Reliable Detection Research Articles

Electrochemical Sensing Mechanisms and Interfacial Design Strategies of Mesoporous Nanochannel Membranes in Biosensing Applications.

ConspectusPrecise and rapid detection of key biomolecules is crucial for early clinical diagnosis. These critical biomolecules and biomarkers are typically present at low concentrations within complex environments, presenting significant challenges for their accurate and reliable detection. Nowadays, electrochemical sensors based on nanochannel membranes have attracted significant attention due to their high sensitivity, simplicity, rapid response, and label-free point-of-care detection capabilities. The confined arena provided by the nanochannels for target recognition and interactions facilitates detection and signal amplification, leading to enhanced detection performance. The nanochannel membranes also can act as filters to repel the interferents and enable target detection in more complex environments. Thus, sensors based on nanochannel membranes are considered promising platforms for biosensing applications. However, challenges such as uncontrollable structures and unstable performance in some materials limit their applications and theoretical advancements. To investigate the relationship between architecture and sensing performance and to achieve reliable and efficient performance, it is essential to construct sensors with precise nanostructures possessing stable properties. With the development of nanomaterials technology, mesoporous nanochannel membranes with robust, controllable, and ordered mesostructures, along with tunable surface properties and tailored ion transport dynamics, have emerged as promising candidates for achieving reliable and efficient biosensing performance. Additionally, investigating the sensing mechanisms and key influencing factors will provide valuable insights into optimizing sensor architecture and enhancing the efficiency and reliability of biosensing technologies. In this Account, we highlight substantial advancements in mesoporous nanochannel membranes, which are mainly based on the research work published by our group. In the first section, we explore the underlying mechanisms of the sensing processes, including the solid-liquid interfacial interactions and nanoconfinement effects (i.e., electrostatic interactions, hydrophilic/hydrophobic interactions, and steric hindrance effects). We also delve into the key parameters including geometry, materials, recognition elements, and external factors related to mesoporous nanochannel membranes and their impacts on sensing mechanisms and performance. In particular, we point out that mesoporous nanochannel membranes with three-dimensional interconnected networks can facilitate ion penetration and lead to an increased number of binding sites, contributing to high sensitivity. Additionally, composite or multilevel mesoporous nanochannel membranes, particularly when integrated with external stimuli such as pH, light, and heat, can introduce unexpected properties, enhancing the sensing performance. These understandings provide valuable insights into the fundamental principles and influencing factors pertinent to the research and design of intelligent, high-quality sensors or nanofluidic devices. Furthermore, we conduct an analysis of integrating various biosensing mechanisms and strategies, which offers significant opportunities for biomedical monitoring, disease diagnosis, and the pharmaceutical industry. Finally, we describe future research directions and their potential for commercial adoption. Nanochannel sensors with novel structures, properties, and functional porous materials may lead to new trends in biomedical applications, including self-powered and wearable sensors for disease monitoring. We believe that this Account holds implications for promoting interdisciplinary endeavors encompassing chemistry and materials science and nanotechnology as well as analysis, biosensing, and biomedical science.

Circulating Tumor DNA as Measurable Residual Disease in Aggressive B-Cell Lymphoma: A Narrative Review.

