Recognition Performance Research Articles

High-resolution aerial monitoring using DL for identifying abnormal activity based on visual patterns in drone videos

Unmanned aerial vehicles (UAVs) and sophisticated deep learning (DL) models have made the application of artificial intelligence (AI) more popular. This has resulted in an increase in the number of attempts to improve high-resolution aerial monitoring using DL for identifying abnormal activity based on visual patterns in drone videos. The study introduces a one-class support vector machine (OC-SVM) oddity locator for low-altitude, limited-scope UAVs used for ethereal video surveillance. The primary goal is to improve UAV-based observation capabilities by identifying areas or things of interest without prior knowledge, hence improving tasks like queue control, vehicle following, and hazardous product identification. The framework makes use of OC-SVM because of its quick and lightweight setup, making it suitable for continuous operation on low-computational UAVs. It empowers the identification of several peculiarities necessary for low-elevation reconnaissance by using textural characteristics to recognise both large-scale and tiny structures. Examine the UAV mosaicking and change location (UMCD) dataset to demonstrate the effectiveness of the framework, which achieves excellent accuracy and outperforms traditional methods by about one fifth in a variety of metrics. The suggested model compares with current methods, demonstrating superior accuracy and performance in recognition of peculiarities. Evaluation metrics include F1-score, review, exactness, and accuracy. The model demonstrates that it always encounters an oddity with a review compromise of up to seven on ten, achieving complete accuracy.

MT-DSNet: Mix-mask teacher-student strategies and dual dynamic selection plug-in module for fine-grained image recognition

The fine-grained image recognition (FGIR) task aims to classify and distinguish subtle differences between subcategories with visually similar appearances, such as bird species and the makes or models of vehicles. However, subtle interclass differences and significant intraclass variances lead to poor model recognition performance. To address these challenges, we developed a mixed-mask teacher–student cooperative training strategy. A mixed masked image is generated and embedded into a knowledge distillation network by replacing one image’s visible marker with another’s masked marker. Collaborative reinforcement between teachers and students is used to improve the recognition performance of the network. We chose the classic transformer architecture as a baseline to better explore the contextual relationships between features. Additionally, we suggest a dual dynamic selection plug-in for choosing features with discriminative capabilities in the spatial and channel dimensions and filter out irrelevant interference information to efficiently handle background and noise features in fine-grained images. The proposed feature suppression module is used to enhance the differences between different features, thereby motivating the network to mine more discriminative features. We validated our method using two datasets: CUB-200-2011 and Stanford Cars. The experimental results show that the proposed MT-DSNet can significantly improve the feature representation for FGIR tasks. Moreover, by applying it to different fine-grained networks, the FGIR accuracy can be improved without changing the original network structure. We hope that this work provides a promising approach for improving the feature representation of networks in the future.

GT-CAM: Game Theory Based Class Activation Map for GCN.

Graph Convolutional Networks (GCN) have shown outstanding performance in skeleton-based behavior recognition. However, their opacity hampers further development. Researches on the explainability of deep learning have provided solutions to this issue, with Class Activation Map (CAM) algorithms being a class of explainable methods. However, existing CAM algorithms applies to GCN often independently compute the contribution of individual nodes, overlooking the interactions between nodes in the skeleton. Therefore, we propose a game theory based class activation map for GCN (GT-CAM). First, GT-CAM integrates Shapley values with gradient weights to calculate node importance, producing an activation map that highlights the critical role of nodes in decision-making. It also reveals the cooperative dynamics between nodes or local subgraphs for a more comprehensive explanation. Second, to reduce the computational burden of Shapley values, we propose a method for calculating Shapley values of node coalitions. Lastly, to evaluate the rationality of coalition partitioning, we propose a rationality evaluation method based on bipartite game interaction and cooperative game theory. Additionally, we introduce an efficient calculation method for the coalition rationality coefficient based on the Monte Carlo method. Experimental results demonstrate that GT-CAM outperforms other competitive interpretation methods in visualization and quantitative analysis.

