Detection Scheme Research Articles

DNA methylation landscape in pregnancy-inducedhypertension: progress and challenges.

Gestational hypertension (PIH), especially pre-eclampsia (PE), is a common complication of pregnancy. This condition poses significant risks to the health of both the mother and the fetus. Emerging evidence suggests that epigenetic modifications, particularly DNA methylation, may play a role in initiating the earliest pathophysiology of PIH. This article describes the relationship between DNA methylation and placental trophoblast function, genes associated with the placental microenvironment, the placental vascular system, and maternal blood and vascular function, abnormalities of umbilical cord blood and vascular function in the onset and progression of PIH, as well as changes in DNA methylation in the progeny of PIH, in terms of maternal, fetal, and offspring. We also explore the latest research on DNA methylation-based early detection, diagnosis and potential therapeutic strategies for PIH. This will enable the field of DNA methylation research to continue to enhance our understanding of the epigenetic regulation of PIH genes and identify potential therapeutic targets.

Film Temperature And Thickness Measurements Of An Impinging Oil Jet For Cooling Of Electric Components Based On Two-Color Laser-Induced Fluorescence.

A fluorescence based two-color detection scheme is used to characterize a jet impingement cooling process relevant for electric and electronic applications. The presented technique enables a simultaneous determination of film temperature and film thickness. The fluorescence signal was generated by the temperature-sensitive fluorescence tracer nile red, which was added to Marlotherm LH, a commercially available heat transfer oil. Suitable band pass filters enable accurate temperature measurements. A preliminary spectral study investigates the influence of dye concentration, temperature and film thickness. At high dye concentrations (up to 37.5 mg/L) reabsorption effects takes place and lead to a spectral shift towards higher wavelengths with increasing film thickness. Low dye concentrations (0.59 mg/L) exhibit no film thickness dependent spectral shift. A film temperature investigation at low dye concentration showed no bias of the intensity ratio due to film thickness, i.e. no additional spectral shift towards lower wavelengths was observed. The investigations on the jet impingement setup revealed an increasing film temperature and decreasing film thickness with increasing solid temperature. The average film temperature increases with increasing solid temperature from 298 K (solid temperature 298 K) to 308 K (solid temperature 398 K). At higher solid temperatures, the film temperature increases with distance to the stagnation zone. The average film thickness decreases with increasing solid temperature from 0.24 mm to 0.17 mm. At high solid temperatures, the film temperature increased with radial distance to the stagnation zone. This behavior is caused by the increasing temperature gradient with increasing solid temperature and decreasing viscosity with increasing film temperature.

Zeptosecond‐Scale Single‐Photon Gyroscope

AbstractThis work presents an all‐fiber telecom‐range optical gyroscope employing a spontaneous parametric down conversion crystal to produce ultra‐low intensity thermal light by tracing‐out one of the heralded photons. The prototype exhibits a detection limit on photon delay measurements of 249 zs over a 72 s averaging time and 26 zs in differential delay measurements at s averaging. The detection scheme proves to be the most resource‐efficient possible, saturating of the Cramér–Rao bound. These results are groundbreaking in the context of low‐photon regime quantum metrology, paving the way to novel experimental configurations to bridge quantum optics with special or general relativity.

Zeptosecond‐Scale Single‐Photon Gyroscope

AbstractThis work presents an all‐fiber telecom‐range optical gyroscope employing a spontaneous parametric down conversion crystal to produce ultra‐low intensity thermal light by tracing‐out one of the heralded photons. The prototype exhibits a detection limit on photon delay measurements of 249 zs over a 72 s averaging time and 26 zs in differential delay measurements at s averaging. The detection scheme proves to be the most resource‐efficient possible, saturating of the Cramér–Rao bound. These results are groundbreaking in the context of low‐photon regime quantum metrology, paving the way to novel experimental configurations to bridge quantum optics with special or general relativity.

