CHEMICAL ENGINEERING JOURNAL
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2024
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IF 13.3
Wang Guo,Zhiqiang Ma,Zhou Chen,Haojun Hua,Dong Wang,Mohamed Elhousseini Hilal,Yatian Fu,Pengyi Lu,Jian Lu,Yuanting Zhang,Derek Ho,Bee Luan Khoo
Increasing demand for flexible sensors in healthcare has spurred the development of nanomaterial-polymer composite sensors. However, existing pressure sensors suffer from low sensitivity due to poor interactions between the functional filler and the polymer matrix. For example, the direct chemical bonding of nanomaterials to the surface of chemically inert polymers such as polydimethylsiloxane (PDMS) is not easily achieved. Herein, we demonstrate that MXene can significantly promote the sensitivity of a thin and soft piezoresistive sensor based on a surface functionalized PDMS (FPDMS) sponge. Using plasma treatment, we constructed a MXene-FPDMS (MFP) sponge with high mechanical strength via the interlayer hydrogen bonding effect. The formed MFP sponge structure (∼450 μm in thickness) endows the pressure sensor with a high sensitivity of 14.2 kPa −1 and a low modulus of 9.7 kPa, critical for detecting ultra-low pressures. When operated with deep learning algorithms, the MFP sponge sensor successfully classifies the pronunciation of 26 letters and various polite expressions with an average accuracy of 94 ± 0.6 %. We envision that this work paves the way for the development of intelligent wearable platforms.
研究领域:
材料科学
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电子工程
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人工智能
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DOI
高影响因子
ACS Applied Materials & Interfaces
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2024
·
IF 8.3
Mingming Zhao,Huizi Lu,Zhiheng You,Huayun Chen,Xiao Wang,Yaqing Zhang,Yixian Wang
Developing a colorimetry-based artificial scent screening system (i.e., an olfactory visual sensing system) with high sensitivity and accurate pattern recognition for detecting fruit ripeness remains challenging. In this work, we construct a flexible dye/CelluMOFs-based sensor array with improved sensitivity for on-site detection of characteristic gases of fruits and integrate a densely connected convolutional network (DenseNet) into the sensor array, enabling it to recognize unique scent fingerprints and categorize the ripeness of fruits. In the system, CelluMOFs are synthesized through in situ growth of γ-cyclodextrin metal–organic frameworks (γ-CD-MOFs) on flexible fiber filter paper to fabricate a uniform, flexible and porous dye/CelluMOFs sensitive membrane. Compared to the pristine filter paper, the CelluMOFs exhibit increased porosity with a 62 times higher specific surface area and a 3-fold increase in dye loading capacity after 12 h of adsorption. The prepared dye/CelluMOFs sensing film shows outstanding mechanical and detection stability with negligible deviation after 100 cycles of rubbing. The colorimetric visualization arrays with multiple colorimetric dye/CelluMOFs chips, enable the sensitive recognition and detection of nine kinds of characteristic fruit odors and achieve a high response at 8–1500 ppm of trans-2-hexenal, showcasing remarkably low gas detection thresholds. On the basis of the ppm-level limit of detection with high sensitivity, the fabricated colorimetric sensor arrays are typically used for in situ assessment of fruit ripeness by integrating DenseNet. This approach achieves a satisfactory classification accuracy of 99.09% on the validation set, enabling high-precision prediction of fruit ripeness levels.
研究领域:
食品科学
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材料科学
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人工智能
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产品关联
DOI
PubMed
高影响因子
JOURNAL OF COLLOID AND INTERFACE SCIENCE
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2024
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IF 9.4
Yang Zhang,Han Wang,Saeed Ahmed Khan,Jianing Li,Chenhui Bai,Hulin Zhang,Rui Guo
Direct and consistent monitoring of respiratory patterns is crucial for disease prognostication. Although the wired clinical respiratory monitoring apparatus can operate accurately, the existing defects are evident, such as the indispensability of an external power supply, low mobility, poor comfort, and limited monitoring timeframes. Here, we present a self-powered in-nostril hydrogel sensor for long-term non-irritant anti-interference respiratory monitoring, which is developed from a dual-network binary-solvent thermogalvanic polyvinyl alcohol hydrogel fiber (d = 500 μm, L=30 mm) with Fe 2+ /Fe 3+ ions serving as a redox couple, which can generate a thermoelectrical signal in the nasal cavity based on the temperature difference between the exhaled gas and skin as well as avoid interference from the external environment. Due to strong hydrogen bonding between solvent molecules, the sensor retains over 90 % of its moisture after 14 days, exhibiting great potential in wearable respiratory surveillance. With the assistance of deep learning, the hydrogel fiber-based respiration monitoring strategy can actively recognize seven typical breathing patterns with an accuracy of 97.1 % by extracting the time sequence and dynamic parameters of the thermoelectric signals generated by respiration, providing an alert for high-risk respiratory symptoms. This work demonstrates the significant potential of thermogalvanic gels for next-generation wearable bioelectronics for early screening of respiratory diseases.