Achieving remission is the first step toward a cure in treating aggressive B-cell lymphomas. Radiographic imaging, such as fluorodeoxyglucose-positron emission tomography/computed tomography scans are the current standard to define remission at the end of therapy but lack specificity for lymphoma and cannot detect disease at the molecular level. Identifying measurable residual disease with ultrasensitive detection of circulating tumor DNA potentially offers the possibility of improving clinical outcomes. Early studies of circulating tumor DNA in aggressive B-cell lymphomas showed a strong association with overall tumor burden, and baseline quantitative levels are associated with clinical outcomes after frontline chemotherapy. Next-generation sequencing methods that detect lymphoma-relevant genetic aberrations in circulating tumor DNA can also be used for noninvasive genotyping that strongly mirror tissue biopsies. Rapid changes in circulating tumor DNA dynamics after 1 or 2 cycles of frontline chemotherapy or within weeks of treatment with chimeric antigen receptor T-cell salvage therapy are also highly prognostic. Although serial monitoring of circulating tumor DNA can detect molecular relapse 3 to 6 months before clinical relapse, improved analytical thresholds are required to detect measurable residual disease at a singular point at the end of therapy. Modern advances in circulating tumor DNA methods now allow for the reliable detection of measurable residual disease, with an analytical detection threshold of 1 in 1 million cell-free DNA molecules. The results of this review suggest that modern ultrasensitive methods of detecting circulating tumor DNA may improve the current definition of remission in aggressive B-cell lymphomas. Incorporating circulating tumor DNA at the end of therapy assessment identifies patients who do not require surveillance monitoring and introduces paradigms of treating measurable residual disease within clinical trials. Practical barriers, including standardization of collection, availability, turnaround times, and cost, remain hurdles preventing widespread implementation into clinical practice.

Dipicolylaminofluorene Derivatives for Fluorescent Sensing of Copper(II) Ion and Glyphosate

AbstractThe contamination of water by toxic herbicides poses a significant environmental challenge, necessitating the development of reliable detection tools. In this study, novel dipicolylamino derivatives of fluorene and fluorenone were synthesized via Sonogashira cross‐coupling reactions with high yields. These compounds demonstrated exceptional selectivity for fluorescence turn‐off by Cu(II) ions, with detection limits of 0.99 μM and 1.11 μM in a DMSO‐HEPES buffer system. Investigations using Job's plot, fluorescence recovery with EDTA, and mass spectrometry revealed that the sensing mechanism involves complexation between the compounds and Cu(II) ions. Notably, the Cu(II)‐fluorene complex exhibited a selective fluorescence turn‐on response to glyphosate, achieving a detection limit of 0.93 μM. Quantitative analysis in real water samples demonstrated good recovery rates, underscoring the practical utility of these fluorene and fluorenone derivatives in environmental monitoring.

A Machine Learning Approach for Enhanced Glucose Prediction in Biosensors

The detection of glucose is crucial for diagnosing diseases such as diabetes and enables timely medical intervention. In this study, a disposable enzymatic screen-printed electrode electrochemical biosensor enhanced with machine learning (ML) for quantifying glucose in serum is presented. The platinum working surface was modified by chemical adsorption with biographene (BGr) and glucose oxidase, and the enzyme was encapsulated in polydopamine (PDP) by electropolymerisation. Electrochemical characterisation and morphological analysis (scanning and transmission electron microscopy) confirmed the modifications. Calibration curves in Cormay serum (CS) and selectivity tests with chronoamperometry were used to evaluate the biosensor’s performance. Non-linear ML regression algorithms for modelling glucose concentration and calibration parameters were tested to find the best-fit model for accurate predictions. The biosensor with BGr and enzyme encapsulation showed excellent performance with a linear range of 0.75–40 mM, a correlation of 0.988, and a detection limit of 0.078 mM. Of the algorithms tested, the decision tree accurately predicted calibration parameters and achieved a coefficient of determination above 0.9 for most metrics. Multilayer perceptron models effectively predicted glucose concentration with a coefficient of determination of 0.828, demonstrating the synergy of biosensor technology and ML for reliable glucose detection.

Ambient Stable CsCu2I3 Flexible Gas Sensors for Reliable NO2 Detection at Room Temperature.

