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学者姓名：孙浩
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Abstract ：
Human beings encompass sophisticated microcirculation and microenvironments, incorporating a broad spectrum of microfluidic systems that adopt fundamental roles in orchestrating physiological mechanisms. In vitro recapitulation of human microenvironments based on lab-on-a-chip technology represents a critical paradigm to better understand the intricate mechanisms. Moreover, the advent of micro/nanorobotics provides brand new perspectives and dynamic tools for elucidating the complex process in microfluidics. Currently, artificial intelligence (AI) has endowed micro/nanorobots (MNRs) with unprecedented benefits, such as material synthesis, optimal design, fabrication, and swarm behavior. Using advanced AI algorithms, the motion control, environment perception, and swarm intelligence of MNRs in microfluidics are significantly enhanced. This emerging interdisciplinary research trend holds great potential to propel biomedical research to the forefront and make valuable contributions to human health. Herein, we initially introduce the AI algorithms integral to the development of MNRs. We briefly revisit the components, designs, and fabrication techniques adopted by robots in microfluidics with an emphasis on the application of AI. Then, we review the latest research pertinent to AI-enhanced MNRs, focusing on their motion control, sensing abilities, and intricate collective behavior in microfluidics. Furthermore, we spotlight biomedical domains that are already witnessing or will undergo game-changing evolution based on AI-enhanced MNRs. Finally, we identify the current challenges that hinder the practical use of the pioneering interdisciplinary technology. Although developed independently at the beginning, AI, micro/nanorobots and microfluidics have become more intertwined in the past few years which has greatly propelled the cutting-edge development in fields of biomedical sciences.
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| GB/T 7714 | Dong, Hui , Lin, Jiawen , Tao, Yihui et al. AI-enhanced biomedical micro/nanorobots in microfluidics [J]. | LAB ON A CHIP , 2024 , 24 (5) . |
| MLA | Dong, Hui et al. "AI-enhanced biomedical micro/nanorobots in microfluidics" . | LAB ON A CHIP 24 . 5 (2024) . |
| APA | Dong, Hui , Lin, Jiawen , Tao, Yihui , Jia, Yuan , Sun, Lining , Li, Wen Jung et al. AI-enhanced biomedical micro/nanorobots in microfluidics . | LAB ON A CHIP , 2024 , 24 (5) . |
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Soft robots, inspired by living organisms in nature, are primarily made of soft materials, and can be used to perform delicate tasks due to their high flexibility, such as grasping and locomotion. However, it is a challenge to efficiently manufacture soft robots with complex functions. In recent years, 3D printing technology has greatly improved the efficiency and flexibility of manufacturing soft robots. Unlike traditional subtractive manufacturing technologies, 3D printing, as an additive manufacturing method, can directly produce parts of high quality and complex geometry for soft robots without manual errors or costly post-processing. In this review, we investigate the basic concepts and working principles of current 3D printing technologies, including stereolithography, selective laser sintering, material extrusion, and material jetting. The advantages and disadvantages of fabricating soft robots are discussed. Various 3D printing materials for soft robots are introduced, including elastomers, shape memory polymers, hydrogels, composites, and other materials. Their functions and limitations in soft robots are illustrated. The existing 3D-printed soft robots, including soft grippers, soft locomotion robots, and wearable soft robots, are demonstrated. Their application in industrial, manufacturing, service, and assistive medical fields is discussed. We summarize the challenges of 3D printing at the technical level, material level, and application level. The prospects of 3D printing technology in the field of soft robots are explored.