Research on CO concentration detection based on deep learning and TDLAS technology

In order to improve the accuracy of detecting CO and other gases in the industrial field, a CO concentration detection scheme based on tunable diode laser absorption spectroscopy (TDLAS) and the CO absorption spectral line at 1566 nm was proposed. At the outset, after removing the outliers from the original data, the harmonic signal is filtered using a moving average filtering method to enhance the signal-to-noise ratio. Subsequently, a one-dimensional convolutional neural network (1D-CNN) is employed to extract the peak and valley values, as well as the mean values of each segment of harmonic data. In the end, the Long short term memory network (LSTM) model establishes a nonlinear fitting relationship between gas characteristics and concentration, and the attention layer is added to further optimize the network weight parameters. The experimental results indicate that the R2 for the test set is 0.9884, and the RMSE is 156.869. Through Allan deviation analysis, the detection limit reaches 1.9605 ppm at an integration time of 60 s. Compared to the traditional regression model, the accuracy and stability are significantly improved, making it highly suitable for gas detection in the industrial circle.

Brief Review for Multi-Class Brain Tumor Diseases Schemes Using Machine Learning Techniques

Brain tumor diseases have had a considerable impact worldwide, affecting millions of individuals of different age groups, including both children and adults above 20 years old. Due to they are more needed in people’s lives, using the method based classifying brain tumors by machine learning schemes has become necessary. However, healthcare applications face challenges in identifying the most suitable classification-based metric, such as accuracy, due to the utilization of recent datasets. This study paper aims to provide a thorough evaluation of computational intelligence strategies used in tumor diagnosis. Several successful data mining techniques have been implemented, including wavelet analysis and spatial pixel modulation techniques. Furthermore, feature extraction and reduction techniques, such as the Grey Level Co-occurrence Matrix (GLCM), have been used to prepare the features for classification. Magnetic resonance imaging scan (MRI) is frequently utilized for the diagnosis of brain tumor diseases which is highly applied for classification-based machine learning, The review paper was focused on gliomas, meningiomas, and pituitary adenoma diseases. Technically, the usage of kernel principal component KPCA analysis with the proposed adaptive back propagation neural network scheme produced better performance-based classification metrics, (i.e:99.84%) for the accuracy metric. The aforementioned review articles have demonstrated that usage of the machine learning-based health care applications (brain diseases) classification widely assists the patient’s outcome and operations inside the hospitals. In summary, the paper has highlighted the importance of machine learning schemes for brain tumor detection and classification, and it also provided a comprehensive analysis and comparison of the state-of-the-art to show the methods such as ;(feature extraction, feature reduction), pros, cons, and the contributions for each of them. The paper's results are considered an advantageous starting point for future works.

Brief Review for Multi-Class Brain Tumor Diseases Schemes Using Machine Learning Techniques

Brain tumor diseases have had a considerable impact worldwide, affecting millions of individuals of different age groups, including both children and adults above 20 years old. Due to they are more needed in people’s lives, using the method based classifying brain tumors by machine learning schemes has become necessary. However, healthcare applications face challenges in identifying the most suitable classification-based metric, such as accuracy, due to the utilization of recent datasets. This study paper aims to provide a thorough evaluation of computational intelligence strategies used in tumor diagnosis. Several successful data mining techniques have been implemented, including wavelet analysis and spatial pixel modulation techniques. Furthermore, feature extraction and reduction techniques, such as the Grey Level Co-occurrence Matrix (GLCM), have been used to prepare the features for classification. Magnetic resonance imaging scan (MRI) is frequently utilized for the diagnosis of brain tumor diseases which is highly applied for classification-based machine learning, The review paper was focused on gliomas, meningiomas, and pituitary adenoma diseases. Technically, the usage of kernel principal component KPCA analysis with the proposed adaptive back propagation neural network scheme produced better performance-based classification metrics, (i.e:99.84%) for the accuracy metric. The aforementioned review articles have demonstrated that usage of the machine learning-based health care applications (brain diseases) classification widely assists the patient’s outcome and operations inside the hospitals. In summary, the paper has highlighted the importance of machine learning schemes for brain tumor detection and classification, and it also provided a comprehensive analysis and comparison of the state-of-the-art to show the methods such as ;(feature extraction, feature reduction), pros, cons, and the contributions for each of them. The paper's results are considered an advantageous starting point for future works.