The advancement of the Internet of Things and artificial intelligence has increased the demand for air quality monitoring to protect human health. Nitrogen dioxide (NO2), a hazardous pollutant, causes inflammatory responses, even at low concentrations, necessitating sensitive gas sensors. Although metal oxide semiconductor sensors are commonly used, their high-operating temperature and reliance on additional heaters limit miniaturization and increase power consumption. Here, a lead-free, transparent, and flexible CsCu2I3 halide perovskite gas sensor was developed, demonstrating high sensitivity (1509% to 5 ppm) and selectivity for NO2 detection at room temperature with full recovery. The sensor exhibited high stability in ambient air and high-humidity environments, which is not achievable with traditional halide perovskite sensors. First-principles density functional theory calculations revealed the mechanisms underlying its stability and sensitivity. This study highlights the potential of halide perovskites for low-power gas sensing at room temperature, addressing critical challenges for commercially viable wearable applications.

Biosensors and Biomarkers for the Detection of Motion Sickness.

Motion sickness (MS) is a prevalent syndrome that predominantly occurs during transportation and virtual reality (VR). The absence of reliable indicators and detection methods makes precise diagnosis difficult. Biomarker concentrations and trends may imply individual susceptibility, symptom classification, and the specific progression of MS. It is therefore essential to explore biosensors capable of providing sensitive, accurate, and real-time monitoring of biomarkers. This review provides a summary of the pathogenesis and biological pathways underlying MS, followed by an examination of biomarkers and their research progress. The most recent electrochemical biosensors developed for the non-invasive detection of representative biomarkers (e.g., cortisol, α-amylase, and estrogen) are comprehensively summarized. The effectiveness of these biosensors in practical application is discussed. It is anticipated that electrochemical biosensors can be gradually improved from the sampling methods, multimodal combinations, and data processing, which can facilitate the detection of MS toward individuation, refinement, and intelligence.

Novel Electrochemical Sensor Based on Cu-MOF/MWCNT-COOH for the Simultaneous Detection of Ascorbic Acid and Dopamine.

By utilizing carboxylated multiwalled carbon nanotubes (MWCNT-COOH) to strengthen the interaction between the electrode and the analytes and improve the conductivity of the composite material and in conjunction with the superior catalytic properties of copper-based metal-organic framework (MOFs), a novel electrochemical sensor was fabricated from a Cu-MOF/MWCNT-COOH composite, specifically designed for the simultaneous and distinct detection of ascorbic acid (AA) and dopamine (DA). Electrochemical analyses were conducted on the innovative Cu-MOF/MWCNT-COOH electrode through both CV and DPV, revealing unique electrochemical behaviors for AA and DA. The sensor not only showed exceptional electrocatalytic properties but also distinguished itself by its broad dynamic response ranges, covering concentrations from 3 to 1800 μM for AA and from 2 to 180 μM for DA, with detection limits (S/N = 3) of 3.00 μM for AA and 0.32 μM for DA. Furthermore, this electrochemical detection platform exhibited robust reproducibility and selectivity. Examinations of serum samples yielded the recovery rates of AA and DA which were 101.9% and 102.1%, respectively, confirming the sensor's capability to perform reliably under varied biological conditions. The findings confirm the sensor's potential of the proposed method for the simultaneous, sensitive, and reliable detection of AA and DA. In conclusion, the electrochemical sensor has a promising potential for practical applications.

Split crRNA Precisely Assisted Cas12a Expansion Strategy for Simultaneous, Discriminative, and Low-Threshold Determination of Two miRNAs Associated with Multiple Sclerosis.

Multiple sclerosis (MS) can proceed into secondary progressive MS accompanied by persistent neurological deterioration; therefore, accurate diagnosis of MS is of vital significance. Irregularities of microRNAs (miRNAs) expression have been observed in MS, so miRNAs have been evaluated as novel biomarkers and therapeutic targets. Herein, a new strategy named split crRNA precisely assisted Cas12a expansion (SPACE) was developed for simultaneous, discriminative, and low-threshold determination of two MS-related miRNAs: miRNA-155 and miRNA-326. On the one hand, owing to the property that split crRNA could activate Cas12a, miRNAs were designed as the spacers of crRNA to combine with scaffold. These integrated crRNAs then recognized the activators, activating Cas12a and enabling RNA target identification. On the other hand, the SPACE strategy dexterously integrated the activator with reporter probe, and utilized Cas12a's cis-cleavage to achieve simultaneous detection and differential signal output for miRNA-155 and miRNA-326. Moreover, trans-cleavage with ultra-high efficiency was assembled in the SPACE strategy to achieve sensitive quantification of total miRNAs in blood samples at low thresholds. Overall, the diversified and integrated design of the SPACE strategy enabled simultaneous, discriminative, and low-threshold detection of dual MS-related miRNAs in one pot and one step, providing a reliable and accurate Cas12a detection tool for clinical low-threshold diagnosis.

