检索结果-内蒙古大学图书馆

A novel ratiometric fluorescent sensor array based on the copper clusters hydrogels coupling of DNA

SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2025年 327卷 125364页

作者： Cao, Haiyan Dong, Wenfei Shi, Si Shi, Wenbing Yangtze Normal Univ Coll Chem & Chem Engn Chongqing Key Lab New Chem Mat Shale Gas Chongqing 408100 Peoples R China Chongqing Med & Pharmaceut Coll Affiliated Hosp 1 Chongqing 400060 Peoples R China

Hydrogels with DNA possess flexible and designable 3D cross-linked polymer networks, which generate numerous specific stimulus-response characteristics through reasonable design for the potential sensor array applications. Unfortunately, the complementary fragments of specific nucleotide sequences that form cross-links in the hydrogels with DNA often come across these problems of instability and high-cost, leading to a serious impediment to further application. Herein, we construct a novel ratiometric fluorescence sensor array to discriminate and quantify metal ions based on the hydrogels with DNA. The dual-emission hydrogels with DNA were synthesized by the self-assembly reaction of agarose, low-cost nonspecific sequence double-stranded DNA, and optically active copper nanoclusters (Cu NCs) with aggregation-induced emission (AIE) effect. Interestingly, different metal ions could precisely regulate the skeleton network of the hydrogels with DNA, leading to the change of their skeleton network and thus showing the distinguishing FL responses of the hydrogels with DNA. As a proof-of-concept demonstration, the ratiometric fluorescence sensor array was used to discriminate four metal ions (Pb2+, Co2+, Ni2+, and Cr(VI)) at multiple concentrations and metal ion mixtures. It exhibited a good linearity in quantitative analysis and reproducibility. Such a simple and high-sensitivity sensor array has been successfully applied to the high-through discrimination of toxic metal ions in environmental and serum samples.

关键词： Metal nanoclusters Metal ions Ratiometric fluorescence sensor array

来源：评论

学校读者我要写书评

暂无评论

Machine learning assisted paper-based fluorescent sensor array with metal-doped multicolor carbon quantum dots for identification and inactivation of bacteria

引用

TALANTA 2025年 293卷 128035页

作者： Zhu, Liang Mei, Lianghui Xuan, Yan Wang, Fangbin Anhui Agr Univ Sch Food & Nutr Hefei 230036 Peoples R China Hefei Univ Technol Sch Food & Biol Engn Hefei 230009 Peoples R China Anhui Med Univ Sch Life Sci Hefei 230032 Peoples R China

Bacterial infection is a thorny threat in a variety of fields, including medicine, environment, food, and agriculture. A multifunctional platform that meets the demands of both bacterial identification and real-time inactivation is urgently needed. This work introduces a novel paper-based fluorescence sensor array. It incorporates three types of metal-doped multicolor carbon quantum dots (CQDs)-Ag-CQDs, Cu-CQDs, and Zn-CQDs-deposited directly onto filter paper using an inkjet printer. The presence of various bacteria reduces the fluorescence of the CQDs. These changes are detected with a smartphone and analyzed through a machine learning algorithm, facilitating accurate identification of the bacteria. The results reveal the platform's capacity to recognize five kinds of bacteria across a wide concentration range from 1.0 x 103 to 1.0 x 107 CFU/mL, underlining its versatility in detecting different levels of bacteria. The platform also exhibits antibacterial capacity, reaching a better antibacterial effect within 30 min. This platform is not only cost-effective and highly integrated but also provides a concept for designing multifunctional biosensors for on-site bacterial detection and antibacterial applications across various fields.