Electrochemical chiral sensor developed using nanofibrous polyaniline chiral Structures: Detection and differentiation of D- and L-tryptophan

In this work, a simple strategy for electrochemical detection and differentiation of D- and L- forms of tryptophan is demonstrated using nanofibrous structures of chiral polyaniline (CP). Basically, CP with desired chirality is prepared using a chiral acid namely, ± camphor sulfonic acid (±CSA) in presence of either a cationic (or) anionic surfactant (CTAB / SDS) through a simple oxidative polymerization process. Structure, morphology and phase purity of the resultant CP materials are characterized by field emission scanning electron microscopy, Fourier transform infrared spectroscopy, Raman spectroscopy and X-ray diffraction studies. Moreover, the chiral properties of these conducting polymers are evaluated using circular dichroism (CD) spectroscopic studies. From these analyses it is found that CP exhibited twisted nanofibrous morphology and possesses the chirality whereas the non-chiral polyaniline (PANI), prepared in the absence of chiral acid, displayed the formation of a simple fibrous structures without any twists or chirality. Interestingly, such CP materials are found to be electrochemically selective for the detection and differentiation of D- and L- forms of tryptophan (Trp). Among the different chiral materials, CP-CT(−) prepared using CTAB displayed a much better sensing characteristics with higher IL/ID ratio, wider concentration range of detection and lower detection limit of 0.98 µM for l-Trp and 1.2 µM for d-Trp respectively. On the other hand, non-chiral PANI does not display any sensing behavior towards the differentiation of amino acid, Trp. Further, chronoamperometric studies reveal the selectivity of this chiral sensor for the detection of Trp among the other potentially interfering analytes. Although the difference in the measured peak current value is smaller, a significant difference of 70 mV peak potential is observed for the differentiation of L- and d-Trp. Moreover, the proposed CP-based sensing platform exhibits excellent stability and reproducibility with statistically negligible error values suggesting the potential use of the developed material towards electrochemical chiral detection and differentiation of amino acid, Trp.

A Strategy for Accurate SERS Gas Detection: Skillful Integration of Mass-Productive Wafer-Scale SERS Substrate and Machine Learning–Assisted Multifeature Profiling

A Strategy for Accurate SERS Gas Detection: Skillful Integration of Mass-Productive Wafer-Scale SERS Substrate and Machine Learning–Assisted Multifeature Profiling

Study on the application value of fecal SDC2 gene methylation detection in colorectal cancer screening of urban residents in Zengcheng District in Guangzhou City

Objective: To investigate the application value of fecal Syndecan-2 (SDC2) gene methylated SDC2 (mSDC2) detection in colorectal cancer (CRC) screening among urban residents in Guangzhou City. Methods: A cross-sectional study was conducted in Shitan Town, Zengcheng District, Guangzhou City from July to December 2022. A community-based screening program for CRC was conducted among residents aged 40-74 years old. mSDC2 detection was employed in the participants, and those with positive results should be recommended to receive colonoscopy examination. The positive rate of mSDC2 detection, colonoscopy compliance rate, detection rate of intestinal lesions and clinicopathological characteristics were observed. The relationship between cycle threshold (CT) value of mSDC2 and intestinal lesions was explored. Further, the cost-effectiveness of screening was evaluated. Results: A total of 8 189 fecal samples were collected from 8 877 participants with the recovery rate of 92.25%. 8 048 qualified samples were enrolled in this study, consisted of 3 182 males (39.54%) and 4 866 females (60.46%), with the average age of 56 years old (40-74 years). The positive rate of mSDC2 detection was 7.99% (643/8 048), and the compliance rate of colonoscopy was 73.10% (470/643). 20 cases (4.25%) of colorectal cancer, 109 cases (23.19%) of advanced adenoma, 145 cases (30.85%) of non-advanced adenoma, 79 cases (16.81%) of polyps were detected. The detection rate of intestinal lesions was 75.11% and indicated significant differences in gender and age. 20 CRCs included 15 of stage 0-I, 4 of stage Ⅱ-Ⅲ and 1 of unknown stage. The CT value of mSDC2 was negatively correlated with the proportion of advanced colorectal neoplasms (χ2=16.063, P<0.001). The total cost of the screening was 4.339 5 million yuan, the screening benefit was 28.506 2 million yuan, and the benefit-cost ratio was 6.57. Conclusion: The CRC screening strategy of fecal mSDC2 detection combined with colonoscopy has high colonoscopy compliance and detection rate of intestinal lesions, which is conducive to the detection of early CRCs, and has good cost-effectiveness. This study suggests that this method may be applied to the general CRC screening in China and contribute to the prevention of CRC. The CT value of mSDC2 may have a certain suggestion on the malignant degree of intestinal tumors.

Genetic biomarkers of cognitive impairment and dementia of potential interest in CKD patients.