Enhanced detection of tomato leaf diseases using ensemble deep learning: INCVX-NET model

<p align="justify"><span lang="EN-US">Automated leaf disease detection quickly identifies early symptoms, and saves time on large farms. Traditional methods like visual inspection and laboratory detection are prevalent despite being labor-intensive, time-consuming, and susceptible to human error. Recently, deep learning (DL) has emerged as a promising alternative for crop disease recognition. However, these models usually demand extensive training data and face problems in generalization due to the diverse features among different crop diseases. This complexity makes it difficult to achieve optimal recognition performance across all scenarios. To solve this issue, a novel ensemble approach INCVX-Net is proposed to integrate the three DL models, ‘Inception, visual geometry group (VGG)-16, and Xception’ using weighted averaging ensemble for tomato crop leaf disease detection. This approach utilizes the strengths of three DL models to recognize a wide range of disease patterns and captures even slight changes in leaf characteristics. INCVX-Net achieves an impressive 99.5% accuracy in disease detection, outperforming base models such as InceptionV2 (93.4%), VGG-16 Net (92.7%), and Xception (95.2%). This significant leap in accuracy demonstrates the growing power of ensemble DL models in disease detection compared to standalone DL models. The research paves the groundwork for future advancements in disease detection, enhancing precision agriculture through ensemble models.</span></p>

Age differences in mental state inference of sarcasm: Contributions of facial emotion recognition and cognitive performance.

Older age is associated with poorer ability to accurately infer mental states, but some mental states are more complex than others. Sarcasm is a complex mental state because the literal and intended meaning of a speaker's words are in opposition. Individuals must rely on additional cues (e.g., facial expressions, intonation) for accurate inference. We hypothesized that understanding of sarcastic versus sincere exchanges would be more sensitive to age-related difficulty in mental state understanding. We examined accuracy at identifying sarcasm among 263 adults (ages 18-90 years) using videos of social interactions in The Awareness of Social Inference Test. Hypotheses were tested using a logistic linear mixed effects model predicting correct/incorrect trial-level responses. To characterize why sarcasm differed with age, we measured two abilities commonly implicated in mental state understanding: facial emotion recognition and cognitive performance. Sarcasm understanding declined with age, whereas understanding of sincere exchanges did not. Both better emotion recognition and cognitive performance related to better understanding of sarcastic but not sincere exchanges. Only cognitive performance showed an age-related effect such that the cognitive performance among the oldest participants facilitated their understanding of both sarcastic and sincere exchanges. We showed that individual variation related to age and social and cognitive performance is more pronounced when the use of multiple mental state cues is more (sarcasm) or less (sincerity) necessary for accurate understanding of social interactions. Naturalistic paradigms involving multiple mental state cues can address important questions about how older adults make decisions in the real world.

An Enhanced Feature Learning CNN Using Auto-encoder for Partially Occluded Target Recognition in SAR Imagery

Deep learning algorithms have found extensive utility in Automatic Target Recognition (ATR) using Synthetic Aperture Radar (SAR). Despite this, conventional deep learning-based ATR models often lack in their performance when subjected to Extended Operating Conditions (EOCs). Significant variations in depression, noise interference, limited training data, and partial occlusion are primary factors in EOCs. In this paper, we proposed a deep neural network-based technique to recognize SAR targets for Standard Operating Conditions (SOC) and Extended Operating Conditions (EOC). The proposed approach comprises an encoder and decoder network. The encoder network integrates multiple dilated convolutions to address noise-induced recognition challenges for extracting multi-scale features. Furthermore, a feature refinement module is integrated within the multi-scale channels to enhance the extraction of discriminative and resilient features. Feature refinement selectively emphasizes informative features while suppressing irrelevant ones. Additionally, a salient attention block and a spatial feature learning module are introduced for feature selection. Selected features are then utilized in the top-level network layer of the encoder network to preserve spatial relationships among diverse features. This spatial feature learning module aids in recognition performance, particularly when training data is scarce. The decoder network consists of stacked transposed convolution layers, facilitating the encoder network's acquisition of discriminative image features. Experimental evaluations were conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset to validate the efficacy of the proposed methodology.

