JOURNAL OF HAZARDOUS MATERIALS
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2025
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IF 11.3
Hailong Zhang,Qiannan Duan,Lehan Sun,Jianchao Lee,Weidong Wu,Chi Zhou,Hongfang Zhang,Zhongming Guo,Xiaodan Zhang,Xiang Tang,Duo Yun,Liang Yan,Lei Huang,Peng Wang,Baoxin Zhai
In the field of environmental monitoring, traditional spectroscopic techniques, despite their high sensitivity in heavy metal (HM) detection, are plagued by complex equipment and high costs. The emergence of artificial intelligence (AI) has driven the digitalization and intelligence of spectral analysis. However, the complexity of environmental samples and spectral overlap remain a major challenge. This study proposes a multi-component HM detection method that integrates ultraviolet-visible (UV-Vis) superimposed spectra with deep learning to address the issue of spectral overlap. By using combinatorial chemical probes to enhance the specificity of colorimetric reactions and conducting high-throughput experiments to collect spectra, a Transformer model is trained to extract qualitative and quantitative information in an end-to-end manner. During the method development phase, the model was trained using five representative HMs (Sb, Fe, Ni, Cd, Cu) to verify its recognition ability under complex conditions (average R² = 0.936, RMSE = 0.132 mg L −1 , MAE = 0.054 mg L −1 ). The method was then extended to detect ten HMs in real-world samples, demonstrating its scalability and robustness (average R² = 0.681, RMSE = 0.189 mg L −1 ). These results show that the method can simultaneously analyze multiple HMs and transition from spectral fingerprints to quantified ecological risk profiles. Additionally, the AI analysis software developed in this study automates the entire process from spectral input to multi-index detection and risk assessment, providing environmental regulatory authorities with minute-level response capabilities and facilitating proactive early warning for HM pollution control.
研究领域:
环境科学
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分析化学
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人工智能
✓ 已验证
DOI
PubMed
高影响因子
Surfaces and Interfaces
·
2023
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IF 6.2
Qingyan Han,Yunxiang Li,Ye Chen,Shixing Fan,Yibin Hu,Ronghua Yan,Wei Gao,Chengyun Zhang,Wenwen Zhang,Jun Dong
Surface-enhanced Raman scattering (SERS) has tremendous significance in the fields of ultrasensitive detection and real-time in situ monitoring in catalysis. However, it remains a challenge to simultaneously achieve higher-quality SERS and excellent plasmonic catalytic properties in a nano/microstructure substrate under random excitation light. Here, we propose the design of a three-dimensional (3D) popcorn-like silver (Ag) microstructure (MS) with diameter of 1.2 ± 0.1 µm and a unique rough surface topography , realize a wide surface plasmon resonance (SPR) band ranging from 300 nm to 1100 nm, approximating a full spectrum. The popcorn-like Ag MSs with full-spectrum property effectively broaden the spectral overlap between the SPR band and wavelength of excitation light or absorption of probe molecules. Moreover, the 3D popcorn-like Ag MSs were used to construct a monolayer film array substrate on silicon wafer with a good homogeneity over large areas by an interfacial self-assembly method. The monolayer Ag MSs film array substrate can provide abundant “hot spots” and present excellent SERS performance for sensitive detection of Rhodamine 6G (Rh6G) with a low detection limit of 10 −9 M. The single-layer Ag MSs film array with superior SERS activity is utilized for in situ monitoring the plasmonic catalytic kinetics of 4-nitrothiophenol (4-NTP) dimerized into 4, 4′-dimercaptoazobenzene (DMAB) under 532 nm and 785 nm excitation, respectively. The mechanism on plasmonic catalytic reaction of the popcorn-like Ag MSs substrate is systemically investigated by experimental and theoretical simulations aspects. The Ag MSs film array substrate will offer a new strategy to further explore applications of SERS in sensors, photocatalysis , and bio/chemical analysis.
