Jing Li, Tianyu Wu, Huan Jiang, Yanyu Chen, Qibiao Yang
Pressure sensitivity and wide range are two crucial features of flexible electromechanical sensors for applications in the next‐generation of intelligent electronics, such as wearable healthcare monitors and soft human–machine interfaces. Conventional pressure sensors have a narrow pressure range (<10 kPa) and complex fabrication processes, which significantly hinder their extensive applications. A facile laser‐engraving method is proposed to fabricate a flexible multiwalled‐carbon‐nanotube (MWCNTs)/poly(dimethylsiloxane) (PDMS) composite‐based piezoresistive sensor with hierarchical microstructures. Herein, the nonstandard‐circular feature and Gaussian distributed facula of a laser spot are utilized to produce the middle‐level porous microdome upon the bottom‐level cylinder microcolumn array, while the top‐level tentacle‐like conical micropillars are produced by vertically rotating the acrylic mold during the laser engraving process. This novel hierarchical microstructure endows the proposed piezoresistive sensor with orders‐of‐magnitude of higher sensitivity (≈35.51 kPa−1) than that of other reported electromechanical sensors and a more extensive pressure sensing range up to 23 kPa. Moreover, the detection limit of the sensor is down to 2 Pa, which makes it a desirable candidate for monitoring subtle pressure. The sensor is successfully applied to distinguish the syllables of each pronounced word, detect movements of the human wrist, and monitor radial arterial pulse, thus demonstrating its promising applications in wearable electronics.
{"title":"Ultrasensitive Hierarchical Piezoresistive Pressure Sensor for Wide‐Range Pressure Detection","authors":"Jing Li, Tianyu Wu, Huan Jiang, Yanyu Chen, Qibiao Yang","doi":"10.1002/aisy.202100070","DOIUrl":"https://doi.org/10.1002/aisy.202100070","url":null,"abstract":"Pressure sensitivity and wide range are two crucial features of flexible electromechanical sensors for applications in the next‐generation of intelligent electronics, such as wearable healthcare monitors and soft human–machine interfaces. Conventional pressure sensors have a narrow pressure range (<10 kPa) and complex fabrication processes, which significantly hinder their extensive applications. A facile laser‐engraving method is proposed to fabricate a flexible multiwalled‐carbon‐nanotube (MWCNTs)/poly(dimethylsiloxane) (PDMS) composite‐based piezoresistive sensor with hierarchical microstructures. Herein, the nonstandard‐circular feature and Gaussian distributed facula of a laser spot are utilized to produce the middle‐level porous microdome upon the bottom‐level cylinder microcolumn array, while the top‐level tentacle‐like conical micropillars are produced by vertically rotating the acrylic mold during the laser engraving process. This novel hierarchical microstructure endows the proposed piezoresistive sensor with orders‐of‐magnitude of higher sensitivity (≈35.51 kPa−1) than that of other reported electromechanical sensors and a more extensive pressure sensing range up to 23 kPa. Moreover, the detection limit of the sensor is down to 2 Pa, which makes it a desirable candidate for monitoring subtle pressure. The sensor is successfully applied to distinguish the syllables of each pronounced word, detect movements of the human wrist, and monitor radial arterial pulse, thus demonstrating its promising applications in wearable electronics.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82463973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marco Fronzi, O. Isayev, D. Winkler, J. Shapter, Amanda V. Ellis, P. Sherrell, N. A. Shepelin, Alexander Corletto, M. Ford
The bandgap is one of the most fundamental properties of condensed matter. However, an accurate calculation of its value, which could potentially allow experimentalists to identify materials suitable for device applications, is very computationally expensive. Here, active machine learning algorithms are used to leverage a limited number of accurate density functional theory calculations to robustly predict the bandgap of a very large number of novel 2D heterostructures. Using this approach, a database of ≈2.2 million bandgap values for various novel 2D van der Waals heterostructures is produced.