关键词： Bacterial identification Antibacterial application sensor array CQDs Machine learning

来源：评论

学校读者我要写书评

暂无评论

Detection of CO2, H2S, NH3, and gas mixtures using a quartz crystal microbalance sensor array with five sensitive materials

引用

MEASUREMENT 2025年 242卷

作者： Liu, Xu He, Guilin Huang, Xinyi Zhuang, Yuyi Hou, Juncai Northwest A&F Univ Coll Mech & Elect Engn Yangling 712100 Shaanxi Peoples R China Yanan Water & Environm Protect Grp Co Ltd Yanan 716000 Shaanxi Peoples R China

The large-scale development of chicken farms has inevitably led to an increase in the emission of harmful gases, which has prompted rapid research development on the detection of harmful gases, such as quartz crystal microbalance (QCM) sensors. In this research, a QCM sensor array modified with ethyl cellulose (EC), polyethyleneimine (PEI), polypyrrole (PPy), tetramethylammonium fluoride tetrahydrate (TMAF), and Nitrogendoped tungsten carbide supported on carbon nanosheets (WC@NC) was established to detect gas mixtures. By analyzing the relationship between the fundamental frequency shift and the number of sensitive material layers for the QCM sensor, the optimal number of layers was determined. The QCM sensor array consisted of a blank QCM sensor, an 8-layer EC sensor, a 4-layer PEI sensor, a 3-layer PPy sensor, an 8-layer TMAF sensor, and an 8layer WC@NC sensor. The results showed that the PEI sensor exhibited maximum frequency shifts when detecting gases, while TMAF and WC@NC films had the highest contribution to NH3 detection. TMAF, PEI, and WC@NC played significant roles in the binary classification of exceeding the standard detection of NH3. Additionally, WC@NC and EC sensors played an important role in detecting H2S and CO2, respectively. The QCM sensor array could accurately classify the excessive state of CO2. Overall, the QCM sensor array demonstrated a correct recognition rate of 87.5 %, indicating its potential for classifying the exceedance of NH3, CO2, and H2S gases in gas mixtures. This work provides basic information for research on QCM sensor arrays in gas detection.

关键词： QCM sensor array Sensitive materials Gas detection

来源：评论

学校读者我要写书评

暂无评论

Imaging of Closed Cracks by Water-Coupled sensor array Based on Nonlinear Lamb Waves

引用

JOURNAL OF NONDESTRUCTIVE EVALUATION 2024年第2期43卷 43-43页

作者： Wang, Zijian Yan, Yuwei Wang, Kui Gao, Zihang Wu, Zhishen Southeast Univ Natl & Local Unified Engn Res Ctr Basalt Fiber Pro Key Lab C & PC Struct Natl & Local Unified EngnMinist Educ Nanjing 211189 Peoples R China Southeast Univ Int Inst Urban Syst Engn Sch Civil Engn Nanjing 211189 Peoples R China

Nonlinear waves have shown higher sensitivity to closed cracks in fatigue, corrosion, and thermal damage than linear waves. Using the frequency information, the existence of a closed crack can be detected along a 1D wave path. However, the extraction of nonlinear waves in the time domain is rarely reported. In this study, nonlinear Lamb waves at a closed crack formed by a thermal pulse are extracted in the time domain. Combining with the sensor array technique, an image is reconstructed to show the location of the closed crack on a 2D plate. The recognized crack is 1 mm wide, 2 cm long, and 1 mm deep. The distance between the transmitter and the crack is set to 100, 150, and 200 mm to demonstrate the reliability of the proposed method. The method reported facilitates the damage identification and predictive maintenance of plate-like structures.

