, are often inefficient and prone to high error rates. To mitigate these risks, we propose a real-time detection model - EnhanceNet using a customized Scale-Enhanced Pooling Network (SEP-Net) integrated into the YOLOv4. The innovative SEP-Net enhances feature representation and localization accuracy, significantly contributing to the model's efficacy in detecting prohibited items. We annotated a custom dataset of nine classes and evaluated our models using different input sizes (608 and 416). The 608 input size achieved a mean Average Precision (mAP) of 74.10% with a detection speed of 22.3 Frames per Second (FPS). The 416 input size showed superior performance, achieving a mAP of 76.75% and a detection speed of 27.1 FPS. These demonstrate that our models are accurate and efficient, making them suitable for real-time applications.

Real-Time Tunable Gas Sensing Platform Based on SnO2 Nanoparticles Activated by Blue Micro-Light-Emitting Diodes.

Micro-light-emitting diodes (µLEDs) have gained significant interest as an activation source for gas sensors owing to their advantages, including room temperature operation and low power consumption. However, despite these benefits, challenges still exist such as a limited range of detectable gases and slow response. In this study, we present a blue µLED-integrated light-activated gas sensor array based on SnO2 nanoparticles (NPs) that exhibit excellent sensitivity, tunable selectivity, and rapid detection with micro-watt level power consumption. The optimal power for µLED is observed at the highest gas response, supported by finite-difference time-domain simulation. Additionally, we first report the visible light-activated selective detection of reducing gases using noble metal-decorated SnO2 NPs. The noble metals induce catalytic interaction with reducing gases, clearly distinguishing NH3, H2, and C2H5OH. Real-time gas monitoring based on a fully hardware-implemented light-activated sensing array was demonstrated, opening up new avenues for advancements in light-activated electronic nose technologies.

An immunosensor for the near real-time and site of inflammation detections of multiple proinflammatory cytokines.

Diseases mediated by cytokine storms are often characterized by an overexuberant pace of pathogenesis accompanied by significant morbidity and mortality. Thus, near real-time (NRT) detections via a site-of-inflammation (SOI) sampling of proinflammatory cytokines are essential to ensure a timely and effective treatment of acute inflammations, which up to now, has not been fully possible. In this work, we proposed a novel NRT and SOI immunosensor using ZIF-8 signal amplification together with an off-on strategy. To achieve NRT detections via a SOI sampling, the body fluid of choice is the dermal interstitial fluid (ISF). The significant merits of ISF over blood are the quality, quantity and diversity of ISF-based biomarkers; the fluid is non-coagulating, making it feasible to perform multiple or continuous samplings and the sampling is minimally invasive. Our immunosensor requires only 5 µL of ISF to achieve a simultaneous detection of five highly potent proinflammatory cytokines: IL-6, IFN-γ, IL-1ß, TNF-α, IP-10. We employed a microneedle array patch (MAP) together with a trifurcated nozzle pump to extract a mean volume of between 30 and 60 µL of ISF in 20 min. Under optimal conditions, the biosensor is capable of high-quality performance that exhibits a lower limit of detection (LOD) of 5.761 pg/mL over a wide linear range of 5.761-3 ‒ 20.00 ng/mL. We believe our immunosensor for NRT detections via a SOI sampling of ISF-biomarkers offers new theranostic opportunities that may not be possible with blood-based biomarkers.

Real-time fluorescent multiple cross displacement amplification for rapid and sensitive Mycoplasma pneumoniae detection.

Mycoplasma pneumoniae is a significant pathogen responsible for community-acquired pneumonia, predominantly affecting children and adolescents. Here, we devised a rapid method for M. pneumoniae that combined multiple cross displacement amplification (MCDA) with real-time fluorescence technology. A set of ten primers, which were specifically designed for M. pneumoniae detection, were employed in a real-time fluorescence MCDA reaction. Of these, one primer incorporated a restriction endonuclease recognition sequence, a fluorophore, and a quencher, facilitating real-time fluorescence detection. The real-time (RT)-MCDA reactions were monitored in a simple real-time fluorescence instrument and conducted under optimised conditions (64°C for 40 min). The detection limit of the M. pneumoniae RT-MCDA assay for genomic DNA extracted from M. pneumoniae culture was down to 43 fg/µl. This assay accurately identified M. pneumoniae strains without cross-reacting with other bacteria. To validate its practical application, we tested the M. pneumoniae RT-MCDA assay using genomic DNA extracted from clinical samples. The assay's detection capability proved comparable with real-time PCR, MCDA-based biosensor detection, and visual inspection under blue light. The entire process, including rapid DNA extraction and real-time MCDA detection, was completed within 1 h. Overall, the M. pneumoniae RT-MCDA assay reported here is a simple and effective diagnostic tool for rapid M. pneumoniae detection, which holds significant potential for point-of-care testing and in resource-limited regions.