Keyword ：
3D printing 3D printing additive manufacturing additive manufacturing soft materials soft materials soft robots soft robots
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| GB/T 7714 | Dong, Hui , Weng, Tao , Zheng, Kexin et al. Review: Application of 3D Printing Technology in Soft Robots [J]. | 3D PRINTING AND ADDITIVE MANUFACTURING , 2024 , 11 (3) : 954-976 . |
| MLA | Dong, Hui et al. "Review: Application of 3D Printing Technology in Soft Robots" . | 3D PRINTING AND ADDITIVE MANUFACTURING 11 . 3 (2024) : 954-976 . |
| APA | Dong, Hui , Weng, Tao , Zheng, Kexin , Sun, Hao , Chen, Bingxing . Review: Application of 3D Printing Technology in Soft Robots . | 3D PRINTING AND ADDITIVE MANUFACTURING , 2024 , 11 (3) , 954-976 . |
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Microfluidic technology facilitates high-throughput generation of time series data for biological and medical studies. Deep learning enables accurate, predictive analysis and proactive decision-making based on autonomous recognition of intricate pattern hidden in series. In this work, we first devised a paper-based microfluidic system for portable nucleic acid amplification test with economic energy consumption. Then, we employed Graph Neural Network (GNN), distinguished by its non-Euclidean data structure tailored for deep learning, with spatiotemporal attention mechanism to perform near-sensor predictive analysis of the on-chip reaction. Our findings demonstrated that the novel GNN model can provide accurate predictions of positive outcomes at the early stages of the reaction using less than one-third of the total reaction time. Then, the deep learning model trained by onchip data was subsequently applied to more than 900 clinical plots. Generalization of the GNN model was successfully validated across different detection methods, diverse types of datasets and time series with variable length. Accuracy, sensitivity and specificity of the predictive approach were 96.5 %, 94.3 % and 99.0 % by utilizing the early half of reaction information. Finally, we compared the GNN model with various deep learning models. Despite differences in the prediction of negative samples among various models were minute, GNN obviously offered overall superior performance. This work ignites a cutting-edge application of deep learning in point-of-care and near-sensor tests. By harnessing the power of body area networks and edge/fog computing, our approach unlocks promising possibilities in diverse fields like healthcare and instrument science.
Keyword ：
GNN GNN Microfluidics Microfluidics Nucleic acid amplification test Nucleic acid amplification test Predictive analysis Predictive analysis
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| GB/T 7714 | Sun, Hao , Pan, Yihan , Dong, Hui et al. Generalized predictive analysis of reactions in paper devices via graph neural networks [J]. | SENSORS AND ACTUATORS B-CHEMICAL , 2024 , 417 . |
| MLA | Sun, Hao et al. "Generalized predictive analysis of reactions in paper devices via graph neural networks" . | SENSORS AND ACTUATORS B-CHEMICAL 417 (2024) . |
| APA | Sun, Hao , Pan, Yihan , Dong, Hui , Liu, Canfeng , Yang, Jintian , Tao, Yihui et al. Generalized predictive analysis of reactions in paper devices via graph neural networks . | SENSORS AND ACTUATORS B-CHEMICAL , 2024 , 417 . |
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Automated of gas and liquid classification technologies are of great in multiple fields including food production and human healthcare. Of these, fruit juice contains water, organic acids, minerals and other nutrients which offers a pleasant taste and promotes healthy condition. However, the main challenges faced by conventional components sensing technologies for juice classification are limited to the complexity of experimental preparation, bulky instrument, high consumption and susceptibility to contamination. Moisture Electricity Generation (MEG) technology has made it feasible to acquire energy from trace amounts of water or environmental humidity. This work proposes a novel sensing unit based on MEG technology. The unit mainly comprises non-woven fabric, hydroxylated carbon nanotubes, polyvinyl alcohol, a solution of sea salt and liquid alloy. By this approach, humid air (relative humidity 60%), pure water and juices from three fruits (lemon, kiwifruit, and clementine) have been successfully classified in 15 seconds. The classification accuracy can reach 90%. Electrical signals standard lines highlight the specific response between samples. The relative standard deviation of stable output section is 1.6% and the root-mean-square error between test data and the standard curve is less than 0.08, which indicates the stability, accuracy are fine. Besides, the sensing unit demonstrates an acceptable reusability. The presented approach may provide opportunities to improve sensing paradigms in industrial and medical settings.
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| GB/T 7714 | Lin, Jiawen , Dong, Hui , Yang, Jintian et al. A Novel Fluid Classification Unit Based on Moisture Electricity Generation Mechanism [J]. | NANO SENSORS FOR AI, HEALTHCARE, AND ROBOTICS, NSENS , 2024 : 76-80 . |
| MLA | Lin, Jiawen et al. "A Novel Fluid Classification Unit Based on Moisture Electricity Generation Mechanism" . | NANO SENSORS FOR AI, HEALTHCARE, AND ROBOTICS, NSENS (2024) : 76-80 . |
| APA | Lin, Jiawen , Dong, Hui , Yang, Jintian , Jia, Haichao , Li, Minglin , Yao, Ligang et al. A Novel Fluid Classification Unit Based on Moisture Electricity Generation Mechanism . | NANO SENSORS FOR AI, HEALTHCARE, AND ROBOTICS, NSENS , 2024 , 76-80 . |
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Bioprinting holds the promise of producing biocompatible structures capable of seamlessly integrating with human physiology, improving human health by enabling the precise fabrication of tissue models that closely mimic the architecture and functions of human skin, brain, and bone. Building on the advancements of bioprinting, there has been a corresponding increase in cross-disciplinary innovations in wearable technologies, brain-machine interfaces, and exoskeleton robotics. Given the progress of bioprinting in skin study, wearable electronics are expected to have improved biocompatibility and integration with the human body. For patient-specific neural tissues created using bioprinting, the potential to replicate neural activities through the synergy of bioprinting and brain-machine interfaces presents opportunities to enhance the performance of more advanced neuromorphic systems. Inspired by the advancements of bioprinting in producing patient-specific bone grafts and scaffolds, this technology could bridge the gap between mechanical systems and biomechanics, redefining the limits of skeleton robotics. This review explores the advancements of bioprinting in wearable sensors, brain-machine interfaces, and exoskeleton robots, and briefly addresses the existing and potential challenges in interdisciplinary research. © 2024 Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution License, permitting distribution, and reproduction in any medium, provided the original work is properly cited.