Signal-off colorimetric and signal-on fluorometric dual-mode aptasensor for ultrasensitive detection of Salmonella Typhimurium using graphitic carbon nitride.

Food safety is severely burdened by the prevalence of foodborne pathogens and the diseases they cause, necessitating the development of rapid, easy-to-use, highly sensitive, and reliable detection methods. Here, a signal-off colorimetric and signal-on fluorometric dual-mode detection method for Salmonella Typhimurium (S. typhimurium) was developed based on its unique interaction with aptamer DNA and graphitic carbon nitride (GCN). In the absence of a target Salmonella species, 6-carboxyfluorescein (FAM)-labeled aptamers are adsorbed on the surface of GCN primarily via a π-π interaction, resulting in reduced fluorescence of FAM through GCN-mediated quenching as well as improved peroxidase-like activity of GCN to generate intense blue color through facilitated electrostatic attraction between the negatively charged aptamer and positively charged 3,3',5,5'-tetramethylbenzidine (TMB) substrate. The introduction of S. typhimurium to the sample solution causes the detachment of the aptamer from GCN due to its higher affinity for S. typhimurium than GCN, thereby rapidly reducing the colorimetric signal and recovering the fluorescence. We successfully determined the number of S. typhimurium using this method in a remarkably short duration (10-30min), highlighting its rapidity. The limit of detection values for S. typhimurium were as low as 8 and 3CFU/mL when using colorimetric and fluorometric methods, respectively. Moreover, this method can be used to detect S. typhimurium spiked in real vegetable extract and milk with high reproducibility and reliability. This method serves as a convenient route to the rapid, sensitive, selective, and reliable detection of pathogens from complex food samples, with the potential to replace conventional yet laborious methods currently in use.

Reliable backdoor attack detection for various size of backdoor triggers

Reliable backdoor attack detection for various size of backdoor triggers

Recent advances in lateral flow assays for MicroRNA detection.

Lateral flow assays (LFAs) have emerged as pivotal tools for the rapid and reliable detection of microRNAs (miRNAs). It is believed that these biomarkers are crucial for the diagnosis and prognosis of various diseases, particularly cancer. Traditional miRNA detection techniques, such as quantitative PCR, are highly sensitive but have limited efficacy due to their complexity, high cost, and technical requirements. LFAs are valuable due to their simplicity, affordability, and portability, making them ideal for point-of-care testing in low-resource environments. However, challenges remain in developing highly sensitive and accurate LFA devices for miRNA detection. This review explores recent advancements in LFAs to improve miRNA detection sensitivity and specificity. Key innovations include signal amplification using isothermal methods, the application of CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)/Cas systems for direct targeting of miRNAs, and the incorporation of nanomaterials, such as gold nanoparticles and nanorods, to enhance signal intensity. Using artificial intelligence (AI) algorithms enables precise, automated, and rapid quantification of miRNAs. Moreover, this review examines the ability of LFA-based devices to detect multiple miRNAs simultaneously. One of the most significant advancements is the detection of miR-21 levels as low as 20 pM and let-7a levels as low as 40 pM within ten minutes. This highlights the potential of these devices for clinical diagnostics.

A label-free electrochemical biosensor based on graphene quantum dots-nanoporous gold nanocomposite for highly sensitive detection of glioma cell.