研究领域:
生物医学工程
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可穿戴设备
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智能健康
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DOI
PubMed
高影响因子
Small
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2025
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IF 12.1
Junjun Huang,Yuting Zong,Yue Liu,Kunhong Hu,Ben Wang,Xingke Zhao,Tao Jiang,Zhanyong Hong,Zhong Lin Wang
With the advancement of intelligent and refined manufacturing, the demand for vibration sensors in smart equipment has surged. Traditional commercial vibration sensors and triboelectric nanogenerator (TENG)-based sensors are limited to basic amplitude and frequency recognition, failing to address both self-powering and diagnostic needs due to inherent design constraints. To overcome these limitations, this study introduces a novel mechanism combining interface dipole energy and vacuum level optimization in triboelectric materials to explain charge generation and separation under vibration. A TENG device with polydimethylsiloxane (PDMS)-encapsulated metal electrode is designed and developed, enabling the precise recognition of equipment operating status through vibration waveform analysis. By optimizing interface contact area and electron transfer capacity, the device achieves enhanced signal clarity and the introduction of subtler characteristics in the signal waveform. Furthermore, the integration of a deep learning algorithm enables high-resolution classification of vibration states with an accuracy of 98.3% approximately, achieving effective monitoring of the operating status of the jaw crusher and vibrating screen. This work not only verifies the feasibility of designing a self-powered vibration sensor but also demonstrates its potential for real-time monitoring and diagnostic applications in smart equipment.
研究领域:
材料科学
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机械工程
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人工智能
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DOI
PubMed
高影响因子
APPLIED PHYSICS LETTERS
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2023
·Vol. 122
·期 12
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IF 4
Huang Anqi,An Shizhe,Wang Yuebo,Feng Kangyi,Yu Haiyang,Chen Zhuonan,Wan Mingxi,Zong Yujin
Phase-change nanodroplets (PCNDs) have been used as controllable theranostic agents in diverse therapeutic and diagnostic scenarios over the years. However, there is still a pressing need to monitor the accurate distribution of non-flowing extravascular PCNDs by clinical ultrasound for further quantitative analysis. Here, we propose a spatiotemporally super-resolved ultrasound monitoring technique based on tuned post-activation dynamics of high-bulk-boiling-point PCNDs with customized perfluorocarbon cores. The underlying idea was to make the stochastic recondensation-induced contrast signal dominate the total post-activation signal, thus obtaining detectable and resolvable recondensation signals at physiological temperature. The recondensation signal was extracted by inter-frame subtraction and then processed by a deep learning-based detection algorithm tailored to the recondensation signal patterns. Experiments in tissue-mimicking phantoms demonstrate that co-restricting the concentration of PCNDs and the focus-wave activation pulse duration could help generate recondensation signals with ideal detectability and sparsity for accurate monitoring. Meanwhile, the quantitative analysis of the super-resolved results shows the spatiotemporal sensitivity of the proposed technique under varying concentrations and activation pulse durations, which was consistent with the patterns drawn from the total post-activation signal and existing theories on post-activation dynamics of PCNDs. This technique may be suitable for in-depth extravascular monitoring and dose analysis for PCNDs-involved therapies.