Keyword ：
Bioprinting Bioprinting Brain-machine interface Brain-machine interface Exoskeleton robot Exoskeleton robot Wearable sensor Wearable sensor
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| GB/T 7714 | Wang, X. , Dong, W. , Dong, H. et al. Bioprinting of wearable sensors, brain-machine interfaces, and exoskeleton robots [J]. | International Journal of Bioprinting , 2024 , 10 (6) : 16-37 . |
| MLA | Wang, X. et al. "Bioprinting of wearable sensors, brain-machine interfaces, and exoskeleton robots" . | International Journal of Bioprinting 10 . 6 (2024) : 16-37 . |
| APA | Wang, X. , Dong, W. , Dong, H. , Gao, Y. , Lin, J. , Jia, H. et al. Bioprinting of wearable sensors, brain-machine interfaces, and exoskeleton robots . | International Journal of Bioprinting , 2024 , 10 (6) , 16-37 . |
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With the development of medical diagnosis and treatment intervention techniques, there has been an exponential growth in medical data along time series. Artificial intelligence (AI), particularly deep learning (DL), has demonstrated significant potential in uncovering medical data along time series. This study proposed, for the first time, a method that integrates the Transformer architecture with the Kolmogorov-Arnold network (KAN) to enable predictive analysis of nucleic acid amplification experimental data. Through experimental data analysis methods, the effectiveness of the model in accurately predicting amplification trends and endpoint values was validated, achieving an endpoint value error of merely 1.87 and an R-square coefficient as high as 0.98. Moreover, the model was capable of effectively identifying experimental data from different sample types. Furthermore, this research delved into the impact of the model’s components and parameters on predictive performance through ablation experiments and hyperparameter tuning. Finally, a generalization capability test was conducted on 911 clinical data records provided by the Fujian Provincial Hospital across 10 deep learning models. The results demonstrated that the proposed Transformer-KAN network outperformed other models in terms of predictive accuracy and generalization capability. This study not only provided a new perspective for improving routine diagnostic techniques during pandemics but also offered empirical evidence for further research on the KAN model and its corresponding foundational theories. © 2024 Editorial of Board of Journal of Graphics. All rights reserved.