Replay-Based Incremental Learning Framework for Gesture Recognition Overcoming the Time-Varying Characteristics of sEMG Signals

Gesture recognition techniques based on surface electromyography (sEMG) signals face instability problems caused by electrode displacement and the time-varying characteristics of the signals in cross-time applications. This study proposes an incremental learning framework based on densely connected convolutional networks (DenseNet) to capture non-synchronous data features and overcome catastrophic forgetting by constructing replay datasets that store data with different time spans and jointly participate in model training. The results show that, after multiple increments, the framework achieves an average recognition rate of 96.5% from eight subjects, which is significantly better than that of cross-day analysis. The density-based spatial clustering of applications with noise (DBSCAN) algorithm is utilized to select representative samples to update the replayed dataset, achieving a 93.7% recognition rate with fewer samples, which is better than the other three conventional sample selection methods. In addition, a comparison of full dataset training with incremental learning training demonstrates that the framework improves the recognition rate by nearly 1%, exhibits better recognition performance, significantly shortens the training time, reduces the cost of model updating and iteration, and is more suitable for practical applications. This study also investigates the use of the incremental learning of action classes, achieving an average recognition rate of 88.6%, which facilitates the supplementation of action types according to the demand, and further improves the application value of the action pattern recognition technology based on sEMG signals.

The Latest Development of Synthetic Aperture Radar: An Overview

Abstract. Synthetic Aperture Radar (SAR) technology enables high-resolution imaging regardless of weather conditions, offering immense potential for target detection and recognition. This paper reviews the latest advancements in SAR technology, highlighting key developments and applications. The limitations of traditional SAR imaging techniques, particularly the trade-off between resolution and swath width, have been addressed by High Resolution Wide Swath (HRWS) SAR, enabling simultaneous acquisition of high-resolution and wide-swath data. Multi-polarimetric SAR provides richer scattering information for improved target recognition accuracy, while deep learning algorithms enhance image quality and recognition performance. Emerging technologies like 3D-ISAR and holographic imaging offer novel approaches to target recognition and tracking. We discuss the development history, performance advantages, and limitations of these technologies, providing a comprehensive overview of their practical applications. We identify current challenges and future trends in SAR target imaging and recognition, including computational efficiency, adversarial attack defense, data acquisition and processing, and feature extraction and classification. Potential research directions, such as integrated multi-mode SAR systems, multi-source data fusion, and adversarial attack defense strategies, are outlined to fully harness the potential of SAR technology in target imaging and recognition.

QiandaoEar22: a high-quality noise dataset for identifying specific ship from multiple underwater acoustic targets using ship-radiated noise

Target identification of ship-radiated noise is a crucial area in underwater target recognition. However, there is currently a lack of multi-target ship datasets that accurately represent real-world underwater acoustic conditions. To tackle this issue, we conducted experimental data acquisition, resulting in the release of QiandaoEar22—a comprehensive underwater acoustic multi-target dataset. This dataset encompasses 9 h and 28 min of real-world ship-radiated noise data and 21 h and 58 min of background noise data. To demonstrate the availability of QiandaoEar22, we executed two experimental tasks. The first task focuses on assessing the presence of ship-radiated noise, while the second task involves identifying specific ships within the recognized targets in the multi-ship mixed data. In the latter task, we extracted eight features from the data and employed six deep learning networks for classification, aiming to evaluate and compare the performance of various features and networks. The experimental results reveal that ship-radiated noise can be identified from background noise in over 99% of cases. For the specific identification of individual ships, the optimal recognition accuracy achieves 99.56%. Finally, we found using spectrum and MFCC as feature inputs and DenseNet as classifier can achieve excellent recognition performance. Considering the computational efficiency, CRNN and ECAPA-TDNN are also good choices. Our work not only establishes a benchmark for algorithm evaluation but also inspires the development of innovative methods to enhance underwater acoustic target detection (UATD) and underwater acoustic target recognition (UATR).