研究领域:
医学影像
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纳米医学
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超声技术
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DOI
高影响因子
ACS Applied Nano Materials
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2022
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IF 6.14
Wenrou Yu,Xin Xie,Lei Wang,Quanwei Lv,Yuanyan Jiang,Wenjun Zhang,Chuan Huang,Li-ao Wang,Yingzhou Huang
As few odorous molecules in a gas mixture make a great contribution to its odor intensity, autograding in odor intensity is the key process and of great challenge in the prevention of odor pollution. In this work, the surface-enhanced Raman spectroscopy (SERS) spectra of a gas mixture from kitchen waste are detected on plasmonic MOF nanoparticle (NP) films for autograding in odor intensity. To obtain plasmonic MOF NP films with high SERS sensitivity, AgNP@ZIF-8 NPs with different shell thicknesses and adsorbing times are investigated, which is further confirmed by the simulation of electric field distribution and airflow distribution. Then, a 45 nm shell thickness and 3H adsorbing time are selected to collect the SERS spectra of the gas mixture from kitchen waste with different odor intensities. These SERS spectra point out that the odorous molecules generated from the different statuses of kitchen waste dominate the odor intensity of the gas mixture. Furthermore, autograding in odor intensities with four levels based on deep learning (DL) is achieved here. The results exhibit excellent diagnostic accuracy (93.3%) for the external held-out overall dataset, which indicates that a high-throughput, rapid, and label-free tool for screening odor intensities could be developed. Our work not only enriches the research of gas detection by SERS spectra but also improves the prevention technique of odor pollution.
研究领域:
环境科学
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分析化学
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产品关联
DOI
高影响因子
ACS Applied Materials & Interfaces
·
2023
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IF 9.5
Jun Wang,Dongzhi Zhang,Dongyue Wang,Zhenyuan Xu,Hao Zhang,Xiaoya Chen,Zihu Wang,Hui Xia,Haolin Cai
Flexible wearable pressure sensors have received increasing attention as the potential application of flexible wearable devices in human health monitoring and artificial intelligence. However, the complex and expensive process of the conductive filler has limited its practical production and application on a large scale to a certain extent. This study presents a kind of piezoresistive sensor by sinking nonwoven fabrics (NWFs) into tungsten disulfide (WS2) and Ti3C2Tx MXene solutions. With the advantages of a simple production process and practicality, it is conducive to the realization of large-scale production. The assembled flexible pressure sensor exhibits high sensitivity (45.81 kPa–1), wide detection range (0–410 kPa), fast response/recovery time (18/36 ms), and excellent stability and long-term durability (up to 5000 test cycles). Because of the high elastic modulus of MXene and the synergistic effect between WS2 and MXene, the detection range and sensitivity of the piezoresistive pressure sensor are greatly improved, realizing the stable detection of human motion status in all directions. Meanwhile, its high sensitivity at low pressure allows the sensor to accurately detect weak signals such as weak airflow and wrist pulses. In addition, combining the sensor with deep-learning makes it easy to recognize human respiratory patterns with high accuracy, demonstrating its potential impact in the fields of ergonomics and low-cost flexible electronics.
研究领域:
材料科学
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生物医学工程
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人工智能
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产品关联
DOI
PubMed
高影响因子
IEEE SENSORS JOURNAL
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2024
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IF 4.3
He Yu,Guan-Ya Zhou,Yu-Bing Liu,Hao-Bin Li,Lin Song,Chang-Yun Kun Xiao,Mu-Gen Peng
Carbon-based nanomaterials, particularly graphene, have garnered significant attention across diverse fields owing to their remarkable properties and versatile applications. Among these, laser-induced graphene (LIG) exhibits high porosity, excellent conductivity, and mechanical flexibility, which can be obtained through laser irradiation of carbon-rich precursors. This article introduces a novel flexible tactile sensor based on LIG/multiwalled carbon nanotube (MWCNT) composites. The surface is treated with spraying SiO2 and precured Ecoflex to form superhydrophobic property with water contact angle (WCA) of 153.4°, which is suitable for underwater condition. Enhanced with deep learning algorithms, the LIG/MWCNT sensors are integrated into a data glove, enabling precise recognition of Arabic sign language gestures with accuracy of 99.34%. The data glove equipped with the sensor can communicate wirelessly with a smartphone App via Bluetooth low energy (BLE) by a low-power and secure wireless protocol. The App displays the finger bending status and the recognized gestures in real time, and also provides feedback to the user. This work showcases the feasibility of the proposed sensing system for gesture recognition and the potential applications in underwater wearable electronics. Based on the excellent performance of the proposed sensing system, this article contributes to advancing the interdisciplinary research field of carbon-based flexible electronics and deep learning-based sensing applications.