Keyword ：
deep learning deep learning Kolmogorov-Arnold network Kolmogorov-Arnold network nucleic acid amplification test nucleic acid amplification test time series prediction time series prediction Transformer Transformer
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| GB/T 7714 | Liu, C. , Sun, H. , Dong, H. . Molecular amplification time series prediction research combining Transformer with Kolmogorov-Arnold network; [结合 Transformer 与 Kolmogorov Arnold 网络的分子扩增时序预测研究] [J]. | Journal of Graphics , 2024 , 45 (6) : 1256-1265 . |
| MLA | Liu, C. et al. "Molecular amplification time series prediction research combining Transformer with Kolmogorov-Arnold network; [结合 Transformer 与 Kolmogorov Arnold 网络的分子扩增时序预测研究]" . | Journal of Graphics 45 . 6 (2024) : 1256-1265 . |
| APA | Liu, C. , Sun, H. , Dong, H. . Molecular amplification time series prediction research combining Transformer with Kolmogorov-Arnold network; [结合 Transformer 与 Kolmogorov Arnold 网络的分子扩增时序预测研究] . | Journal of Graphics , 2024 , 45 (6) , 1256-1265 . |
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Classifications of fluids using miniaturized sensors are of substantial importance for various fields of application. Modified with functional nanomaterials, a moisture-enabled electricity generation (MEG) device can execute a dual-purpose operation as both a self-powered framework and a fluid detection platform. In this study, a novel intelligent self-sustained sensing approach was implemented by integrating MEG with deep learning in microfluidics. Following a multilayer design, the MEG device including three individual units for power generation/fluid classification was fabricated in this study by using nonwoven fabrics, hydroxylated carbon nanotubes, poly(vinyl alcohol)-mixed gels, and indium tin bismuth liquid alloy. A composite configuration utilizing hydrophobic microfluidic channels and hydrophilic porous substrates was conducive to self-regulation of the on-chip flow. As a generator, the MEG device was capable of maintaining a continuous and stable power output for at least 6 h. As a sensor, the on-chip units synchronously measured the voltage (V), current (C), and resistance (R) signals as functions of time, whose transitions were completed using relays. These signals can serve as straightforward indicators of a fluid presence, such as the distinctive "fingerprint". After normalization and Fourier transform of raw V/C/R signals, a lightweight deep learning model (wide-kernel deep convolutional neural network, WDCNN) was employed for classifying pure water, kiwifruit, clementine, and lemon juices. In particular, the accuracy of the sample distinction using the WDCNN model was 100% within 15 s. The proposed integration of MEG, microfluidics, and deep learning provides a novel paradigm for the development of sustainable intelligent environmental perception, as well as new prospects for innovations in analytical science and smart instruments.
Keyword ：
deep learning deep learning fluid classification fluid classification moisture-enabled electricity generation moisture-enabled electricity generation self-sustainedsensing self-sustainedsensing V/C/R signals V/C/R signals
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| GB/T 7714 | Lin, Jiawen , Dong, Hui , Cui, Shilong et al. Fluid Classification via the Dual Functionality of Moisture-Enabled Electricity Generation Enhanced by Deep Learning [J]. | ACS APPLIED MATERIALS & INTERFACES , 2024 , 16 (46) : 63723-63734 . |
| MLA | Lin, Jiawen et al. "Fluid Classification via the Dual Functionality of Moisture-Enabled Electricity Generation Enhanced by Deep Learning" . | ACS APPLIED MATERIALS & INTERFACES 16 . 46 (2024) : 63723-63734 . |
| APA | Lin, Jiawen , Dong, Hui , Cui, Shilong , Dong, Wei , Sun, Hao . Fluid Classification via the Dual Functionality of Moisture-Enabled Electricity Generation Enhanced by Deep Learning . | ACS APPLIED MATERIALS & INTERFACES , 2024 , 16 (46) , 63723-63734 . |
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Hollow nanomaterials have attracted significant interest. However, developing an effective growth mechanism for synthesizing seed-like hollow nanoparticles (SHNPs) with tailored structures still remains challenging. In this study, we developed a dynamic interfacial-tension-controlled polar growth strategy to synthesize SHNPs with a narrow size distribution using a metal-organic coordination compound. The synthesis was performed in an oil-in-water emulsion system comprising disulfiram (DSF) oil nanodroplets and cis-dichlorodiamine platinum-(II) (Cpt) aqueous phases. The DSF nanodroplets exhibited dynamic interfacial tension owing to the gradual consumption of DSF molecules, resulting in the polar growth of DSF nanodroplets from a spherical to anisotropic seed-like morphology. This method produced seed-like hollow nanostructures with tailored morphologies, such as pomegranate, peanut, bean sprout, and pistachio structures and desired lengths. Additionally, we constructed a seed-like nanomotor by loading small platinum (Pt) nanoparticles onto the surface of SHNPs, which exhibit an enhanced diffusion coefficient and exceptional oriented movement in response to the hydrogen peroxide (H2O2) fuel.