关键词： Lamb wave Contact acoustic nonlinearity sensor array Closed crack Thermal damage

来源：评论

学校读者我要写书评

暂无评论

Paper-based fluorescence sensor array with functionalized carbon quantum dots for bacterial discrimination using a machine learning algorithm

引用

ANALYTICAL AND BIOANALYTICAL CHEMISTRY 2024年第13期416卷 3139-3148页

作者： Wang, Fangbin Xiao, Minghui Qi, Jing Zhu, Liang Hefei Univ Technol Sch Food & Biol Engn Hefei 230009 Peoples R China Natl Univ Singapore Dept Chem Singapore 117543 Singapore Hong Kong Polytech Univ Dept Biomed Engn Hong Kong 999077 Peoples R China

The rapid discrimination of bacteria is currently an emerging trend in the fields of food safety, medical detection, and environmental observation. Traditional methods often require lengthy culturing processes, specialized analytical equipment, and bacterial recognition receptors. In response to this need, we have developed a paper-based fluorescence sensor array platform for identifying different bacteria. The sensor array is based on three unique carbon quantum dots (CQDs) as sensing units, each modified with a different antibiotic (polymyxin B, ampicillin, and gentamicin). These antibiotic-modified CQDs can aggregate on the bacterial surface, triggering aggregation-induced fluorescence quenching. The sensor array exhibits varying fluorescent responses to different bacterial species. To achieve low-cost and portable detection, CQDs were formulated into fluorescent ink and used with an inkjet printer to manufacture paper-based sensor arrays. A smartphone was used to collect the responses generated by the bacteria and platform. Diverse machine learning algorithms were utilized to discriminate bacterial types. Our findings showcase the platform's remarkable capability to differentiate among five bacterial strains, within a detection range spanning from 1.0 x 103 CFU/mL to 1.0 x 107 CFU/mL. Its practicality is further validated through the accurate identification of blind bacterial samples. With its cost-effectiveness, ease of fabrication, and high degree of integration, this platform holds significant promise for on-site detection of diverse bacteria.

关键词： Bacterial discrimination sensor array CQDs Machine learning

来源：评论

学校读者我要写书评

暂无评论

Machine learning supported single-stranded DNA sensor array for multiple foodborne pathogenic and spoilage bacteria identification in milk

引用

FOOD CHEMISTRY 2025年第Pt 2期463卷 141115页

作者： Wang, Yi Feng, Yihang Xiao, Zhenlei Luo, Yangchao Univ Connecticut Dept Nutr Sci Storrs CT 06269 USA Univ Connecticut Dept Nutr Sci Nanotechnol & Biodelivery Lab Storrs CT 06269 USA

Ensuring food safety through rapid and accurate detection of pathogenic bacteria in food products is a critical challenge in the food supply chain. In this study, a non-specific optical sensor array was proposed for the identification of multiple pathogenic bacteria in contaminated milk samples. Fluorescence-labeled singlestranded DNA was efficiently quenched by two-dimensional nanoparticles and subsequently recovered by foreign biomolecules. The recovered fluorescence generated a unique fingerprint for each bacterial species, enabling the sensor array to identify eight bacteria (pathogenic and spoilage) within a few hours. Four traditional machine learning models and two artificial neural networks were applied for classification. The neural network showed a 93.8 % accuracy with a 30-min incubation. Extending the incubation to 120 min increased the accuracy of the multiplayer perceptron to 98.4 %. This sensor array is a novel, low-cost, and high-accuracy approach for the identification of multiple bacteria, providing an alternative to plate counting and ELISA methods.

关键词： Machine learning Multiplexed bacteria detection sensor array Milk

来源：评论

学校读者我要写书评

暂无评论

Development of RhB@CdMOF-based Fluorescent sensor array for Discrimination of BTEX

引用

JOURNAL OF FLUORESCENCE 2025年 1-9页

作者： Guo, Zongning Zeng, Shaohan Zhang, Hui Zhang, Xiaolin Wang, Shouhui Hu, Jiwan Huang, Xuelin Yang, Li Jiao, Zhe Guangdong Prov Key Lab Intelligent Port Secur Insp Guangzhou 510700 Peoples R China Huangpu Customs Dist Technol Ctr Dongguan 523000 Peoples R China Dongguan Univ Technol Sch Environm & Civil Engn Dongguan Key Lab Low carbon & Recycling Dongguan 523808 Peoples R China Zengcheng Customs Guangzhou 511340 Peoples R China Guangdong Dongguan Ecol & Environm Monitoring Stn Dongguan Peoples R China