Development of sensitive biomass xylan-based carbon dots fluorescence sensor for quantification detection Cu2+ in real water and soil.

Copper ions (Cu2+) pose significant risks to both human health and the environment as they tend to accumulate in soil and water. To address this issue, an innovative method using biomass-derived fluorescent carbon dots (D-CDs) synthesized via a hydrothermal process, with xylan serving as the carbon source was developed. D-CDs solution exhibited remarkable sensitivity and selectivity as a fluorescence sensor for Cu2+, boasting a low detection threshold of 0.64 µM. In order to facilitate real-time monitoring of Cu2+, solid-state fluorescent nanofiber membrane (NFD-CDs) through electrospinning was engineered. Additionally, D-CDs demonstrated successful Cu2+ detection in various real water samples, including those sourced from Xuanwu Lake, the Yangtze River, tap water, and bottled water, with accurate recovery rates observed. As a result, this research introduces a dual-mode analytical system for onsite detection of Cu2+ in real scenarios. By harnessing biomass-derived fluorescent CDs materials and solid-state fluorescence sensors, this approach offers a promising solution for addressing the challenges associated with Cu2+ contamination.

Simultaneous noninvasive ultrasensitive detection of prostate specific antigen and lncRNA PCA3 using multiplexed dual optical microfibers with strong plasmonic nanointerfaces.

Low accuracy of diagnosing prostate cancer (PCa) was easily caused by only assaying single prostate specific antigen (PSA) biomarker. Although conventional reported methods for simultaneous detection of two specific PCa biomarkers could improve the diagnostic efficiency and accuracy, low detection sensitivity restrained their use in extreme early-stage PCa clinical assay applications. In order to overcome above drawbacks, this paper herein proposed a multiplexed dual optical microfibers separately functionalized with gold nanorods (GNRs) and Au nanobipyramids (Au NBPs) nanointerfaces with strong localized surface plasmon resonance (LSPR) effects. The sensors could simultaneously detect PSA protein biomarker and long noncoding RNA prostate cancer antigen 3 (lncRNA PCA3) with ultrahigh sensitivity and remarkable specificity. Consequently, the proposed dual optical microfibers multiplexed biosensors could detect the PSA protein and lncRNA PCA3 with ultra-low limit-of-detections (LODs) of 3.97 × 10-15 mol/L and 1.56 × 10-14 mol/L in pure phosphorus buffer solution (PBS), respectively, in which the obtained LODs were three orders of magnitude lower than existed state-of-the-art PCa assay technologies. Additionally, the sensors could discriminate target components from complicated physiological environment, that showing noticeable biosensing specificity of the sensors. With good performances of the sensors, they could successfully assay PSA and lncRNA PCA3 in undiluted human serum and urine simultaneously, respectively. Consequently, our proposed multiplexed sensors could real-time high-sensitivity simultaneously detect complicated human samples, that providing a novel valuable approach for the high-accurate diagnosis of early-stage PCa individuals.

Smartphone image dataset to distinguish healthy and unhealthy leaves in papaya orchards in Bangladesh.