{"title":"Active Learning in Bayesian Neural Networks for Bandgap Predictions of Novel Van der Waals Heterostructures","authors":"Marco Fronzi, O. Isayev, D. Winkler, J. Shapter, Amanda V. Ellis, P. Sherrell, N. A. Shepelin, Alexander Corletto, M. Ford","doi":"10.1002/aisy.202100080","DOIUrl":"https://doi.org/10.1002/aisy.202100080","url":null,"abstract":"The bandgap is one of the most fundamental properties of condensed matter. However, an accurate calculation of its value, which could potentially allow experimentalists to identify materials suitable for device applications, is very computationally expensive. Here, active machine learning algorithms are used to leverage a limited number of accurate density functional theory calculations to robustly predict the bandgap of a very large number of novel 2D heterostructures. Using this approach, a database of ≈2.2 million bandgap values for various novel 2D van der Waals heterostructures is produced.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"77 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87302926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Priyanka Sharan, Charlie Maslen, Berk Altunkeyik, I. Rehor, J. Simmchen, T. Montenegro-Johnson
Hydrogels have received increased attention due to their biocompatible material properties, adjustable porosity, ease of functionalization, tuneable shape, and Young's moduli. Initial work has recognized the potential that conferring out‐of‐equilibrium properties to these on the microscale holds and envisions a broad range of biomedical applications. Herein, a simple strategy to integrate multiple swimming modes into catalase‐propelled hydrogel bodies, produced via stop‐flow lithography (SFL), is presented and the different dynamics that result from bubble expulsion are studied. It is found that for “Saturn” filaments, with active poles and an inert midpiece, the fundamental swimming modes correspond to the first three fundamental shape modes that can be obtained by buckling elastic filaments, namely, I, U, and S‐shapes.
{"title":"Fundamental Modes of Swimming Correspond to Fundamental Modes of Shape: Engineering I‐, U‐, and S‐Shaped Swimmers","authors":"Priyanka Sharan, Charlie Maslen, Berk Altunkeyik, I. Rehor, J. Simmchen, T. Montenegro-Johnson","doi":"10.1002/aisy.202100068","DOIUrl":"https://doi.org/10.1002/aisy.202100068","url":null,"abstract":"Hydrogels have received increased attention due to their biocompatible material properties, adjustable porosity, ease of functionalization, tuneable shape, and Young's moduli. Initial work has recognized the potential that conferring out‐of‐equilibrium properties to these on the microscale holds and envisions a broad range of biomedical applications. Herein, a simple strategy to integrate multiple swimming modes into catalase‐propelled hydrogel bodies, produced via stop‐flow lithography (SFL), is presented and the different dynamics that result from bubble expulsion are studied. It is found that for “Saturn” filaments, with active poles and an inert midpiece, the fundamental swimming modes correspond to the first three fundamental shape modes that can be obtained by buckling elastic filaments, namely, I, U, and S‐shapes.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"78 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74121778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Intelligent microrobot systems at the microscopic scale provide enormous opportunities for emerging biomedical and environmental applications. Herein, a multiagent stochastic feedback control framework to control colloidal microrobot swarms for capturing Brownian cargo particles in complex environments such as mazes is proposed. The decision‐making module in the control framework consists of the adaptive generation of target sites surrounding the cargo, optimal target assignment, and approximate motion planning. The stochastic trajectories of robot swarms are efficiently navigated toward their exclusively assigned target around the cargo particle and enable the cargo to be captured. The capture strategy realized by the control framework is robust, adaptive, and flexible in that it accommodates diverse local geometries in the vicinity of a cargo, swarm, and maze sizes and is able to spontaneously split the workforce to catch multiple Brownian cargo particles via multitasking. The present intelligent robot swarm enabled by the multiagent control offers a path to realize complex functions at the microscopic scale in a resilient and flexible manner.