研究领域:
材料科学
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化学
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光学
✓ 已验证
产品关联
DOI
高影响因子
JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS
·
2024
·
IF 3.1
Xiaozhou Li,Qi Wang,Xintong Li,Cong Wang,Jinhui Lu,Enhui Zhang,Chaolan Liang,Wenjing Wang,Yongshui Fu,Chengyao Li,Ling Zhang,Tingting Li
Toxoplasmosis is a common zoonotic disease caused by a protozoan parasite Toxoplasma gondii (Tox), approximately infecting one-third of human populations worldwide. This study developed the carbon nanospheres (CNPs) based dual spectral-overlapped fluorescence quenching lateral flow immunoassay (CNPs-FQLFIA) for detection of Tox antibodies (ToxAbs). The CNPs have been effectively coupled with Tox antigen (ToxAg), which can completely overlap the excitation and emission spectra of europium nanospheres (EuNPs) and CdSe/ZnS quantum dots (QDs) in testing strips of CNPs-QDs-FQLFIA or CNPs-EuNPs-FQLFIA. The sensitivity of CNPs-EuNPs-FQLFIA or CNPs-QDs-FQLFIA was 4 or 8 IU/mL under natural light readout, or both 4 IU/mL ToxAbs under ultraviolet (UV) light readout by the naked eyes, respectively. The limit of detection (LOD) of two types of CNPs-FQLFIA was both 1 IU/mL ToxAbs under UV light by a dry fluorescence analyzer, but no cross-reaction was found with other antibodies. The intra-assay coefficient variation (CV) of both CNPs-EuNPs-FQLFIA and CNPs-QDs-FQLFIA was less than 8%, while the inter-assay CV was less than 14%, respectively. The correlation coefficient (R 2 ) of CNPs-EuNPs-FQLFIA or CNPs-QDs-FQLFIA to measure the different concentrations of ToxAbs spiked serum samples was 0.99712 and 0.99896, respectively. The CNPs-FQLFIA presented a characteristics of 94.3% sensitivity, 100% specificity and 98% accuracy for detection of ToxAbs in clinical serum samples. In conclusion, CNPs-FQLFIA with EuNPs or QDs fluorescence reporter was an easy, rapid, sensitive, precise and quantitative assay for detecting Tox antibodies in human blood samples.
CAS / MDL:
研究领域:
纳米技术
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免疫学
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医学诊断
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寄生虫学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
SENSORS AND ACTUATORS B-CHEMICAL
·
2019
·
IF 6.393
Wenjing Qi,Yuling Fu,Hongkun He,Maoyu Zhao,Di Wu,Lin Qi,Lianzhe Hu,Rong Li
It is the first time to develop electrochemiluminescence resonance energy transfer (ECRET) strategy using luminol-doped silica nanoparticles (luminol-SiNPs) for both “turn-off” detection of 2,4,6-trinitrophenol (TNP) and “turn-on” detection of anesthetic drug lidocaine hydrochloride (LDC). The spectral overlap of ECL emission of luminol-SiNPs with the absorption of TNP triggers ECRET from luminol-SiNPs to TNP and results in the decreased ECL signals. Besides, the specific interaction between TNP and LDC consumes TNP, accordingly prevents ECRET from luminol-SiNPs to TNP and achieves increasing ECL signals. The proposed ECRET strategy using luminol-SiNPs exhibits fast response (within 10 min), high sensitivity with LOD of 22.4 nM for TNP and 92.5 nM for LDC, respectively. It is also successfully applied in ECRET detection of TNP in lake water, LDC in human serum and commercial drug sample detections with satisfactory recoveries. More importantly, ECL of luminol-SiNPs is measured via linear sweep voltammetry by electrochemical reduction of dissolved oxygen at negative potential (– 1 V) and in no need of adding hydrogen peroxide (H 2 O 2 ). Reactive oxygen species be generated in situ and make luminol-SiNPs system emit ECL, which makes ECL of the whole luminol-SiNPs system more stable than luminol-H 2 O 2 system.
研究领域:
分析化学
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纳米技术
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环境科学
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药物分析
✓ 已验证
DOI
高影响因子
危险品化学品经营许可证（带存储）