Papaya, renowned for its nutritional benefits, represents a highly profitable crop. However, it is susceptible to various diseases that can significantly impede fruit productivity and quality. Among these, leaf diseases pose a substantial threat, severely impacting the growth of papaya plants. Consequently, papaya farmers frequently encounter numerous challenges and financial setbacks. To facilitate the easy and efficient identification of papaya leaf diseases, a comprehensive dataset has been assembled. This dataset, comprising approximately 1400 images of diseased, infected, and healthy leaves, aims to enhance the understanding of how these ailments affect papaya plants. The images, meticulously collected from diverse regions and under varying weather conditions, offer detailed insights into the disease patterns specific to papaya leaves. Stringent measures have been taken to ensure the dataset's quality and enhance its utility. The images, captured from multiple angles and boasting high resolution are designed to aid in the development of a highly accurate model. Additionally, RGB mode has been employed to meticulously capture each detail, ensuring a flawless representation of the leaves. The dataset meticulously identifies and categorizes five primary types of leaf diseases: Leaf Curl (inclusive of its initial stage), Papaya Mosaic, Ring Spot, Mites (specifically, those affected by Red Spider Mites), and Mealybug. These diseases are recognized for their detrimental effects on both the leaves and the overall fruit production of the papaya plant. By leveraging this curated dataset, it is possible to train a model for the real-time detection of leaf diseases, significantly aiding in the timely identification of such conditions.

Winter wheat ear counting based on improved YOLOv7x and Kalman filter tracking algorithm with video streaming.

Accurate and real-time field wheat ear counting is of great significance for wheat yield prediction, genetic breeding and optimized planting management. In order to realize wheat ear detection and counting under the large-resolution Unmanned Aerial Vehicle (UAV) video, Space to depth (SPD) module was added to the deep learning model YOLOv7x. The Normalized Gaussian Wasserstein Distance (NWD) Loss function is designed to create a new detection model YOLOv7xSPD. The precision, recall, F1 score and AP of the model on the test set are 95.85%, 94.71%, 95.28%, and 94.99%, respectively. The AP value is 1.67% higher than that of YOLOv7x, and 10.41%, 39.32%, 2.96%, and 0.22% higher than that of Faster RCNN, SSD, YOLOv5s, and YOLOv7. YOLOv7xSPD is combined with the Kalman filter tracking and the Hungarian matching algorithm to establish a wheat ear counting model with the video flow, called YOLOv7xSPD Counter, which can realize real-time counting of wheat ears in the field. In the video with a resolution of 3840×2160, the detection frame rate of YOLOv7xSPD Counter is about 5.5FPS. The counting results are highly correlated with the ground truth number (R2 = 0.99), and can provide model basis for wheat yield prediction, genetic breeding and optimized planting management.

Bearing-DETR: A Lightweight Deep Learning Model for Bearing Defect Detection Based on RT-DETR.

Detecting bearing defects accurately and efficiently is critical for industrial safety and efficiency. This paper introduces Bearing-DETR, a deep learning model optimised using the Real-Time Detection Transformer (RT-DETR) architecture. Enhanced with Dysample Dynamic Upsampling, Efficient Model Optimization (EMO) with Meta-Mobile Blocks (MMB), and Deformable Large Kernel Attention (D-LKA), Bearing-DETR offers significant improvements in defect detection while maintaining a lightweight framework suitable for low-resource devices. Validated on a dataset from a chemical plant, Bearing-DETR outperformed the standard RT-DETR, achieving a mean average precision (mAP) of 94.3% at IoU = 0.5 and 57.5% at IoU = 0.5-0.95. It also reduced floating-point operations (FLOPs) to 8.2 G and parameters to 3.2 M, underscoring its enhanced efficiency and reduced computational demands. These results demonstrate the potential of Bearing-DETR to transform maintenance strategies and quality control across manufacturing environments, emphasising adaptability and impact on sustainability and operational costs.

Real-time detection of aging status of methylammonium lead iodide perovskite thin films by using terahertz time-domain spectroscopy.

The inadequate stability of organic-inorganic hybrid perovskites remains a significant barrier to their widespread commercial application in optoelectronic devices. Aging phenomena profoundly affect the optoelectronic performance of perovskite-based devices. In addition to enhancing perovskite stability, the real-time detection of aging status, aimed at monitoring the aging progression, holds paramount importance for both fundamental research and the commercialization of organic-inorganic hybrid perovskites. In this study, the aging status of perovskite was real-time investigated by using terahertz time-domain spectroscopy. Our analysis consistently revealed a gradual decline in the intensity of the absorption peak at 0.968 THz with increasing perovskite aging. Furthermore, a systematic discussion was conducted on the variations in intensity and position of the terahertz absorption peaks as the perovskite aged. These findings facilitate the real-time assessment of perovskite aging, providing a promising method to expedite the commercialization of perovskite-based optoelectronic devices.