研究领域:
材料科学
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电子工程
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人工智能
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产品关联
DOI
高影响因子
SENSORS AND ACTUATORS B-CHEMICAL
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2024
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IF 8
Haoyu Dong,Shuang Lin,Guoqiang Fang,Yiran Liu,Huachuang Li,Li Wang
Metal film over nanosphere (MFON) as a classical SERS-active substrate has attracted numerous attention and applications due to its excellent sensitivity and signal uniformity. However, such MFON substrates prepared by metal deposition require strictly controlled experimental conditions with high cost to obtain high batch reproducibility. In addition, the precise adjustment of the metal particle fineness and crystal facet is hard to conduct, limiting the SERS signal for further improvement. In this work, we propose multifaceted Au nanocrystal over nanosphere (AuNCFON) structures based on a facile and robust interfacial assembly technique. Notably, the preparation method could reduce significant loss of metallic materials without the requirement of large-scale instrumentation, and allows precise control of the metal film thickness and the nanocrystal facet. Specifically, the size and facet of nanocrystals on PS spheres are systematically regulated and the Au dodecahedral films with {110} facet on PS arrays exhibit optimal SERS activity. The SERS intensity is amplified by an enhancement factor of 0.13 × 10 7 with excellent signal homogeneity and batch reproducibility. Finally, the sensitive and accurate distinction of similarly structured molecules including crystal violet and malachite green (fungicides) is demonstrated assisted by principal component analysis-partial least squares (PCA-PLS) algorithm. The AuNCFON SERS substrate combined with deep learning analysis has a broad application prospect in reliable identification of chemical pollutants.
研究领域:
材料科学
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分析化学
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环境科学
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产品关联
DOI
高影响因子
AICHE JOURNAL
·
2023
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IF 3.7
Tuo Yao,Jian Liu,Xuxing Wan,Beibei Li,Sohrab Rohani,Zhenguo Gao,Junbo Gong
In situ monitoring and closed-loop control of the solution crystallization process are the modern trends for pharmaceutical development, in which the critical process parameters (CPPs) as well as the product critical quality attributes (CQAs) can be regulated and guaranteed during the manufacturing process. In this study, an in situ image monitoring methodology based on a state-of-the-art deep-learning model was developed to track the CQAs such as polymorph ratio, two-dimensional crystal size, and crystal shape in a solvent-mediated polymorphic transformation (SMPT) process. Coupled with the multidimensional process information, a 2D population balance model (PBM) was developed and validated using the results of the in situ image-based CQAs analysis. The 2D-PBM was solved using a high-resolution finite volume method (HR-FVM) which could provide a high dimensional particle-size distribution. Through the validation between the process image analysis and the 2D-PBM, the accuracy of image analysis was discussed, and the potential and challenges of in situ image analysis were proposed. This work aims to integrate the crystal polymorphism and two-dimensional crystal size distribution (2D-CSD) information in the SMPT process using intelligent microscopic image analysis and then to validate the results of neural network processing by solving the numerical solution of the multidimensional PBM.
研究领域:
制药工程
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化学工程
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人工智能应用
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产品关联
DOI
高影响因子
InfoMat
·
2024
·Vol. 6
·期 5
·e12534
·
IF 22.7
Yiping Zhang,Chengyu Li,Chuanhui Wei,Renwei Cheng,Tianmei Lv,Junpeng Wang,Cong Zhao,Zhaoyang Wang,Fangming Li,Xiao Peng,Minyi Xu,Kai Dong
The inherent unpredictability of the maritime environment leads to low rates of survival during accidents. Life jackets serve as a crucial safety measure in underwater environments. Nonetheless, most conventional life jackets lack the capability to monitor the wearer's underwater body movements, impeding their effectiveness in rescue operations. Here, we present an intelligent self-powered life jacket system (SPLJ) composed of a wireless body area sensing network, a set of deep learning analytics, and a human condition detection platform. Six coaxial core-shell structure triboelectric fiber sensors with high sensitivity, stretchability, and flexibility are integrated into this system. Additionally, a portable integrated circuit module is incorporated into the SPLJ to facilitate real-time monitoring of the wearer's movement. Moreover, by leveraging the deep-learning-assisted data analytics and establishing a robust correlation between the wearer's movements and condition, we have developed a comprehensive system for monitoring drowning individuals, achieving an outstanding recognition accuracy of 100%. This groundbreaking work introduces a fresh approach to underwater intelligent survival devices, offering promising prospects for advancing underwater smart wearable devices in rescue operations and the development of ocean industry.
研究领域:
生物医学工程
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智能穿戴设备
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海洋科技
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产品关联
DOI
高影响因子
危险品化学品经营许可证（带存储）