Effects of a social network enhancement intervention for older adults: a feasibility study

BackgroundSocial networks play a critical role in the mental health of older adults. This pilot study investigates the feasibility of a newly developed intervention to enhance older adults’ social networks. This intervention was designed on the Theory of Mind’s foundation and aimed to enhance older adults’ social interaction motivation through theoretical explanations. Furthermore, the courses fostered more social opportunities for the participants through group-based sessions.MethodsThe feasibility of this intervention was tested using a double-blind, two-arm, non-randomized grouping approach. Older individuals residing in two separate residential buildings (n = 31, mean age = 66.81, 48% women) were divided into an intervention group (n = 15) and a control group (n = 16). They attended daily group sessions at a designated location and completed homework assignments. The primary outcomes of this pilot study were the feasibility of the intervention, and secondary outcomes included Theory of Mind levels and social network indicators. Additional outcomes encompassed levels of global mental health and depression.ResultsAll participants completed the pilot intervention and completed assessments. The primary outcomes indicated that the intervention had excellent feasibility, including compliance (attendance and homework completion rates met the standards) and satisfaction (average ratings for all items ranged from 4.47 to 5.00 on a 5-point scale). Interview results revealed that participants in the intervention group found the intervention beneficial for their daily lives and expressed a desire to participate in a formal intervention. Regarding secondary and additional outcomes, compared to the control group, the intervention group exhibited a significant improvement in emotional recognition performance of Theory of Mind. There was a significant increase in the whole network density in the intervention group. There were no significant differences in other social network indicators, global mental health, and depression levels in the intervention group compared to the control group.ConclusionsThe social network enhancement intervention for older adults is feasible. This pilot study has identified several improvements in the courses and tests. It is necessary to carry out a formal course to examine the effectiveness of the intervention on social networks in older adults.Trial registrationRegistration number: ChiCTR2100053779; Reg Date: 29/11/2021.

Few‐Shot Contrastive Learning‐Based Multi‐Round Dialogue Intent Classification Method

ABSTRACTTraditional text classification models face challenges in handling long texts and understanding topic transitions in dialogue scenarios, leading to suboptimal performance in automatic speech recognition (ASR)‐based multi‐round dialogue intent classification. In this article, we propose a few‐shot contrastive learning‐based multi‐round dialogue intent classification method. First, the ASR texts are partitioned, and role‐based features are extracted using a Transformer encoder. Second, refined sample pairs are forward‐propagated, adversarial samples are generated by perturbing word embedding matrices and contrastive loss is applied to positive sample pairs. Then, positive sample pairs are input into a multi‐round reasoning module to learn semantic clues from the entire scenario through multiple dialogues, obtain reasoning features, input them into a classifier to obtain classification results, and calculate multi‐task loss. Finally, a prototype update module (PUM) is introduced to rectify the biased prototypes by using gated recurrent unit (GRU) to update the prototypes stored in the memory bank and few‐shot learning (FSL) task. Experimental evaluations demonstrate that the proposed method outperforms state‐of‐the‐art methods on two public datasets (DailyDialog and CM) and a private real‐world dataset.