TCS-Fall: Cross-individual fall detection system based on channel state information and time-continuous stack method.

Background: Falls pose a serious health risk for the elderly, particular for those who are living alone. The utilization of WiFi-based fall detection, employing Channel State Information (CSI), emerges as a promising solution due to its non-intrusive nature and privacy preservation. Despite these advantages, the challenge lies in optimizing cross-individual performance for CSI-based methods. Objective: This study aimed to develop a resilient real-time fall detection system across individuals utilizing CSI, named TCS-Fall. This method was designed to offer continuous monitoring of activities over an extended timeframe, ensuring accurate and prompt detection of falls. Methods: Extensive CSI data on 1800 falls and 2400 daily activities was collected from 20 volunteers. The grouped coefficient of variation of CSI amplitudes were utilized as input features. These features capture signal fluctuations and are input to a convolutional neural network classifier. Cross-individual performance was extensively evaluated using various train/test participant splits. Additionally, a user-friendly CSI data collection and detection tool was developed using PyQT. To achieve real-time performance, data parsing and pre-processing computations were optimized using Numba's just-in-time compilation. Results: The proposed TCS-Fall method achieved excellent performance in cross-individual fall detection. On the test set, AUC reached 0.999, no error warning ratio score reached 0. 955 and correct warning ratio score reached of 0.975 when trained with data from only two volunteers. Performance can be further improved to 1.00 when 10 volunteers were included in training data. The optimized data parsing/pre-processing achieved over 20× speedup compared to previous method. The PyQT tool parsed and detected the fall within 100 ms. Conclusions: TCS-Fall method enables excellent real-time cross-individual fall detection utilizing WiFi CSI, promising swift alerts and timely assistance to elderly. Additionally, the optimized data processing led to a significant speedup. These results highlight the potential of our approach in enhancing real-time fall detection systems.

Highly Sensitive Detection of Hydrogen Peroxide in Cancer Tissue Based on 3D Reduced Graphene Oxide-MXene-Multi-Walled Carbon Nanotubes Electrode.

Hydrogen peroxide (H2O2) is a signaling molecule that has the capacity to control a variety of biological processes in organisms. Cancer cells release more H2O2 during abnormal tumor growth. There has been a considerable amount of interest in utilizing H2O2 as a biomarker for the diagnosis of cancer tissue. In this study, an electrochemical sensor for H2O2 was constructed based on 3D reduced graphene oxide (rGO), MXene (Ti3C2), and multi-walled carbon nanotubes (MWCNTs) composite. Three-dimensional (3D) rGO-Ti3C2-MWCNTs sensor showed good linearity for H2O2 in the ranges of 1-60 µM and 60 µM-9.77 mM at a working potential of -0.25 V, with sensitivities of 235.2 µA mM-1 cm-2 and 103.8 µA mM-1 cm-2, respectively, and a detection limit of 0.3 µM (S/N = 3). The sensor exhibited long-term stability, good repeatability, and outstanding immunity to interference. In addition, the modified electrode was employed to detect real-time H2O2 release from cancer cells and cancer tissue ex vivo.

An Improved YOLOv7-Based Model for Real-Time Meter Reading with PConv and Attention Mechanisms.

With the increasing complexity of the grid meter dial, precise feature extraction is becoming more and more difficult. Many automatic recognition solutions have been proposed for grid meter readings. However, traditional inspection methods cannot guarantee detection accuracy in complex environments. So, deep-learning methods are combined with grid meter recognition. Existing recognition systems that utilize segmentation models exhibit very high computation. It is challenging to ensure high real-time performance in edge computing devices. Therefore, an improved meter recognition model based on YOLOv7 is proposed in this paper. Partial convolution (PConv) is introduced into YOLOv7 to create a lighter network. Different PConv introduction locations on the base module have been used in order to find the optimal approach for reducing the parameters and floating point of operations (FLOPs). Meanwhile, the dynamic head (DyHead) module is utilized to enhance the attention mechanism for the YOLOv7 model. It can improve the detection accuracy of striped objects. As a result, this paper achieves mAP50val of 97.87% and mAP50:90val of 62.4% with only 5.37 M parameters. The improved model's inference speed can reach 108 frames per second (FPS). It enables detection accuracy that can reach ±0.1 degrees in the grid meter.

