ULTRASONICS
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2023
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IF 4.2
Qi Zhang,Guofeng Zhang,Lan Luo,Zijun Liu,Yifei Zhu,Zheng Fan,Xiasheng Guo,Xiaoge Wu,Dong Zhang,Juan Tu
Ultrasonic cavitation, characterized by the oscillation or abrupt collapse of cavitation nuclei in response to ultrasound stimulation, plays a significant role in various applications within both industrial and biomedical sectors. In particular, inertial cavitation (IC) has garnered considerable attention due to the resulting mechanical, chemical, and thermal effects. Passive cavitation detection (PCD) has emerged as a valuable technique for monitoring this procedure. While the fast Fourier transform (FFT) is a widely used algorithm to analyze IC-induced broadband noise detected by PCD system, it may not adequately capture the time-varying instability of cavitation due to potential nuclei collapse during ultrasound irradiation. In contrast, the continuous wavelet transform offers a more flexible approach, enabling more sensitive analysis of signals with varying frequencies over time. In this study, nanodiamond (ND) and its derivative, nitro-doped nanodiamond (N-AND), known to possess cavitation potential from previous research, were chosen as the source of cavitation nuclei. The cavitation signals detected by PCD were subjected to both FFT and wavelet analyses, with their results comprehensively compared. This research showcased the feasibility of employing wavelet analysis for effective inertial cavitation evaluation. It provided the advantage of monitoring the temporal evolution of cavitation events in real-time, enhancing sensitivity to weak and unstable cavitation signals, especially those in higher order components (3rd and 4th order). Additionally, it yielded a higher level of precision in determining IC thresholds and doses. Furthermore, the inclusion of time information through wavelet analysis offered insights into the limitations of low-cycle ultrasound in inducing IC. This study introduces a novel perspective for more sensitive and precise cavitation assessment, leveraging time and frequency data from wavelet analysis, and holds promise for effective utilization of cavitation effects while minimizing losses and damages resulting from unintended cavitation events.
研究领域:
生物医学工程
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超声技术
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信号处理
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产品关联
DOI
PubMed
高影响因子
ENVIRONMENTAL ENGINEERING SCIENCE
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2020
·Vol. 37
·期 5
·382-392
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IF 1.681
Jingwei Feng,Lijun Pan,Binhua Cui,Yabing Sun,Aiyong Zhang,Miao Gong
To realize the rapid concentration prediction of phenolic pollutants, simultaneous prediction of phenolic pollutants in water by wavelet analysis and support vector machine (SVM) was proposed in this article. The ultraviolet-visible (UV-Vis) absorption spectra of representative phenolic compounds were determined by spectrophotometry. Spectral analysis and spectral reconstruction were carried out by wavelet analysis. Specific types of phenolic compounds were identified by SVM classification. The regression equations of UV-Vis spectra and concentration of phenolic compounds were established by SVM regression, which was used to predict the concentration of the samples. The results showed that in the mixture of two [3-Methylphenol (m-cresol) and phenol], three [2,4-Dichlorophenol (2,4-DCP), m-cresol, and phenol], and four [2,4-DCP, 2,4,6-Trichlorophenol (2,4,6-TCP), phenol, and m-cresol] phenolic compounds, the wavelet reconstruction of phenolic compounds improved with decreasing wavelet decomposition layers. After wavelet decomposition and reconstruction, the spectra of phenolic compounds were smoother and noise elimination was more obvious. In the mixtures of two, three, and four phenolic compounds, the SVM model showed better prediction results. The predicted values by SVM and the reference values showed a good linear relationship (two phenolic compounds, R2 > 0.99; three phenolic compounds, R2 > 0.99; four phenolic compounds, R2 > 0.92). This study provides a feasible method for early warning of phenolic compound pollution in drinking water sources.
研究领域:
环境科学
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化学
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DOI
高影响因子
ADVANCED FUNCTIONAL MATERIALS
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2025
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IF 19
Zhixin Wang,Yuanpeng Fu,Tianzhao Bu,Yuqi Qiu,Bo Pang,Qingyang Zheng,Sen Zhou,Yangyang Li,Xiling Xiao,Zhouping Yin,Hao Wu
Quantitative and objective assessment of muscle spasticity grades in post-stroke patients plays a pivotal role for physicians and patients in adjusting rehabilitation plans and preventing secondary complications. However, the development of such methods for spasticity detection and assessment is hindered by the subjectivity of conventional assessment methods, the limited clinical applicability of digital-devices, and the structural complexity of sensors used for monitoring muscle strength. Here, a multimodal on-skin sensor is developed, enabling simultaneous acquisition of surface electromyography (sEMG) and triboelectric signals, through wavelet analysis, the signal from the sensor can comprehensively reflect the bioelectrical and biomechanical characteristics of human motion. The high-performance triboelectric materials (BPA NPs) with metal-organic–inorganic core-shell structures are synthesized to enhance the signal quality of the multimodal on-skin sensor by leveraging the self-polymerization ability and weak reducibility of dopamine. The sensitivity of the multimodal on-skin sensor is improved by designing a radially arranged micro-cone array with gradient heights. Additionally, a quantitative spasticity assessment strategy is developed by integrating muscle co-activation coefficients (Index 1) and antagonistic efficacy metrics (Index 2), which are strongly correlated with the Modified Ashworth Scale scores. The multimodal on-skin sensor with the proposed assessment strategy enables quantitative assessment of spasticity in 9 spasticity patients.
研究领域:
生物医学工程
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可穿戴传感器
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神经康复
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信号处理
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智能材料
✓ 已验证
产品关联
DOI
高影响因子
危险品化学品经营许可证（带存储）