Adaptive Multi-Function Radar Temporal Behavior Analysis

The performance of radar mode recognition has been significantly enhanced by the various architectures of deep learning networks. However, these approaches often rely on supervised learning and are susceptible to overfitting on the same dataset. As a transitional phase towards Cognitive Multi-Functional Radar (CMFR), Adaptive Multi-Function Radar (AMFR) possesses the capability to emit identical waveform signals across different working modes and states for task completion, with dynamically adjustable waveform parameters that adapt based on scene information. From a reconnaissance perspective, the valid signals received exhibit sparsity and localization in the time series. To address this challenge, we have redefined the reconnaissance-focused research priorities for radar systems to emphasize behavior analysis instead of pattern recognition. Based on our initial comprehensive digital system simulation model of a radar, we conducted reconnaissance and analysis from the perspective of the reconnaissance side, integrating both radar and reconnaissance aspects into environmental simulations to analyze radar behavior under realistic scenarios. Within the system, waveform parameters on the radar side vary according to unified rules, while resource management and task scheduling switch based on operational mechanisms. The target in the reconnaissance side maneuvers following authentic behavioral patterns while adjusting the electromagnetic space complexity in the environmental aspect as required. The simulation results indicate that temporal annotations in signal flow data play a crucial role in behavioral analysis from a reconnaissance perspective. This provides valuable insights for future radar behavior analysis incorporating temporal correlations and sequential dependencies.

Research on the Forward Simulation and Intelligent Detection of Defects in Highways Using Ground-Penetrating Radar

The increasing variety and frequency of subgrade defects in operational highways have led to a rise in road safety incidents. This study employed ground-penetrating radar (GPR) detection and forward simulation to analyze the characteristic patterns of common subgrade defects, such as looseness, voids, and cavities. Through the integration of instantaneous feature information from different defect patterns with complex signal techniques, the boundary judgment of structural layers and anomalies in GPR images of various subgrade defects was improved. An intelligent recognition platform was established, and a radar image dataset was created and trained to evaluate the recognition performance of the You Only Look Once (YOLO) v3 and Single-Shot Multi-Box Detector (SSD) algorithms. Evaluation metrics such as precision, recall, F1-score, average precision (AP), and mean average precision (mAP) were used to assess the detection efficiency and accuracy for subgrade defect images. The results showed that YOLO v3 achieved an average detection accuracy of 76.69%, while the SSD achieved 75.07%. This study demonstrates that the reliability of the intelligent recognition and classification of highway subgrade defects can be enhanced by using GPR for non-destructive testing.

Construction of Chiral Covalent Organic Frameworks Through a Linker Decomposition Chiral Induction Strategy for Circularly Polarized Light Detection

AbstractExploring new strategies for construction of chiral covalent organic frameworks (COFs) is of paramount importance yet remains a challenge. Herein, we report the rational design and construction of chiral COFs through a linker decomposition chiral induction (LDCI) strategy. Three pairs of azine‐linked chiral COFs are successfully synthesized by the condensation reactions of C3‐symmetric 4,4′,4′′‐(1,3,5‐triazine‐2,4,6‐triyl)tribenzaldehyde (Tz) with flexible chiral dihydrazide linkers derived from malic acid, aspartic acid and tartaric acid, respectively. Remarkably, upon complete or partial decomposition from flexible chiral dihydrazides to hydrazine during COF synthesis, the homochirality of these COFs, originating from the single‐handedness conformation of propeller‐like Tz cores, is well preserved. Such a stereoselective chiral memory realized via the LDCI strategy is confirmed by time‐dependent powder X‐ray diffraction (PXRD), Fourier transform infrared (FT‐IR) and diffuse reflectance circular dichroism (DRCD). Moreover, the resultant azine‐linked chiral COFs are used as the active materials to fabricate photodetectors to directly distinguish circularly polarized light (CPL), showing impressive recognition performances on the identification of left‐handed circularly (LHC) and right‐handed circularly (RHC) polarized lights. Notably, the residual undecomposed flexible chiral linkers within the COFs are found to be conducive to improving the polarization discrimination ratio. This work highlights LDCI as a new and effective strategy for constructing homochiral COFs with promising future in chiral optical application.