This review discusses genetic variants associated with cognitive dysfunction in chronic kidney disease (CKD) patients, emphasising the limited research in this area. Four studies have explored genetic markers of cognitive dysfunction in CKD, with findings suggesting shared genetic biomarkers between Alzheimer's Disease and CKD.Because of the limited specific research on genetic markers of cognitive dysfunction and dementia in CKD, we extracted data from the current literature studies on genetic markers in the general population that may be relevant to the CKD population. These markers include Apolipoprotein E (APOE), Complement Receptor 1 (CR1), Clusterin (CLU), Sortilin-related receptor 1 (SORL1), Catechol-O-methyltransferase (COMT), and Brain-derived neurotrophic factor (BDNF), all of which are known to be associated with cognitive dysfunction and dementia in other populations. These genes play various roles in lipid metabolism, inflammation, Aβ clearance, and neuronal function, making them potential candidates for studying cognitive decline in CKD patients.CKD-specific research is needed to understand the role of these genetic markers in CKD-related cognitive dysfunction. Investigating how these genes influence cognitive decline in CKD patients could provide valuable insights into early detection, targeted interventions, and personalised treatment strategies. Overall, genetic studies to enhance our understanding and management of cognitive dysfunction in CKD represent a clinical research priority in this population.

Multi-scale and multi-path cascaded convolutional network for semantic segmentation of colorectal polyps

Colorectal polyps are structural abnormalities of the gastrointestinal tract that can potentially become cancerous in some cases. The study introduces a novel framework for colorectal polyp segmentation named the Multi-Scale and Multi-Path Cascaded Convolution Network (MMCC-Net), aimed at addressing the limitations of existing models, such as inadequate spatial dependence representation and the absence of multi-level feature integration during the decoding stage by integrating multi-scale and multi-path cascaded convolutional techniques and enhances feature aggregation through dual attention modules, skip connections, and a feature enhancer. MMCC-Net achieves superior performance in identifying polyp areas at the pixel level. The Proposed MMCC-Net was tested across six public datasets and compared against eight SOTA models to demonstrate its efficiency in polyp segmentation. The MMCC-Net's performance shows Dice scores with confidence interval ranging between 77.43 ± 0.12, (77.08, 77.56) and 94.45 ± 0.12, (94.19, 94.71) and Mean Intersection over Union (MIoU) scores with confidence interval ranging from 72.71 ± 0.19, (72.20, 73.00) to 90.16 ± 0.16, (89.69, 90.53) on the six databases. These results highlight the model's potential as a powerful tool for accurate and efficient polyp segmentation, contributing to early detection and prevention strategies in colorectal cancer.

Transformer with Selective Shuffled Position Embedding and key-patch exchange strategy for early detection of Knee Osteoarthritis

Knee OsteoArthritis (KOA) is a widespread musculoskeletal disorder that can severely impact the mobility of older individuals. Insufficient medical data presents a significant obstacle for effectively training models due to the high cost associated with data labeling. Currently, deep learning-based models extensively utilize data augmentation techniques to improve their generalization ability and alleviate overfitting. However, conventional data augmentation techniques are primarily based on the original data and fail to introduce substantial diversity to the dataset. In our study, we introduce a unique method that utilizes the Vision Transformer (ViT) model, enhanced by our newly developed Selective Shuffled Position Embedding (SSPE) and key-patch exchange techniques. These innovations generate varied input sequences, serving as a data augmentation strategy for improving KOA classification. More specifically, we fix and shuffle the position embeddings of key patches and non-key patches, respectively. Then, for the target image, we randomly select other candidate images from the training set to exchange their key patches and thus obtain different input sequences. Finally, a hybrid loss function is developed by incorporating multiple loss functions for different types of the sequences. According to the experimental results, the generated data are considered valid as they lead to a notable improvement in the model’s classification performance.

Field Detection of Uranyl in Coastal Water of China Using a Portable Device via DNA Photocleavage.