Glioma accounts for 80% of all malignant primary brain tumors with a high mortality rate. Histopathological examination is the current diagnostic methods for glioma, but its invasive surgical interventions can cause cerebral edema or impair neural functioning. Liquid biopsy proves to be an efficient method for glioma detection. However, the blood-brain barrier restricts the number of circulating tumor cells (CTCs) in the bloodstream, posing a challenge for sensitive detection of glioma CTCs. This study aims to use the unique characteristics of nanocomposites and the specificity of Angiopep2 (Ang-2) to develop a method that can sensitively identify glioma CTCs. Herein, a novel label-free impedimetric biosensor was successfully constructed for glioma CTCs detection by using graphene quantum dots (GQDs)-nanoporous gold (NPG) nanocomposites as the immobilized platform and the Ang-2 protein as biorecognition element. The GQDs was homogeneously assembled onto NPG, resulting in the creation of a novel GQDs-NPG nanocomposite with unique structure and function properties. Due to the high electron transfer efficiency of the GQDs-NPG nanocomposite, the developed biosensor exhibited a wild detection range from 1 to 1×106cell mL-1, with a minimal detection limit of 1cell mL-1. Additionally, the glioma cell biosensor demonstrated a strong anti-interference ability against multiple cell lines, and the stability of the biosensor remained at 96% after 21 days of storage. Besides, the quantities of glioma cells detected in human serum samples by the glioma cell biosensor demonstrated outstanding consistency with the standard values added to the samples. The study provided a novel GQDs-NPG nanocomposite and an electrochemical biosensor based on GQDs-NPG was firstly developed for glioma CTCs detection. The glioma cell biosensor showed high sensitivity, low detection limit, strong anti-interference ability, and good stability in complex biological matrix. The reliable detection of glioma cell was successfully realized in human serum, providing an excellent option for liquid biopsy of glioma CTCs identification and early diagnosis of glioma diseases.

Pragmatic algorithm for visual assessment of 4-Repeat tauopathies in [18F]PI-2620 PET Scans.

Standardized evaluation of [18F]PI-2620 tau-PET scans in 4R-tauopathies represents an unmet need in clinical practice. This study aims to investigate the effectiveness of visual evaluation of [18F]PI-2620 images for diagnosing 4R-tauopathies and to develop a straight-forward reading algorithm to improve objectivity and data reproducibility. A total of 83 individuals with [18F]PI-2620 PET scans were included. Participants were classified as probable 4R-tauopathies (n = 29), Alzheimer's disease (AD) (n = 20), α-synucleinopathies (n = 15), and healthy controls (n = 19) based on clinical criteria. Visual assessment of tau-PET scans (choice: 4R-tauopathy, AD-tauopathy, no-tauopathy) was conducted using either 20-40-minute or 40-60-minute intervals, with raw (common) and cerebellar grey matter scaled standardized reading settings (intensity-scaled). Two readers evaluated scans independently and blinded, with a third reader providing consensus in case of discrepant primary evaluation. A regional analysis was performed using the cortex, basal ganglia, midbrain, and dentate nucleus. Sensitivity, specificity, and interrater agreement were calculated for all settings and compared against the visual reads of parametric images (0-60-minutes, distribution volume ratios, DVR). Patients with 4R-tauopathies in contrast to non-4R-tauopathies were detected at higher sensitivity in the 20-40-minute frame (common: 79%, scaled: 76%) compared to the 40-60-minute frame (common: 55%, scaled: 62%), albeit with reduced specificity in the common setting (20-40-min: 78%, 40-60-min: 95%), which was ameliorated in the intensity-scaled setting (20-40-min: 91%, 40-60-min: 96%). Combined assessment of multiple brain regions did not significantly improve diagnostic sensitivity, compared to assessing the basal ganglia alone (76% each). Evaluation of intensity-scaled parametric images resulted in higher sensitivity compared to intensity-scaled static scans (86% vs. 76%) at similar specificity (89% vs. 91%). Visual reading of [18F]PI-2620 tau-PET scans demonstrated reliable detection of 4R-tauopathies, particularly when standardized processing methods and early imaging windows were employed. Parametric images should be preferred for visual assessment of 4R-tauopathies.

