MICROCHEMICAL JOURNAL
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2024
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IF 4.9
Chenyue Kang,Yueying Li,Xuran Wang,Yan Zhang,Caihong Zhang,Ying Zhou,Shaomin Shuang,Guomei Zhang
In this study, we develop a novel ratiometric fluorescent nanosensor for the detection excess Co 2+ in soil. This fluorescent nanosensor is fabricated using mesoporous silica-encapsulated nitrogen-doped carbon dots (N-CDs@mSiO 2 ) and gold nanoclusters stabilized by bovine serum albumin (BSA-AuNCs). The green fluorescence of the carbon dots within the mesoporous silica spheres (mSiO 2 ) serves as an internal reference signal. The red fluoresence BSA-AuNCs are covalently connected onto the surface of amino-functionalized nanospheres (N-CDs@mSiO 2 -NH 2 ), providing the response signal. This nanosensor has dual emission peaks at 520 nm and 650 nm under excitation wavelength of 380 nm. The addition of Co 2+ to this nanosensor causes the fluorescence quenching at 650 nm while the green fluorescence at 520 nm remains unchanged, resulting in fluorescence color change from yellow to green. The developed ratiometric fluorescence nanosensor exhibits excellent selectivity to Co 2+ with a range of 2.00–200.00 μM and a detection limit as low as 0.74 μM. In addition, a Co 2+ prediction model is developed using the Light Gradient Boosting Machine (LightGBM) algorithm. The entire detection process is within 10 s and the model prediction value is as high as 0.991 on average. This shows that the proposed fluorescent nanosensor, optimized with the LightGBM algorithm, provides an efficient, environmentally friendly and potentially practical solution for Co 2+ detection.
研究领域:
分析化学
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环境科学
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材料科学
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产品关联
DOI
高影响因子
Foods
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2024
·Vol. 13
·期 24
·4145
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IF 4.7
Yunpeng Wei,Huiqiang Hu,Minghua Yuan,Huaxing Xu,Xiaobo Mao,Yuping Zhao,Luqi Huang
The bioactive components of chrysanthemum tea are an essential indicator in evaluating its nutritive and commercial values. Combining hyperspectral imaging (HSI) with key wavelength selection and pattern recognition methods, this study developed a novel approach to estimating the content of bioactive components in chrysanthemums, including the total flavonoids (TFs) and chlorogenic acids (TCAs). To determine the informative wavelengths of hyperspectral images, we introduced a variable similarity regularization term into particle swarm optimization (denoted as VSPSO), which can focus on improving the combinatorial performance of key wavelengths and filtering out the features with higher collinearity simultaneously. Moreover, considering the underlying relevance of the phytochemical content and the exterior morphology characteristics, the spatial image features were also extracted. Finally, an ensemble learning model, LightGBM, was established to estimate the TF and TCA contents using the fused features. Experimental results indicated that the proposed VSPSO achieved a superior accuracy, with R2scores of 0.9280 and 0.8882 for TF and TCA prediction. Furthermore, after the involvement of spatial image information, the fused spectral–spatial features achieved the optimal model accuracy on LightGBM. The R2scores reached 0.9541 and 0.9137, increasing by 0.0308–0.1404 and 0.0181–0.1066 in comparison with classical wavelength-related methods and models. Overall, our research provides a novel method for estimating the bioactive components in chrysanthemum tea accurately and efficiently. These discoveries revealed the potential effectiveness for constructing feature fusion in HSI-based practical applications, such as nutritive value evaluation and heavy metal pollution detection, which will also facilitate the development of quality detection in the food industry.
