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In this work, a wireless and passive radio frequency identification (RFID) tag sensor, which integrated a reduced graphene oxide/ion-selective membrane (rGO/ISM) chemiresistive sensing component, was developed for the onsite detection of Pb in soil. Additionally, a new detection method combining impedance mismatch and spectral sensing technique for Pb was proposed. Furthermore, the sensing mechanism of the RFID tag sensor was investigated in terms of the field-effect transistor (FET) transfer curve and antenna reflection coefficient. The charge density in the sensing component was altered by Pb, reducing resistance and modifying the tag antenna impedance. Impedance mismatch between the antenna and RFID chip was thereby induced, with varying degrees of mismatch directly altering the light-emitting diode (LED) luminous intensity on the tag sensor. Rapid detection (<2 s) via luminous intensity monitoring eliminated a vector network analyser (VNA), achieving a detection limit of 1.96 μg/L. Ultimately, the sensing performance of the RFID tag sensor was verified through the detection of Pb in real soil samples (recovery rates of 99-101 %, and RMSEs of 2.08-7.32 μg/kg), indicating that the tag sensor offers a simple and effective solution for rapid and onsite detection of hazardous heavy metals in soil, with potential for practical application.
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http://dx.doi.org/10.1016/j.jhazmat.2025.138763 | DOI Listing |
Biosens Bioelectron
September 2025
Department of Chemistry, College of Science, China Agricultural University, Yuanmingyuan West Road 2#, Haidian District, Beijing, 100193, China. Electronic address:
Aflatoxin B (AFB) is a potent carcinogen and a major environmental and public health threat owing to its persistence and toxicity. In this study, we developed a ratiometric fluorescence probe with excellent responsiveness and specificity for AFB detection based on a molecularly imprinted polymer (MIP)-assisted Al-MOF@Eu-MOF system. This system leverages a dual-selectivity recognition mechanism, where the MIP layer provides molecular-level recognition and anti-interference capability, and the Al-MOFs contribute structural adaptability through their breathing effect.
View Article and Find Full Text PDFMikrochim Acta
September 2025
College of Communications and Electronics Engineering, Qiqihar University, Qiqihar, Heilongjiang, 161006, China.
A passive coding monopod antenna sensor (RFID) tag based on a composite material of titanium dioxide (TiO)/single-walled carbon nanotubes (SWCNT)/reduced graphene oxide (RGO) is studied. This sensor can be used to precisely measure light intensity and carbon dioxide concentration. Under the illumination of light with an intensity ranging from 4 to 18.
View Article and Find Full Text PDFSensors (Basel)
August 2025
Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, 00184 Rome, Italy.
This study presents a wireless, non-invasive sensing system for monitoring the dielectric permittivity of materials, with a particular focus on applications in cultural heritage conservation. The system integrates a passive split-ring resonator tag, electromagnetically coupled to a compact antipodal Vivaldi antenna, operating in the reactive near-field region. Both numerical simulations and experimental measurements demonstrate that shifts in the antenna's reflection coefficient resonance frequency correlate with variations in the dielectric permittivity of the material under test.
View Article and Find Full Text PDFAnimal
July 2025
Department of Animal Science, Penn State University, University Park, PA, USA. Electronic address:
This scoping review addressed the disparity in statistical approaches for validating wearable sensors in dairy cattle behavior research. The objective of this scoping review was to (1) synthesize 101 original research articles that validated wearable sensors to observe dairy cattle behavior (activity and feeding behavior) from the past 11 years to build a reference point for researchers, (2) make recommendations for statistical reporting (precision, bias, and minimum reporting standards) for future validation research that uses wearable sensors to record dairy cattle behavior, focusing on calculating precision, and bias, and reporting reproducibility criteria, and (3) evaluate which validated wearables met our criteria for validity; ≥ 85% precision, reported repeatability criteria, and no bias was observed. A systematic search across PubMed and Web of Science yielded 2 955 articles, which were reduced to 101 after duplicate removal.
View Article and Find Full Text PDFData Brief
October 2025
Systems Engineering and Applications Laboratory, Cadi Ayyad University, ENSA, BP 2390, Marrakech, 40000, Marrakech-Safi, Morocco.
Automating code generation in manufacturing systems requires Artificial Intelligence (AI) models capable of interpreting textual requirement specifications. One of the main challenges is the absence of publicly available, domain-specific datasets suitable for training such models. This article presents AutoFactory, an open-source dataset that includes manually written and LLM-augmented requirement specifications, annotated by domain experts for Named Entity Recognition (NER) tasks using the BIO format.
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