Design and Engineering of Silver Nanomushroom Arrays as a Universal Solid-State SERS Platform for the Label-Free, Sensitive, and Quantitative Detection of Trace Proteins.

Surface-enhanced Raman scattering (SERS) is an ultrasensitive optical technique that is critical for protein detection and essential for identifying protein structure and concentrations in various biomedical and diagnostic applications. However, achieving highly sensitive and reproducible SERS signals for label-free proteins remains challenging due to their weak Raman signals and structural complexity. In this study, silver nanomushroom arrays (Ag NMAs) as SERS substrates were readily prepared and surface-engineered using a facile template-assisted micro- and nanofabrication approach. The surface of the substrate exhibits nanoscale roughness, long-range order, and hydrophilicity, enabling rapid and uniform dispersion of protein molecules. These molecules are anchored through Ag-S bonds, resulting in ultrasensitive Raman signals driven by strong electromagnetic enhancement effects. The highly ordered array structure improves signal repeatability, achieving a relative standard deviation of as low as 4.32%. Additionally, utilizing the silicon characteristic peak of the SERS substrate as an internal standard significantly reduces measurement errors, allowing for reliable and precise quantitative detection of protein molecules, with a linear correlation coefficient (R2) exceeding 0.96. Ultrasensitive SERS detection and effective protein discrimination via principal component analysis further validate the Ag NMA substrate's potential for universal trace protein detection. This study presents an advanced SERS platform for the sensitive and rapid detection of trace proteins, showcasing significant potential in pharmaceutical research, metabolic studies, diagnostic medicine, and protein engineering.

Review of filtering based feature selection for Botnet detection in the Internet of Things

Botnets are a major security threat in the Internet of Things (IoT), posing significant risks to user privacy, network availability, and the integrity of IoT devices. With the increasing availability of large datasets that contain hundreds or even thousands of variables, selecting the right set of features can be a challenging task. Feature selection is a critical step in developing effective machine learning-based botnet detection systems, as it enables the selection of a subset of features that are most relevant for detection. This paper provides a comprehensive review of filtering based feature selection techniques for botnet detection in IoT. It examines a range of filtering based techniques and evaluates their effectiveness in addressing the challenges and limitations of botnet detection in IoT. It aims to identify the gaps in the literature and areas for future research, and discuss the broader implications of findings for the field of IoT botnet detection. This review provides valuable insights and guidance for researchers and practitioners working on botnet detection in IoT, and highlights the importance of effective feature selection in developing robust and reliable detection systems.

Detection of Staphylococcus aureus via IgY-Based immunomagnetic separation and a phage lysin LysGH15-Based colloidal gold immunochromatographic assay.

Staphylococcus aureus (S. aureus) is an important human pathogen that frequently causes disease in hospital and community settings. Due to the pervasive emergence of virulent and multidrug-resistant methicillin-resistant S. aureus (MRSA) strains, S. aureus infections have become a leading cause of morbidity and mortality. We aimed to develop a reliable and effective detection method that can be used for preventing and managing infections caused by S. aureus. A novel S. aureus detection platform that combines immunomagnetic separation via immunomagnetic beads (IMBs) and the phage lysin LysGH15-based colloidal gold immunochromatographic assay (GICA) (named IMBs-GICA) was developed. IMBs-GICA was performed on 426 consecutive samples, including 185, 107, 74 and 60 samples from sputum, urine, bronchoalveolar lavage fluid (BALF) and serum samples, respectively. S. aureus culture and PCR assays were routinely conducted in parallel. By targetingS. aureus, the IMBs-GICA platform could detect as little as 40 CFU/mL within 35 min. Among the clinical specimens, 38 samples were S. aureus culture positive, and 36 were IMBs-GICA positive, resulting in an IMBs-GICA sensitivity of 89.5 % (95 % confidence interval 74.3-96.6) and a specificity of 99.5 % (97.9-99.9). IMBs-GICA can rapidly and sensitively detect for S. aureus, demonstrating the potential utility of this developed method in the field of testing.