Benzene, toluene ethylbenzene and xylene (BTEX), known for their high toxicity and volatility, are key targets for air quality monitoring. Fluorescent probes based on the luminescence intensity of one transition is not efficient for probing BTEX. In this work, we have synthesized a luminescent RhB@MOF sensor array to realize the probing of different BETX by excellent fingerprint correlation between the BTEX and emission peak-height ratio of ligand to dye moieties. Three kinds of RhB@MOF were synthesized simultaneously by varying concentrations of Rhodamine B (RhB) in the solvothermal reaction of (E)-4-(2-carboxyvinyl)benzoic acid (H2L) and Cd(NO3)24H2O (RhB@MOF1, RhB@MOF2, RhB@MOF3). These sensors exhibited two characteristic emission peaks at 430 nm for the host CdMOF and 570 nm for guest RhB, and the emission peak height ratio I430/I570 were constant for each RhB@MOF. Upon reaction with BTEX, due to gap-gap reaction between ligand and different BETX as well as the different swelling effect of BTEX on RhB, the two emission peaks height ratio of three kinds of RhB@MOF showed specific change to different BTEX. The sensor array exhibited a unique pattern of fluorescence variations of BTEX. Principal component analysis (PCA) was applied and achieved complete separation of BTEX. Linear discriminant analysis (LDA) was then applied to discriminate between individual, binary, ternary, and quaternary mixtures of BTEX, resulting in a 100% accuracy rate. Further study also indicated that concentration gradients did not affect the clustering outcomes of BTEX. Compared to traditional methods, the developed fluorescent sensor array offered a more efficient and economical strategy for the identification of BTEX.

关键词： RhB@MOF Fluorescent detection sensor array BTEX

来源：评论

学校读者我要写书评

暂无评论

Fluorescent sensor array for rapid bacterial identification using antimicrobial peptide-functionalized gold nanoclusters and machine learning

引用

TALANTA 2025年 291卷 127883页

作者： Liu, Renjie Wang, Pengcheng Chen, Yiliang Huang, Fuyuan Shen, Yunqiu Zheng, Yan Zheng, Laibao Wenzhou Med Univ Sch Lab Med & Life Sci Wenzhou Key Lab Sanit Microbiol Key Lab Lab MedMinist Educ Wenzhou 325035 Peoples R China Wenzhou Med Univ Affiliated Yueqing Hosp Yueqing 325600 Peoples R China Jiaxing Univ Affiliated TCM Hosp Jiaxing 314000 Peoples R China

Bacterial infectious diseases pose significant challenges to public health, emphasizing the need for rapid and accurate diagnostic tools. Here, we introduced a multichannel fluorescent sensor array based on antimicrobial peptide-functionalized gold nanoclusters (AMP-AuNCs) designed for precise bacterial identification. By utilizing the unique electrostatic and hydrophobic properties of three AMP-AuNCs, this sensor array generated distinct fluorescence patterns upon binding to different bacterial species. Machine learning algorithms, including Principal Component Analysis (PCA), Hierarchical Clustering Analysis (HCA), and Linear Discriminant Analysis (LDA), were employed to analyze fluorescence fingerprint patterns and identify bacterial strains with high accuracy. The sensor array achieved 100 % accuracy in identifying six common bacterial species and demonstrated an 86.7 % accuracy in classifying clinical Escherichia coli isolates from urinary tract infections. This AMP-AuNCbased sensor array offers a promising approach for rapid and precise bacterial diagnostics, with potential applications in clinical settings for combating antibiotic resistance.