Real-Time Arabic Sign Language Recognition Using a Hybrid Deep Learning Model.

Sign language is an essential means of communication for individuals with hearing disabilities. However, there is a significant shortage of sign language interpreters in some languages, especially in Saudi Arabia. This shortage results in a large proportion of the hearing-impaired population being deprived of services, especially in public places. This paper aims to address this gap in accessibility by leveraging technology to develop systems capable of recognizing Arabic Sign Language (ArSL) using deep learning techniques. In this paper, we propose a hybrid model to capture the spatio-temporal aspects of sign language (i.e., letters and words). The hybrid model consists of a Convolutional Neural Network (CNN) classifier to extract spatial features from sign language data and a Long Short-Term Memory (LSTM) classifier to extract spatial and temporal characteristics to handle sequential data (i.e., hand movements). To demonstrate the feasibility of our proposed hybrid model, we created a dataset of 20 different words, resulting in 4000 images for ArSL: 10 static gesture words and 500 videos for 10 dynamic gesture words. Our proposed hybrid model demonstrates promising performance, with the CNN and LSTM classifiers achieving accuracy rates of 94.40% and 82.70%, respectively. These results indicate that our approach can significantly enhance communication accessibility for the hearing-impaired community in Saudi Arabia. Thus, this paper represents a major step toward promoting inclusivity and improving the quality of life for the hearing impaired.

Real-time biofilm detection techniques: advances and applications.

Microbial biofilms, complex assemblies enveloped in extracellular matrices, are significant contributors to various infections. Traditional in vitro biofilm characterization methods, though informative, often disrupt the biofilm structure. The need to address biofilm-related infections urgently emphasizes the importance of continuous monitoring and timely interventions. This review provides a focused examination of advancements in real-time biofilm detection techniques, specifically in electrochemical, optical and mechanical systems. The potential applications of real-time detection in managing and monitoring biofilm growth in industrial settings, preventing medical infections, comprehending biofilm dynamics and evaluating control strategies highlight the necessity for it. Crucially, the review emphasizes the importance of evaluating these methods for their accuracy and reliability in real-time biofilm detection, offering valuable insights for precise interventions across various applications.

[Box: see text].

Establishment and validation of an artificial intelligence-based model for real-time detection and classification of colorectal adenoma.

Colorectal cancer (CRC) prevention requires early detection and removal of adenomas. We aimed to develop a computational model for real-time detection and classification of colorectal adenoma. Computationally constrained background based on real-time detection, we propose an improved adaptive lightweight ensemble model for real-time detection and classification of adenomas and other polyps. Firstly, we devised an adaptive lightweight network modification and effective training strategy to diminish the computational requirements for real-time detection. Secondly, by integrating the adaptive lightweight YOLOv4 with the single shot multibox detector network, we established the adaptive small object detection ensemble (ASODE) model, which enhances the precision of detecting target polyps without significantly increasing the model's memory footprint. We conducted simulated training using clinical colonoscopy images and videos to validate the method's performance, extracting features from 1148 polyps and employing a confidence threshold of 0.5 to filter out low-confidence sample predictions. Finally, compared to state-of-the-art models, our ASODE model demonstrated superior performance. In the test set, the sensitivity of images and videos reached 87.96% and 92.31%, respectively. Additionally, the ASODE model achieved an accuracy of 92.70% for adenoma detection with a false positive rate of 8.18%. Training results indicate the effectiveness of our method in classifying small polyps. Our model exhibits remarkable performance in real-time detection of colorectal adenomas, serving as a reliable tool for assisting endoscopists.

Versatile filter membrane for effective sampling and real-time quantitative detection of airborne pathogens.