{"title":"Brownian Cargo Capture in Mazes via Intelligent Colloidal Microrobot Swarms","authors":"Kun Xu, Yuguang Yang, Bo Li","doi":"10.1002/aisy.202100115","DOIUrl":"https://doi.org/10.1002/aisy.202100115","url":null,"abstract":"Intelligent microrobot systems at the microscopic scale provide enormous opportunities for emerging biomedical and environmental applications. Herein, a multiagent stochastic feedback control framework to control colloidal microrobot swarms for capturing Brownian cargo particles in complex environments such as mazes is proposed. The decision‐making module in the control framework consists of the adaptive generation of target sites surrounding the cargo, optimal target assignment, and approximate motion planning. The stochastic trajectories of robot swarms are efficiently navigated toward their exclusively assigned target around the cargo particle and enable the cargo to be captured. The capture strategy realized by the control framework is robust, adaptive, and flexible in that it accommodates diverse local geometries in the vicinity of a cargo, swarm, and maze sizes and is able to spontaneously split the workforce to catch multiple Brownian cargo particles via multitasking. The present intelligent robot swarm enabled by the multiagent control offers a path to realize complex functions at the microscopic scale in a resilient and flexible manner.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"21 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91447542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chenxi Tian, Tianjiao Li, Jenniffer Bustillos, Shonak Bhattacharya, Talia Turnham, J. Yeo, A. Moridi
The latest industrial revolution, Industry 4.0, is driven by the emergence of digital manufacturing and, most notably, additive manufacturing (AM) technologies. The simultaneous material and structure forming in AM broadens the material and structural design space. This expanded design space holds a great potential in creating improved engineering materials and products that attract growing interests from both academia and industry. A major aspect of this growing interest is reflected in the increased adaptation of data‐driven tools that accelerate the exploration of the vast design space in AM. Herein, the integration of data‐driven tools in various aspects of AM is reviewed, from materials design in AM (i.e., homogeneous and composite material design) to structure design for AM (i.e., topology optimization). The optimization of AM tool path using machine learning for producing best‐quality AM products with optimal material and structure is also discussed. Finally, the perspectives on the future development of holistically integrated frameworks of AM and data‐driven methods are provided.
{"title":"Data‐Driven Approaches Toward Smarter Additive Manufacturing","authors":"Chenxi Tian, Tianjiao Li, Jenniffer Bustillos, Shonak Bhattacharya, Talia Turnham, J. Yeo, A. Moridi","doi":"10.1002/aisy.202100014","DOIUrl":"https://doi.org/10.1002/aisy.202100014","url":null,"abstract":"The latest industrial revolution, Industry 4.0, is driven by the emergence of digital manufacturing and, most notably, additive manufacturing (AM) technologies. The simultaneous material and structure forming in AM broadens the material and structural design space. This expanded design space holds a great potential in creating improved engineering materials and products that attract growing interests from both academia and industry. A major aspect of this growing interest is reflected in the increased adaptation of data‐driven tools that accelerate the exploration of the vast design space in AM. Herein, the integration of data‐driven tools in various aspects of AM is reviewed, from materials design in AM (i.e., homogeneous and composite material design) to structure design for AM (i.e., topology optimization). The optimization of AM tool path using machine learning for producing best‐quality AM products with optimal material and structure is also discussed. Finally, the perspectives on the future development of holistically integrated frameworks of AM and data‐driven methods are provided.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85860764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Y. Lim, Chee Koon Ng, U. S. Vaitesswar, K. Hippalgaonkar
Bayesian optimization (BO) has emerged as the algorithm of choice for guiding the selection of experimental parameters in automated active learning driven high throughput experiments in materials science and chemistry. Previous studies suggest that optimization performance of the typical surrogate model in the BO algorithm, Gaussian processes (GPs), may be limited due to its inability to handle complex datasets. Herein, various surrogate models for BO, including GPs and neural network ensembles (NNEs), are investigated. Two materials datasets of different complexity with different properties are used, to compare the performance of GP and NNE—the first is the compressive strength of concrete (8 inputs and 1 target), and the second is a simulated high‐dimensional dataset of thermoelectric properties of inorganic materials (22 inputs and 1 target). While NNEs can converge faster toward optimum values, GPs with optimized kernels are able to ultimately achieve the best evaluated values after 100 iterations, even for the most complex dataset. This surprising result is contrary to expectations. It is believed that these findings shed new light on the understanding of surrogate models for BO, and can help accelerate the inverse design of new materials with better structural and functional performance.