The urgent need for field detection of uranium in seawater is 2-fold: to provide prompt guidance for uranium extraction and to prevent human exposure to nuclear radiation. However, current methods for this purpose are largely hindered by bulky instrumentation, high costs of developed materials, and severe matrix interferences, which limit their further application in the field. Herein, we demonstrated a portable and label-free strategy for the field detection of uranyl in seawater based on the efficient photocleavage of DNA. Further experiments confirmed the generation of ultraviolet (UV) light-induced reactive oxygen species (ROS), such as O2•- and •OH, which fragmented oligomeric DNA in the presence of uranyl and UV light. Detailed studies showed that DNA significantly enhances uranyl absorption in the UV-visible region, leading to the generation of more ROS. A fluorescence system for the selective detection of uranyl in seawater was established by immobilizing two complementary oligonucleotides with the fluorescent dye SYBR Green I. The strategy of UV-induced photocleavage offers high selectivity, excellent interference immunity, and high sensitivity for uranyl, with a detection limit of 6.8 nM. Additionally, the fluorescence can be visually detected using a 3D-printed miniaturized device integrated with a smartphone. This method has been successfully applied to the on-site detection of uranyl in seawater in 18 Chinese coastal cities and along the coast of Hainan Island within 3 min for a single sample. The sample testing and field analysis results indicate that this strategy has promising potential for real-time monitoring of trace uranyl in China's coastal waters. It is expected to be utilized for the rapid assessment of nuclear contamination and nuclear engineering construction.

Copy-move forgery detection using Regional Density Center clustering

Copy-move forgery detection is a common image tampering detection technology. In this paper, a novel copy-move forgery detection scheme is proposed. The proposed scheme is based on Regional Density Center (RDC) clustering and Refined Length Homogeneity Filtering (RLHF) policy. First, to obtain an adequate number of keypoints in smooth or small areas of the image, the proposed scheme employs scale normalization and adjustment of the contrast threshold of the input image. Subsequently, to speed up the feature matching process, a matching algorithm based on gray value grouping is used to match the keypoints. RLHF policy is applied to filter the mismatched pairs. To guarantee a good estimation of the affine transformation, the RDC clustering algorithm is proposed to group the matched pairs. Finally, the correlation coefficients are computed to precisely locate the tampered regions. The proposed copy-move forgery detection scheme based on RDC and RLHF can effectively identify duplicated regions of digital images. It demonstrates the effectiveness and robustness of the proposed scheme over many state-of-the-art schemes on public datasets.

Development of tri-mode lateral flow immunoassay based on tailored porous gold nanoflower for sensitive detection of aflatoxin B1

Aflatoxin B1 (AFB1) is regarded as the most toxic mycotoxin and a proven human carcinogen. The development of detection assays for AFB1 is important for human health and environmental protection. In this study, the hollow porous gold nanoflower (HPGN) was synthesized to achieve colorimetric and photothermal performance. Especially, the HPGN exhibited excellent surface-enhanced Raman scattering (SERS) performance by loading with 4-mercaptobenzoic acid (4-MBA), thus SERS determination of AFB1 could be achieved. Compared with gold nanoparticles (AuNPs), the HPGN possessed unique morphology and excellent properties, providing the basis for sensitive and selective AFB1 detection. Under the optimized experimental conditions, the limits of detection (LODs) of lateral flow immunoassay (LFIA) for AFB1 through colorimetric, photothermal and SERS modes were 0.09 ng/mL, 0.09 ng/mL and 0.05 ng/mL, respectively. The tri-mode detection strategy provided a novel concept for immunosensing of mycotoxins or other small molecules.

A Co-evolutionary Multi-population Evolutionary Algorithm for Dynamic Multiobjective Optimization

Dynamic multiobjective optimization problems (DMOPs) widely appear in various real-world applications and have attracted increasing attention worldwide. However, how to obtain both good population diversity and fast convergence speed to efficiently solve DMOPs are two challenging issues. Inspired by that the multiple populations for multiple objectives (MPMO) framework can provide algorithms with good population diversity and fast convergence speed, this paper proposes a new efficient algorithm called a co-evolutionary multi-population evolutionary algorithm (CMEA) based on the MPMO framework together with three novel strategies, which are helpful for solving DMOPs efficiently from two aspects. First, in the evolution control aspect, a convergence-based population evolution strategy is proposed to select the suitable population for executing the evolution in different generations, so as to accelerate the convergence speed of the algorithm. Second, in the dynamic control aspect, a multi-population-based dynamic detection strategy and a multi-population-based dynamic response strategy are proposed to help the algorithm maintain the population diversity, which are efficient for detecting and responding to the dynamic changes of environments. Integrating with the above strategies, the CMEA is proposed to solve the DMOP efficiently. The superiority of the proposed CMEA is validated in experiments on widely-used DMOP benchmark problems.