Construction of air filter membranes bearing prominent collecting and transferring capability is highly desirable for detecting airborne pathogens but remains challenging. Here, a hyaluronic acid air filter membrane (HAFM) with tunable heterogeneous micro-nano porous structures is straightforwardly constructed through the ethanol-induced phase separation strategy. Airborne pathogens can be trapped and collected by HAFM with high performance due to the ideal trade-off between removal efficiency and pressure drop. By exempting the sample elution and extraction processes, the HAFM after filtration sampling can not only directly disperse on the agar plate for colony culture but also turn to an aqueous solution for centrifugal enrichment, which significantly reduces the damage and losses of the captured microorganisms. The following combination with ATP bioluminescence endows the HAFM with a real-time quantitative detection function for the captured airborne pathogens. Benefiting from high-efficiency sampling and non-traumatic transfer of airborne pathogens, the real-world bioaerosol concentration can be facilely evaluated by the HAFM-based ATP assay. This work thus not only provides a feasible strategy to fabricate air filter membranes for efficient microbial collection and enrichment but also sheds light on designing advanced protocols for real-time detection of bioaerosols in the field.

Phage-based magnetic capture method as an aid for real-time recombinase polymerase amplification detection of Salmonella spp. in milk.

Salmonella is a major cause of foodborne diseases worldwide. Conventional rapid assays for detecting Salmonella in real samples often encounter severe matrix interference or detect a limited number of species of a genus, resulting in inaccurate detection. In this study, we developed a method that combined phage-based magnetic capture with real-time recombinase polymerase amplification (RPA) for the rapid, highly sensitive, and specific detection of Salmonella in milk with an ultra-low detection limit. The Felix O-1 phage-conjugated magnetic beads (O-1 pMBs) synthesized in this method showed excellent capture ability for Salmonella spp. and ideal specificity for non-Salmonella strains. After O-1 pMBs-based magnetic separation, the limit of detection of the real-time RPA assay was 50 cfu/mL in milk samples, which was significantly increased by a magnitude of 3 to 4 orders. The method exhibited a high sensitivity (compatibility) of 100% (14/14) for all tested Salmonella serotype strains and an ideal specificity (exclusivity) of 100% (7/7) for the tested non-Salmonella strains. The entire detection process, including Salmonella capture, DNA extraction, and real-time RPA detection, was completed within 1.5 h. Furthermore, milk samples spiked with 10 cfu/25 mL of Salmonella were detected positive after being cultured in buffered peptone water for only 3 h. Therefore, the proposed method could be an alternative for the rapid and accurate detection of Salmonella.

Flexible SERS sensor based on the photodecoration of Au-NPs on Co3O4 NWs/carbon fiber cloth for the ultrasensitive detection of methylene blue in the curved fish surfaces.

BACKGROUND: Development of flexible platform via the surface-enhanced Raman spectroscopy (SERS) technique has gained enormous attention as a low-cost and portable substrate for a wide range application. In particular, the fabrication of semiconductors and tuning their surface morphologies with plasmonic nanoparticles are considered to be a fascinating strategy to create numerous hotspots to yield superior SERS enhancement. RESULTS: This work involved fabricating a flexible SERS active substrate using the carbon fiber cloth (CFC), which is hydrothermally grown with cobalt oxide nanowires (Co3O4 NWs) and photodecorated with plasmonic gold nanoparticles (Au-NPs) for the ultrasensitive detection of organic dye, methylene blue (MB). The proposed substrate exhibits high enhancement factor (4.5 × 1010), low limit of detection (1.42 × 10-10 M), good uniformity (6.27 %), superior reproducibility (6.30 %) and demonstrate an excellent mechanical strength up to 40 cycles towards the MB detection. The residues of the MB are directly detected on the fish surfaces by adopting a facile swab-sampling technique. Additionally, the proposed flexible SERS sensor exhibit a successful photodegradation of MB at 90 min under UVC light irradiation. SIGNIFICANCE AND NOVELTY: The proposed flexible SERS methodology for detecting MB in the curved surfaces exhibited a superior SERS enhancement owing to the synergistic effect raised from the Co3O4 NWs (chemical enhancement) and Au NPs (electromagnetic enhancement). These findings indicate that the CFC-based flexible SERS sensor is a promising candidate for detecting various organic pollutants in real-time and on non-planar surfaces.