Research on Climactic Chapter Recognition of a Chinese Long Novel Based on Plot Description

Many readers continue to pursue Chinese long novels in the past several decades because of diverse characters and fascinating plots. The climactic chapter is an important part of a Chinese long novel, where the key conflict develops to the extreme point. However, how to quickly and accurately recognize the climactic chapter remains a common problem for many readers in their reading choices. This paper conducts research on recognizing the climactic chapter of a Chinese long novel by accurately describing its plot. The proposed method consists of two parts; one is the extraction of key elements, such as viewpoint paragraphs, non-viewpoint paragraphs, chapter keywords, major characters etc. The other part is the climactic chapter recognition, which applies the Bidirectional Gate Recurrent Unit (BiGRU) model and the multi-head attention to recognize the climactic chapter, on the basis of the chapter plot description matrix. Comparative experiments on the corpus named The Condor Trilogy show that the proposed method in this paper has a better recognition performance compared with the existing models, such as Naive Bayesian (NB), Support Vector Machine (SVM), Roberta-large, and the Bidirectional Long-Short Term Memory (BiLSTM) network. Ablation experiments validated the effectiveness of primary components in the proposed method.

Construction of Chiral Covalent Organic Frameworks Through a Linker Decomposition Chiral Induction Strategy for Circularly Polarized Light Detection

AbstractExploring new strategies for construction of chiral covalent organic frameworks (COFs) is of paramount importance yet remains a challenge. Herein, we report the rational design and construction of chiral COFs through a linker decomposition chiral induction (LDCI) strategy. Three pairs of azine‐linked chiral COFs are successfully synthesized by the condensation reactions of C3‐symmetric 4,4′,4′′‐(1,3,5‐triazine‐2,4,6‐triyl)tribenzaldehyde (Tz) with flexible chiral dihydrazide linkers derived from malic acid, aspartic acid and tartaric acid, respectively. Remarkably, upon complete or partial decomposition from flexible chiral dihydrazides to hydrazine during COF synthesis, the homochirality of these COFs, originating from the single‐handedness conformation of propeller‐like Tz cores, is well preserved. Such a stereoselective chiral memory realized via the LDCI strategy is confirmed by time‐dependent powder X‐ray diffraction (PXRD), Fourier transform infrared (FT‐IR) and diffuse reflectance circular dichroism (DRCD). Moreover, the resultant azine‐linked chiral COFs are used as the active materials to fabricate photodetectors to directly distinguish circularly polarized light (CPL), showing impressive recognition performances on the identification of left‐handed circularly (LHC) and right‐handed circularly (RHC) polarized lights. Notably, the residual undecomposed flexible chiral linkers within the COFs are found to be conducive to improving the polarization discrimination ratio. This work highlights LDCI as a new and effective strategy for constructing homochiral COFs with promising future in chiral optical application.

Direct articulatory observation reveals phoneme recognition performance characteristics of a self-supervised speech model

Variability in speech pronunciation is widely observed across different linguistic backgrounds, which impacts modern automatic speech recognition performance. Here, we evaluate the performance of a self-supervised speech model in phoneme recognition using direct articulatory evidence. Findings indicate significant differences in phoneme recognition, especially in front vowels, between American English and Indian English speakers. To gain a deeper understanding of these differences, we conduct real-time MRI-based articulatory analysis, revealing distinct velar region patterns during the production of specific front vowels. This underscores the need to deepen the scientific understanding of self-supervised speech model variances to advance robust and inclusive speech technology.

Impact of Salary and Recognition on Job Performance: A study of Nangarhar University Lecturers, Afghanistan

Organizations seek to gain a competitive advantage by applying different strategies in today’s competitive environment. Due to intense competitive pressure, organizations attempt to gain a competitive advantage using employee skills, abilities, and expertise. Organizations offer different kinds of incentives, like salary and recognition, to ensure proper utilization and increase employee performance. Satisfied employees become more dedicated, committed, and loyal to organizations than other employees without a proper incentive system. The purpose of this study was to determine the impact of salary and recognition on the job performance of university lecturers at Nangarhar University, Afghanistan. The questionnaire method was adopted to collect data using Likert scales ranging from 1 to 5. Data were collected from 210 respondents and analyzed using SPSS version 27. The Pearson product-moment correlation was applied to determine the impact of salary and recognition on job performance. The result of the study showed that there is a significant correlation between salary and employee performance. In addition, it was also found that there was a significant correlation between recognition and employee performance.