Three-birds-with-one-stone: An eco-friendly and renewable humic acid-derived material application strategy for macrolide antibiotic detection and multifunctional composite film preparation

This research proposes a simple and novel strategy for the green detection of antibiotics along with the reduction of microplastic and humic acid (HA) hazards. The entire process is based on a single-step solvent-sieving method to separate HA into insoluble (IHA) and soluble (SHA) components, subsequently recombining and designing the application according to the original characteristics of selected fractions in accordance with the zero-waste principle. IHA was applied as a dispersive solid phase extraction (DSPE) sorbent without chemical modification for the enrichment of trace MACs in complex biological matrices. The recovery of MACs was 74.06–100.84 % in the range of 2.5–1000 μg∙kg−1. Furthermore, SHA could be combined with biodegradable polyvinyl alcohol (PVA) to prepare multifunctional composite films. SHA endows the PVA film with favorable mechanical properties, excellent UV shielding as well as oxidation resistance performance. Compared with pure PVA, the tensile strength, toughness, antioxidant and UV-protection properties were increased to 157.3 Mpa, 258.6 MJ·m−3, 78.6 % and 60 % respectively. This study achieved a green and economically valuable utilization of all components of waste HA, introduced a novel approach for monitoring and controlling harmful substances and reducing white pollution. This has significant implications for promoting sustainable development and recovering valuable resources.

Au-decorated Ti3C2Tx/porous carbon immunoplatform for ECM1 breast cancer biomarker detection with machine learning computation for predictive accuracy

Electrochemical immunosensors, surpassing conventional diagnostics, exhibit significant potential for cancer biomarker detection. However, achieving a delicate balance between signal sensitivity and operational stability, especially at the heterostructure interface, is crucial for practical immunosensors. Herein, porous carbon (PC) integration with Ti3C2Tx-MXene (MX) and gold nanoparticles (Au NPs) constructs a versatile immunosensing platform for detecting extracellular matrix protein-1 (ECM1), a breast cancer-associated biomarker. The inclusion of PC provided robust structural support, enhancing electrolytic diffusion with an expansive surface area while synergistically facilitating charge transfer with Ti3C2Tx. The biosensor optimized with 1.0 mg PC demonstrates a robust electrochemical redox response to the surface-bound thionine (th) redox probe, utilizing an inhibition-based strategy for ECM1 detection. The robust antibody-antigen interactions across the PC-integrated Ti3C2Tx-Au NPs platform (MX-Au-C-1) enabled robust ECM1 detection within 0.1–7.5 nM, with a low limit of detection (LOD) of 0.012 nM. The constructed biosensor shows improved operational stability with a 98.6 % current retention over 1 h, surpassing MXene-integrated (MX-Au) and pristine Au NPs (63.2 % and 44.3 %, respectively) electrodes. Moreover, the successful adaptation of the artificial neural network (ANN) model for predictive analysis of the generated DPV data further validates the accuracy of the biosensor, promising its future application in AI-powered remote health monitoring.

Electrochemical activity of self-supporting nitrogen-doped graphene for the degradation and in-situ determination of methylene blue

Environmentally friendly wastewater treatment and online pollution monitoring systems, important for water quality control, are yet to be developed. Electrochemical strategies combining the electrocatalytic degradation and electroanalysis of organic pollutants are expected to achieve this key technology. This study employed a chemical vapor deposition method using N₂ gas as the nitrogen source and applying an alternating electric field to prepare self-supporting nitrogen-doped graphene (NG) tubes with a wall thickness of up to 480 μm comprising few-layer NG nanosheets. The correlation between the properties of these materials and the electrocatalytic activity for methylene blue (MB) was investigated. The feasibility of the electrochemical strategy for online wastewater treatment and detection was validated using a combination of UV–visible spectroscopy and differential pulse voltammetry. The NG tube with the N-C bond distribution regulated by adjusting the methane flow rate and growth time exhibited high electrocatalytic activity, rapidly degrading 50 mg L⁻¹ of MB within 36 min and tracking changes in MB concentration. The NG tubes were assembled in an electrocatalytic meter pen for water treatment, achieving a degradation efficiency of 99.4 % and a detection limit of 0.3 mg L−1 for the MB solution, quite satisfactory for emerging simultaneous degradation and monitoring processes. The developed self-supporting NG tubes are promising materials for electrocatalytic degradation anodes and sensing electrodes in water treatment. Dual-functional electrocatalytic systems have potential applications in water quality monitoring and improvement.