研究领域:
食品科学
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植物化学
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分析化学
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产品关联
DOI
PubMed
高影响因子
JOURNAL OF CONTROLLED RELEASE
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2020
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IF 7.727
Yuan He,Zhuyifan Ye,Xinyang Liu,Zhengjie Wei,Fen Qiu,Hai-Feng Li,Ying Zheng,Defang Ouyang
Nanocrystals have exhibited great advantage for enhancing the dissolution rate of water insoluble drugs due to the reduced size to nanoscale. However, current pharmaceutical approaches for nanocrystals formulation development highly depend on the expert experience and trial-and-error attempts which remain time and resource consuming. In this research, we utilized machine learning techniques to predict the particle size and polydispersity index (PDI) of nanocrystals. Firstly, 910 nanocrystal size data and 341 PDI data by three preparation methods (ball wet milling (BWM) method, high-pressure homogenization (HPH) method and antisolvent precipitation (ASP) method) were collected for the construction of the prediction models. The results demonstrated that light gradient boosting machine (LightGBM) exhibited well performance for the nanocrystals size and PDI prediction with BWM and HPH methods, but relatively poor predictions for ASP method. The possible reasons for the poor prediction refer to low quality of data because of the poor reproducibility and instability of nanocrystals by ASP method, which also confirm that current commercialized products were mainly manufactured by BWM and HPH approaches. Notably, the contribution of the influence factors was ranked by the LightGBM, which demonstrated that milling time, cycle index and concentration of stabilizer are crucial factors for nanocrystals prepared by BWM, HPH and ASP, respectively. Furthermore, the model generalizations and prediction accuracies of LightGBM were confirmed experimentally by the newly prepared nanocrystals. In conclusion, the machine learning techniques can be successfully utilized for the predictions of nanocrystals prepared by BWM and HPH methods. Our research also reveals a new way for nanotechnology manufacture.
研究领域:
药学
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纳米技术
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人工智能
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产品关联
DOI
PubMed
高影响因子
ACS Nano
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2025
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IF 16
Jing-Hui Mao,Zhong-Hui Shen,Jian Wang,Run-Lin Liu,Xiao-Fei Liu,Ying Lan,Mengjun Zhou,Jian-Yong Jiang,Yang Shen,Ce-Wen Nan
Emotion monitoring plays a crucial role in mental health management. However, traditional methods of emotion recognition predominantly rely on subjective questionnaires or facial expression analyses, which are often inadequate for continuous and highly accurate monitoring. In this study, we propose a high-precision, fine-grained emotion recognition system based on multisource throat physiological signals. The system collects signals through optimized flexible multiporous skin sensors and analyzes them using machine learning models capable of efficiently capturing complex feature interactions. First, we adopt a two-step cross-linking strategy to modulate the porous structure of the sensitive layer to enable accurate detection of the diverse and weak physiological signals in the throat. By extracting four-dimensional features from the input of 7025 samples, the platform based on the Light Gradient Boosting Machine (LightGBM) efficiently captures their nonlinear interactions, ultimately achieving precise classification of five emotional states (relaxation, surprise, disgust, fear, and neutral) with an accuracy of 98.9%. Further validation on an independent data set reveals an average emotion recognition accuracy of 99.3%, demonstrating the system’s robustness and reliability in real-world applications. This work provides a viable technological solution for real-time and continuous emotion monitoring, offering significant potential in mental health management and related fields.
研究领域:
生物医学工程
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人工智能
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心理健康
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产品关联
DOI
PubMed
高影响因子
Energies
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2024
·Vol. 17
·期 2
·404
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IF 3
Yin Chen,Zhenli Tang,Xiaofeng Weng,Min He,Sheng Zhou,Ziqiang Liu,Tao Jin
The open-circuit fault in electric vehicle charging stations not only impacts the power quality of the electrical grid but also poses a threat to charging safety. Therefore, it is of great significance to study open-circuit fault diagnosis for ensuring the safe and stable operation of power grids and reducing the maintenance cost of charging stations. This paper addresses the multidimensional characteristics of open-circuit fault signals in charging stations and proposes a fault diagnosis method based on an improved S-transform and LightGBM. The method first utilizes improved incomplete S-transform and principal component analysis (PCA) to extract features of front- and back-stage faults separately. Subsequently, LightGBM is employed to classify the extracted features, ultimately achieving fault diagnosis. Simulation results demonstrate the method’s effectiveness in feature extraction, achieving an average diagnostic accuracy of 97.04% on the test dataset, along with notable noise resistance and real-time performance. Additionally, we designed an experimental platform for diagnosing open-circuit faults in DC charging station and collected experimental fault data. The results further validate the effectiveness of the proposed method.
研究领域:
电气工程
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电力电子
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故障诊断
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机器学习
✓ 已验证
DOI
高影响因子
危险品化学品经营许可证（带存储）