关键词： Bacterial identification Antimicrobial peptides Gold nanoclusters sensor array Machine learning

来源：评论

学校读者我要写书评

暂无评论

Rapid Classification and Concentration Prediction of VOC Gaseous Mixtures Using sensor array With Multitask Learning

引用

IEEE sensorS JOURNAL 2025年第7期25卷 12422-12429页

作者： Li, Lei Zhao, Yanzhe Tong, Bainan Cao, Feng Zhang, Renhang You, Yuan Zhu, Hongyin Changchun Univ Technol Dept Elect & Elect Engn Changchun 130012 Jilin Peoples R China Second Hosp Jilin Univ Dept Ophthalmol Changchun 130041 Jilin Peoples R China Hangzhou City Univ Sch Informat & Elect Engn Hangzhou 310015 Zhejiang Peoples R China

The sensor array is the core of the electronic nose (E-nose) system, while the pattern recognition algorithm is an important component of the system. Therefore, the sensor array optimization and the algorithm research are regarded as two key research fields. Volatile organic compounds (VOCs) mixture are ubiquitous in environmental monitoring, disease screening, industrial emissions, and natural environments. Due to the flammability and potential health impacts, rapid and accurate detection of VOCs is crucial. Currently, E-nose systems for gas detection build separate models and a random number of sensors for classification and concentration prediction, which leads to low efficiency and increased complexity. To address these issues, this study proposed a novel multitask learning (MTL) framework combined with the gated recurrent unit (GRU) and feature enhancement module (MTL-GRUA), performing both gas classification and concentration prediction tasks simultaneously. The model achieved over 99% accuracy in classification tasks for acetone, ethanol, and their mixture, and demonstrated high precision in concentration prediction tasks. By optimizing the hyperparameters of the network structure of MTL-GRUA by a particle swarm optimization (PSO) algorithm and using the first 100-s data instead of stable phase data, the detection efficiency was increased by 63%, achieving rapid detection. Furthermore, the experiment verified that even with an optimization in the number of sensors from eight to three, acceptable detection results could still be obtained. The application of MTL proposed in this work provides a more efficient method for gas detection tasks.

关键词： sensors sensor arrays Multitasking Feature extraction Gas detectors Gases Predictive models Data models sensor phenomena and characterization Accuracy Concentration prediction mixture volatile organic compounds (VOCs) multitask learning (MTL) rapid detection sensor array

来源：评论

学校读者我要写书评

暂无评论

Microwave sensor array Based on Differential Mode Detection Strategy

引用

IEEE MICROWAVE AND WIRELESS TECHNOLOGY LETTERS 2025年第2期35卷 257-260页

作者： Xu, Leijun Li, Fanfan Zhang, Shengqi Bai, Xue Wang, Qiang Chen, Jianfeng Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Peoples R China Southeast Univ State Key Lab Millimeter Waves Nanjing 210096 Peoples R China Guilin Univ Elect Technol Sch Optoelect Engn Guilin 541004 Peoples R China

A novel microwave sensor array utilizing a differential-mode detection strategy is introduced for localized material defect detection. Two types of modified split-ring resonators (SRRs) with unloaded Q-factor above 437 and 290, respectively, are utilized to construct the basic differential sensing elements. By arranging these elements with different sizes, a sensor array can be realized. Since the distinct resonant frequency of each element, multiple transmission zeros are included in the working range. Each resonance zero corresponds to an independent region of the material under test (MUT). Any local defect will cause a split in the corresponding resonant frequency. Therefore, detection results can be obtained simply by observing the split points in the resonance spectrum, eliminating the need for further calibration. This letter presents the coupling mechanisms of the proposed two basic sensing units and the operating principle of the proposed sensor array. A prototype operating from 2.4 to 4.6 GHz with five independent differential units has been designed and fabricated. Three different samples with local defects were used for validation.

关键词： sensor arrays Microwave sensors Filtering theory Couplings Prototypes Microwave measurement Microwave circuits Microstrip antenna arrays Dielectrics Calibration Differential measurement microwave sensor sensor array split-ring resonator (SRR)

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：