Acute Effects of High-Intensity Resistance Exercise on Recognition of Relational Memory, Lactate, and Serum and Plasma Brain-Derived Neurotrophic Factor.

Baumgartner, NW, Belbis, MD, Kargl, C, Holmes, MJ, Gavin, TP, Hirai, DM, and Kao, S-C. Acute effects of high-intensity resistance exercise on recognition of relational memory, lactate, and serum and plasma brain-derived neurotrophic factor. J Strength Cond Res 38(11): 1867-1878, 2024-Acute aerobic exercise improves memory, but this phenomenon is understudied in response to resistance exercise (RE) despite evidence that RE-induced increases in lactate and brain-derived neurotrophic factor (BDNF) play mechanistic roles in memory performance. To determine the acute effect of RE on lactate, BDNF, and their associations with object and relational memory, blood lactate, and serum and plasma BDNF were taken from 36 adults (average age 23.64 ± 3.89 years; 18 woman) before and immediately after 42 minutes of high-intensity RE and a rest condition on counterbalanced days. Subjects then immediately studied a series of paired objects and completed object and relational recognition tasks. Results revealed a condition by trial interaction, previously studied objects were remembered less accurately following RE ( d = 0.66) but recognition occurred faster ( d = 0.28), indicating a speed-accuracy tradeoff following RE. There was no effect of either intervention on relational recognition performance. Lactate ( d = 3.68) and serum BDNF ( d = 0.74) increased following RE, whereas there was no time-related change in lactate and serum BDNF following rest. However, changes in lactate and BDNF did not predict any measures of object ( rs < 0.25, p s > 0.16) or relation recognition ( rs < 0.28, p s > 0.13). Collectively, these findings suggest that acute high-intensity RE selectively improves the processing speed of recognizing objects at the cost of less accurate recognition of previously studied objects. Furthermore, changes in object and relational memory performance are unlikely driven by acute increases in lactate or BDNF following high-intensity RE.

Electrochemical Biosensor Based on Dual-Ligand Functionalized Lanthanide-Encapsulated Polyoxometalate Conductive Polymer Film for Detecting Broad-Spectrum Tumor Marker MicroRNA-155.

In this work, a dual-ligand functionalized lanthanide-encapsulated selenotungstate [H2N(CH3)2]16Na2H10[Ho6(H2O)10(HPACA)4W10O28(Ac)2][SeW9O33]6 · 60H2O (1, HPACA = 2-pyrazinecarboxylic acid, HAc = acetic acid) was successfully acquired by simultaneously incorporating rigid HPACA and flexible Ac- ligands to one reaction system. Interestingly, the polyanion [Ho6(H2O)10(HPACA)4W10O28(Ac)2][SeW9O33]628- of 1 is composed of six trivacant Keggin-type [B-α-SeW9O33]8- units interconnected through an organic-inorganic hybrid dual-ligand bimetallic [Ho6(H2O)10(HPACA)4W10O28(Ac)2]20+ cluster. Moreover, the 1@PNMPy film (PNMPy = poly(N-methylpyrrole)) was successfully prepared through an electrochemical polymerization strategy. The doping of 1 significantly narrows the bandgap in the 1@PNMPy film, which enables the 1@PNMPy film to exhibit remarkable conductivity and rapid electron transfer capability. Then, the 1@PNMPy film-modified glassy carbon electrode was used to construct a 1@PNMPy-based electrochemical biosensor (ECBS), which achieves sensitive electrochemical detection (a low limit of detection of 0.108 fM and a wide concentration detection range of 10-8-10-15 M) for broad-spectrum tumor marker microRNA-155. Also, the 1@PNMPy-based ECBS has a good specific recognition performance for microRNA-155 in a variety of interfering media. The research not only contributes to a deeper understanding of the synthetic chemistry of multicomponent polyoxometalate (POM)-based materials but also can further expand innovative applications of multicomponent POM-based materials in electrochemical detection and electrochemical devices.