Anti-plagiarism tool helping developers to generate authentic code

Source code plagiarism detection has become a critical area of research with the increasing prevalence of code reuse in academic and professional settings. In order to achieve thorough code comparison, this work introduces a novel tool for detecting source code plagiarism that combines lexical similarity, abstract syntax trees (ASTs) and cosine similarity. The system incorporates a dynamic front-end that was created using Streamlit, providing an intuitive user interface with a code editor that can run code. Through the "Check Similarity" feature, which calculates the plagiarism percentage and finds the most similar file, the application offers real-time plagiarism detection. The methods, benefits, and difficulties of various approaches are examined in this study, with a focus on how well they identify structural and syntactic similarities. The suggested system has a great deal of promise for academic and professional environments, offering reliable and efficient plagiarism detection.

A Survey on Liver Cancer Detection Using Hyperfusion of CNN and SVM in Machine Learning

Since liver cancer ranks among of the most aggressive renditions of the disease, improving patient outcomes requires early identification. We propose an inventive tactic to liver cancer detection by integrating CNN and SVM. CNNs, known for their powerful feature extraction capabilities, are particularly effective in analysing complex medical images. SVMs, on the other hand, are efficient classifiers that can separate data points in high-dimensional spaces with accuracy. By merging the feature extraction strength of CNN with the classification efficiency of SVM, the proposed model aims to enhance liver cancer detection accuracy and robustness. The experimental results reveal that the fused CNN-SVM model significantly surpasses the performance of standalone CNN and SVM models, achieving a high detection accuracy of 95.2%. This hybrid method offers a promising direction for improving the precision of computer-aided diagnosis systems, contributing to more effective and reliable liver cancer detection methods that can assist healthcare professionals in making timely decisions.

Development and validation of a LAMP-based method for rapid and reliable detection of Xanthomonas albilineans, the causal agent of sugarcane leaf scald

Development and validation of a LAMP-based method for rapid and reliable detection of Xanthomonas albilineans, the causal agent of sugarcane leaf scald

High sensitivity flexible strain sensor for motion monitoring based on MWCNT@MXene and silicone rubber

Research on flexible strain sensors has grown rapidly and is widely applied in the fields of soft robotics, body motion detection, wearable sensors, health monitoring, and sports. In this study, MXene was successfully synthesized in powder form and combined with multi-walled carbon nanotube (MWCNT) to develop MWCNT@MXene conductive network-based flexible strain sensors with silicone rubber (SR) substrate. Combining MWCNTs with MXene as a conductive material has been shown to significantly improve the sensor performance, due to MXene’s high conductivity properties that strengthen the MWCNT conductive pathway, increase sensitivity, and improve sensor stability. The sensor is fabricated by a sandwich method consisting of three layers, which enables more accurate and reliable detection of strain changes. The main innovation of this research is the utilization of MWCNT@MXene as a conductive material that optimizes the performance of flexible strain sensors, overcomes the limitations of previous materials, and makes it a more effective solution for long-term applications. Furthermore, the sensor was evaluated to test its performance through sensitivity, linearity, response time, and durability tests. The results showed that the sensor exhibited excellent performance with a high sensitivity of 39.97 over a strain range of 0-100% and excellent linearity (0.99) over a strain of 0–50%. The sensor also has a fast response time of about 70 ms, it also has good stability during low (1–5%) and high (20–100%) strain cycle testing and can withstand up to 1200 loading and unloading cycles. In addition, the sensor effectively detects a wide range of body movements, including finger, wrist and knee movements. These findings show that the electromechanical properties of strain sensors are significantly improved through the use of MWCNT@MXene as a conductive material, so these sensors are considered a promising solution for applications in wearables and body motion monitoring.