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| GB/T 7714 | Dang, Meng , Yu, Ruifa , Han, Xiaolin et al. Seed-like Hollow Nanoparticles by a Dynamic Interfacial-Tension-Controlled Polar Growth Strategy [J]. | CHEMISTRY OF MATERIALS , 2023 , 35 (24) : 10542-10549 . |
| MLA | Dang, Meng et al. "Seed-like Hollow Nanoparticles by a Dynamic Interfacial-Tension-Controlled Polar Growth Strategy" . | CHEMISTRY OF MATERIALS 35 . 24 (2023) : 10542-10549 . |
| APA | Dang, Meng , Yu, Ruifa , Han, Xiaolin , Shao, Lixin , Zhao, Jiajia , Ding, Zhi et al. Seed-like Hollow Nanoparticles by a Dynamic Interfacial-Tension-Controlled Polar Growth Strategy . | CHEMISTRY OF MATERIALS , 2023 , 35 (24) , 10542-10549 . |
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Pandemics such as COVID-19 have exposed global inequalities in essential health care. Here, we proposed a novel analytics of nucleic acid amplification tests (NAATs) by combining paper microfluidics with deep learning and cloud computing. Real-time amplifications of synthesized SARS-CoV-2 RNA templates were performed in paper devices. Information pertained to on-chip reactions in time-series format were transmitted to cloud server on which deep learning (DL) models were preloaded for data analysis. DL models enable prediction of NAAT results using partly gathered real-time fluorescence data. Using information provided by the G-channel, accurate prediction can be made as early as 9 min, a 78% reduction from the conventional 40 min mark. Reaction dy-namics hidden in amplification curves were effectively leveraged. Positive and negative samples can be unbiasedly and automatically distinguished. Practical utility of the approach was validated by cross-platform study using clinical datasets. Predicted clinical accuracy, sensitivity and specificity were 98.6%, 97.6% and 99.1%. Not only the approach reduced the need for the use of bulky apparatus, but also provided intelligent, distributable and robotic insights for NAAT analysis. It set a novel paradigm for analyzing NAATs, and can be combined with the most cutting-edge technologies in fields of biosensor, artificial intelligence and cloud computing to facilitate fundamental and clinical research.
Keyword ：
Cloud computing Cloud computing Deep learning Deep learning NAAT NAAT Paper microfluidics Paper microfluidics Time-series forecasting Time-series forecasting
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| GB/T 7714 | Sun, Hao , Jiang, Qinghua , Huang, Yi et al. Integrated smart analytics of nucleic acid amplification tests via paper microfluidics and deep learning in cloud computing [J]. | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2023 , 83 . |
| MLA | Sun, Hao et al. "Integrated smart analytics of nucleic acid amplification tests via paper microfluidics and deep learning in cloud computing" . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL 83 (2023) . |
| APA | Sun, Hao , Jiang, Qinghua , Huang, Yi , Mo, Jin , Xie, Wantao , Dong, Hui et al. Integrated smart analytics of nucleic acid amplification tests via paper microfluidics and deep learning in cloud computing . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2023 , 83 . |
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In situ bioprinting has emerged as a promising technology for tissue and organ engineering based on the precise positioning of living cells, growth factors, and biomaterials. Rather than traditional in vitro reconstruction and recapitulation of tissue or organ models, the in situ technology can directly print on specific anatomical positions in living bodies. The requirements for biological activity, function, and mechanical property in an in vivo setting are more complex. By combining progressive innovations of biomaterials, tissue engineering, and digitalization, especially robotics, in situ bioprinting has gained significant interest from the academia and industry, demonstrating its prospect for clinical studies. This article reviews the progress of in situ bioprinting, with an emphasis on roboticassisted studies. The main modalities for in situ three-dimensional bioprinting, which include extrusion-based printing, inkjet printing, laser-based printing, and their derivatives, are briefly introduced. These modalities have been integrated with various custom-tailored printers (i.e., end effectors) mounted on robotic arms for dexterous and precision biofabrication. The typical prototypes based on various robot configurations, including Cartesian, articulated, and parallel mechanisms, for in situ bioprinting are discussed and compared. The conventional and most recent applications of robotic-assisted methods for in situ fabrication of tissue and organ models, including cartilage, bone, and skin, are also elucidated, followed by a discussion on the existing challenges in this field with their corresponding suggestions.
Keyword ：
In situ bioprinting In situ bioprinting Robot configurations Robot configurations Robotic -assisted bioprinting Robotic -assisted bioprinting
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| GB/T 7714 | Dong, Hui , Hu, Bo , Zhang, Weikang et al. Robotic-assisted automated in situ bioprinting [J]. | INTERNATIONAL JOURNAL OF BIOPRINTING , 2023 , 9 (1) : 98-108 . |
| MLA | Dong, Hui et al. "Robotic-assisted automated in situ bioprinting" . | INTERNATIONAL JOURNAL OF BIOPRINTING 9 . 1 (2023) : 98-108 . |
| APA | Dong, Hui , Hu, Bo , Zhang, Weikang , Xie, Wantao , Mo, Jin , Sun, Hao et al. Robotic-assisted automated in situ bioprinting . | INTERNATIONAL JOURNAL OF BIOPRINTING , 2023 , 9 (1) , 98-108 . |
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