{"title":"Extrapolative Bayesian Optimization with Gaussian Process and Neural Network Ensemble Surrogate Models","authors":"Y. Lim, Chee Koon Ng, U. S. Vaitesswar, K. Hippalgaonkar","doi":"10.1002/aisy.202100101","DOIUrl":"https://doi.org/10.1002/aisy.202100101","url":null,"abstract":"Bayesian optimization (BO) has emerged as the algorithm of choice for guiding the selection of experimental parameters in automated active learning driven high throughput experiments in materials science and chemistry. Previous studies suggest that optimization performance of the typical surrogate model in the BO algorithm, Gaussian processes (GPs), may be limited due to its inability to handle complex datasets. Herein, various surrogate models for BO, including GPs and neural network ensembles (NNEs), are investigated. Two materials datasets of different complexity with different properties are used, to compare the performance of GP and NNE—the first is the compressive strength of concrete (8 inputs and 1 target), and the second is a simulated high‐dimensional dataset of thermoelectric properties of inorganic materials (22 inputs and 1 target). While NNEs can converge faster toward optimum values, GPs with optimized kernels are able to ultimately achieve the best evaluated values after 100 iterations, even for the most complex dataset. This surprising result is contrary to expectations. It is believed that these findings shed new light on the understanding of surrogate models for BO, and can help accelerate the inverse design of new materials with better structural and functional performance.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89938243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Additive manufacturing (AM) is a digital manufacturing process that can directly convert a computer‐aided design model into a physical object in a layer‐by‐layer manner. Due to the additive and discrete nature of the digital manufacturing process, AM needs to find a trade‐off between process resolution and production efficiency. Traditional AM processes balance the resolution and efficiency by tuning the processes either in the temporal domain (e.g., higher speed in serial processes) or in the spatial domain (e.g., more tools in parallel processes). To improve the resolution without sacrificing efficiency, a data‐driven mask image planning method based on subpixel shifting in a split second by tuning the process in both temporal and spatial domains is presented. The method is based on the optimized pixel blending principle and a fast error diffusion‐based optimization model. Various simulation and experimental tests are carried out to verify the developed subpixel shifting method. The experimental results demonstrate the data‐driven‐based mask image calibration and planning techniques significantly improve the fabricated part quality without compromising the process efficiency. The presented spatiotemporal strategy may shed light for future research on the projection‐based AM processes.
