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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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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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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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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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Accurate detection of weeds is a key technology for developing automated weeding equipment. To address the problems of high detection complexity and poor robustness resulting from the complex distribution and variety of weeds, we proposed a weed detection approach for vegetable seedling based on the improved YOLOv5 algorithm and image processing, implemented on a self-developed mobile robot platform. The weed detection complexity was reduced by indirectly detecting weeds through identifying vegetables, thus improving the detection accuracy and robustness. The convolutional block attention module (CBAM) attention module was added to the backbone feature extraction network of the YOLOv5 object detection algorithm to enhance the focus of the network on vegetable targets, and the Transformer module was added to enhance the global information capture capability. The results showed that the average detection accuracy of the improved YOLOv5 algorithm for vegetable targets could reach 95.7%, which was increased by 5.8%, 6.9%, 10.3%, 13.1%, 9.0%, 5.2%, and 3.2% compared with Faster R-CNN, SSD, EfficientDet, RetinaNet, YOLOv3, YOLOv4, and YOLOv5, respectively. The average detection time of the algorithm for a single run was 11 ms, indicating good real-time performance. The method defined green plants outside the vegetable border as weeds, and combined the extreme green (ExG) with the OTSU threshold segmentation method to segment weeds from the soil background. Finally, the weed connectivity domain was marked, followed by outputting the weed plasmids and detection frames. The proposed method could provide a technical reference for automated precision weeding in agriculture. © 2023, Editorial of Board of Journal of Graphics. All rights reserved.
Keyword ：
attention mechanism attention mechanism vegetable identification vegetable identification weed detection weed detection weeding robot weeding robot YOLOv5 YOLOv5
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| GB/T 7714 | Zhang, W.-K. , Sun, H. , Chen, X.-K. et al. Research on weed detection in vegetable seedling fields based on the improved YOLOv5 intelligent weeding robot [J]. | Journal of Graphics , 2023 , 44 (2) : 346-356 . |
| MLA | Zhang, W.-K. et al. "Research on weed detection in vegetable seedling fields based on the improved YOLOv5 intelligent weeding robot" . | Journal of Graphics 44 . 2 (2023) : 346-356 . |
| APA | Zhang, W.-K. , Sun, H. , Chen, X.-K. , Li, X.-B. , Yao, L.-G. , Dong, H. . Research on weed detection in vegetable seedling fields based on the improved YOLOv5 intelligent weeding robot . | Journal of Graphics , 2023 , 44 (2) , 346-356 . |
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A highly efficient coupling of glycosyl stannanes and sulfonium salts enabled by synergistic Pd/Cu catalysis is disclosed, facilitating the construction of C -aryl/alkenyl glycals under mild conditions in high yields. The protocol tolerates a wide scope of functional groups including ketone, cyano, ester, amide, nitro, halide. The one-pot formal C -H glycosylation starting from arene is demonstrated with a reaction sequence of dibenzothiophenylation/Stille coupling. Besides, a gram-scale reaction is performed successfully, showing the high applicability of this protocol.(c) 2023 Published by Elsevier B.V. on behalf of Chinese Chemical Society and Institute of Materia Medica, Chinese Academy of Medical Sciences.
Keyword ：
Carbohydrate Carbohydrate C-glycals C-glycals Glycosyl stannanes Glycosyl stannanes Stille coupling Stille coupling Sulfonium salts Sulfonium salts Synergistic catalysis Synergistic catalysis
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| GB/T 7714 | Yan, Weitao , Zheng, Mingwen , Chuang, Peihsuan et al. Synergistic Pd/Cu catalysis enabled cross-coupling of glycosyl stannanes with sulfonium salts to access C -aryl/alkenyl glycals [J]. | CHINESE CHEMICAL LETTERS , 2023 , 34 (7) . |
| MLA | Yan, Weitao et al. "Synergistic Pd/Cu catalysis enabled cross-coupling of glycosyl stannanes with sulfonium salts to access C -aryl/alkenyl glycals" . | CHINESE CHEMICAL LETTERS 34 . 7 (2023) . |
| APA | Yan, Weitao , Zheng, Mingwen , Chuang, Peihsuan , Sun, Hao , Wang, Shiping , Xu, Chunfa et al. Synergistic Pd/Cu catalysis enabled cross-coupling of glycosyl stannanes with sulfonium salts to access C -aryl/alkenyl glycals . | CHINESE CHEMICAL LETTERS , 2023 , 34 (7) . |
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杂草精准检测是自动化除草装备的关键技术。针对田间杂草分布复杂和种类繁多导致的检测复杂度高和鲁棒性差等问题，基于自研移动机器人平台，提出一种改进YOLOv5算法和图像处理的蔬菜苗田杂草检测方法。通过识别蔬菜间接检测杂草的方式降低杂草检测复杂度，进而提高检测精度和鲁棒性。在YOLOv5目标检测算法主干特征提取网络中引入卷积块注意力模块(CBAM)提高网络对蔬菜目标的关注度，加入Transformer模块增强模型对全局信息的捕捉能力。结果表明，改进YOLOv5算法对蔬菜目标的平均检测准确率可达95.7%，与Faster R-CNN,SSD,EfficientDet,RetinaNet,YOLOv3,YOLOv4和YOLOv5算法相比，分别提高了5.8%,6.9%,10.3%,13.1%,9.0%,5.2%和3.2%。算法单幅图像平均检测时间11 ms，具有较好的实时性。采用改进YOLOv5算法检测蔬菜，将蔬菜边框之外绿色植物定义为杂草，超绿特征(ExG)结合OTSU阈值分割法将杂草与土壤背景分割，最后标记杂草连通域输出杂草质心和检测框。本研究方法可为农业自动化精准除草提供借鉴。
Keyword ：
YOLOv5 YOLOv5 杂草检测 杂草检测 注意力机制 注意力机制 蔬菜识别 蔬菜识别 除草机器人 除草机器人
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| GB/T 7714 | 张伟康 , 孙浩 , 陈鑫凯 et al. 基于改进YOLOv5的智能除草机器人蔬菜苗田杂草检测研究 [J]. | 图学学报 , 2023 , 44 (02) : 346-356 . |
| MLA | 张伟康 et al. "基于改进YOLOv5的智能除草机器人蔬菜苗田杂草检测研究" . | 图学学报 44 . 02 (2023) : 346-356 . |
| APA | 张伟康 , 孙浩 , 陈鑫凯 , 李叙兵 , 姚立纲 , 东辉 . 基于改进YOLOv5的智能除草机器人蔬菜苗田杂草检测研究 . | 图学学报 , 2023 , 44 (02) , 346-356 . |
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提出一种基于 2D先验的 3D目标判定算法.首先用轻量级MobileNet网络替换经典SSD的VGG-16 网络,构建出MobileNet-SSD目标检测模型;其次,通过改进网络结构,提高模型对小目标的检测能力,并引入Focal Loss函数来解决正负样本不均衡和易分样本占比较高的问题;在相同数据集上,将改进算法与Faster R-CNN、YOLOv3 及MobileNet-SSD进行对比测试,其平均精度mAP分别提高了 7.2％、8.8％和 10.6％;最后,通过改进算法获取ROI,利用深度相机将二维ROI转换为ROI点云,并借助直通滤波来判断目标物体是否为真实场景物体,既省去了传统点云识别中的诸多步骤又避免了点云深度学习中三维数据集制作难度较大的问题,在识别速度和识别精度上达到了较好的平衡.
Keyword ：
MobileNet网络 MobileNet网络 SSD SSD 点云识别 点云识别 目标检测 目标检测
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| GB/T 7714 | 东辉 , 解振宁 , 孙浩 et al. 基于2D先验的3D目标判定算法 [J]. | 福州大学学报（自然科学版） , 2023 , 51 (3) : 387-394 . |
| MLA | 东辉 et al. "基于2D先验的3D目标判定算法" . | 福州大学学报（自然科学版） 51 . 3 (2023) : 387-394 . |
| APA | 东辉 , 解振宁 , 孙浩 , 陈炳兴 , 姚立纲 . 基于2D先验的3D目标判定算法 . | 福州大学学报（自然科学版） , 2023 , 51 (3) , 387-394 . |
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During global outbreaks such as COVID-19, regular nucleic acid amplification tests (NAATs) have posed unprecedented burden on hospital resources. Data of traditional NAATs are manually analyzed post assay. Integration of artificial intelligence (AI) with on-chip assays give rise to novel analytical platforms via data-driven models. Here, we combined paper microfluidics, portable optoelectronic system with deep learning for SARSCoV-2 detection. The system was quite streamlined with low power dissipation. Pixel by pixel signals reflecting amplification of synthesized SARS-CoV-2 templates (containing ORF1ab, N and E genes) can be real-time processed. Then, the data were synchronously fed to the neural networks for early prediction analysis. Instead of the quantification cycle (Cq) based analytics, reaction dynamics hidden at the early stage of amplification curve were utilized by neural networks for predicting subsequent data. Qualitative and quantitative analysis of the 40-cycle NAATs can be achieved at the end of 22nd cycle, reducing time cost by 45%. In particular, the attention mechanism based deep learning model trained by microfluidics-generated data can be seamlessly adapted to multiple clinical datasets including readouts of SARS-CoV-2 detection. Accuracy, sensitivity and specificity of the prediction can reach up to 98.1%, 97.6% and 98.6%, respectively. The approach can be compatible with the most advanced sensing technologies and AI algorithms to inspire ample innovations in fields of fundamental research and clinical settings.
Keyword ：
COVID-19 diagnosis COVID-19 diagnosis Deep learning Deep learning NAAT NAAT Paper microfluidics Paper microfluidics
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| GB/T 7714 | Sun, Hao , Xie, Wantao , Huang, Yi et al. Paper microfluidics with deep learning for portable intelligent nucleic acid amplification tests [J]. | TALANTA , 2023 , 258 . |
| MLA | Sun, Hao et al. "Paper microfluidics with deep learning for portable intelligent nucleic acid amplification tests" . | TALANTA 258 (2023) . |
| APA | Sun, Hao , Xie, Wantao , Huang, Yi , Mo, Jin , Dong, Hui , Chen, Xinkai et al. Paper microfluidics with deep learning for portable intelligent nucleic acid amplification tests . | TALANTA , 2023 , 258 . |
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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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