{"title":"Spatiotemporal Projection‐Based Additive Manufacturing: A Data‐Driven Image Planning Method for Subpixel Shifting in a Split Second","authors":"Chi Zhou, Han Xu, Yong Chen","doi":"10.1002/aisy.202100079","DOIUrl":"https://doi.org/10.1002/aisy.202100079","url":null,"abstract":"Additive manufacturing (AM) is a digital manufacturing process that can directly convert a computer‐aided design model into a physical object in a layer‐by‐layer manner. Due to the additive and discrete nature of the digital manufacturing process, AM needs to find a trade‐off between process resolution and production efficiency. Traditional AM processes balance the resolution and efficiency by tuning the processes either in the temporal domain (e.g., higher speed in serial processes) or in the spatial domain (e.g., more tools in parallel processes). To improve the resolution without sacrificing efficiency, a data‐driven mask image planning method based on subpixel shifting in a split second by tuning the process in both temporal and spatial domains is presented. The method is based on the optimized pixel blending principle and a fast error diffusion‐based optimization model. Various simulation and experimental tests are carried out to verify the developed subpixel shifting method. The experimental results demonstrate the data‐driven‐based mask image calibration and planning techniques significantly improve the fabricated part quality without compromising the process efficiency. The presented spatiotemporal strategy may shed light for future research on the projection‐based AM processes.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79330118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chengjun Wang, Min Cai, Zengming Hao, Shuang Nie, Changying Liu, Honggen Du, Jian Wang, Wei-qiu Chen, Jizhou Song
The concurrent collection of surface electromyography (sEMG) and strain signals is important for many applications, such as human–machine interaction, sign language recognition, and clinical evaluation of muscle function. Nevertheless, the conventional sensor systems made of rigid, bulky components cannot provide a reliable, conformal interface for accurate, continuous measurements of the epidermal physiological signals. Herein, a skin‐interfaced, multifunctional epidermal sensor patch with characteristics of mechanical softness, large stretchability, and wearable conformability for multimodal measurements of sEMG signals and associated skin deformations from various muscle activities and joint motions is reported. The sensor patch features two pairs of stretchable sEMG electrodes and two thin, miniaturized strain sensors, which are connected by stretchable filamentary serpentine interconnects in an open‐meshed structure. Experimental and computational studies reveal the design and operation of the sensor patch, which exhibit stable and repetitive performance even under a 30% stretching strain level. Demonstrations of the sensor patch on the wrist for simple sign language recognition and on the lower back for the flexion‐relaxation phenomenon illustrate its potential for the comprehensive assessment of the muscle activities and related motions of muscle joints.
{"title":"Stretchable, Multifunctional Epidermal Sensor Patch for Surface Electromyography and Strain Measurements","authors":"Chengjun Wang, Min Cai, Zengming Hao, Shuang Nie, Changying Liu, Honggen Du, Jian Wang, Wei-qiu Chen, Jizhou Song","doi":"10.1002/aisy.202100031","DOIUrl":"https://doi.org/10.1002/aisy.202100031","url":null,"abstract":"The concurrent collection of surface electromyography (sEMG) and strain signals is important for many applications, such as human–machine interaction, sign language recognition, and clinical evaluation of muscle function. Nevertheless, the conventional sensor systems made of rigid, bulky components cannot provide a reliable, conformal interface for accurate, continuous measurements of the epidermal physiological signals. Herein, a skin‐interfaced, multifunctional epidermal sensor patch with characteristics of mechanical softness, large stretchability, and wearable conformability for multimodal measurements of sEMG signals and associated skin deformations from various muscle activities and joint motions is reported. The sensor patch features two pairs of stretchable sEMG electrodes and two thin, miniaturized strain sensors, which are connected by stretchable filamentary serpentine interconnects in an open‐meshed structure. Experimental and computational studies reveal the design and operation of the sensor patch, which exhibit stable and repetitive performance even under a 30% stretching strain level. Demonstrations of the sensor patch on the wrist for simple sign language recognition and on the lower back for the flexion‐relaxation phenomenon illustrate its potential for the comprehensive assessment of the muscle activities and related motions of muscle joints.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"2004 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88332761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Dong, Weizhong Xu, Zhongwen Kuang, Youxing Yao, Zhiqin Zhang, D. Guo, Huaping Wu, T. Zhao, Aiping Liu
Smart hydrogel actuators with programmable anisotropic structures present fascinating prospects considering their distinctive shape transformation and controllable environmental responsiveness under external stimuli. However, the design of anisotropic hydrogels with simple and universal fabrication and programmable functionality is challenging for their valuable applications in smart actuators and soft robots. Herein, a simple, green, and devisable strategy is proposed to construct a heterogeneous porous hydrogel system by the different liquid diffusion (such as amyl alcohol, water, and ethanol) into a monomeric precursor solution of thermosensitive hydrogels. The well‐defined micro/nanoporous gradient and patterned structures related to selective liquid stratification and interfacial diffusion favor the fast response and accurate programmable deformation of hydrogels under temperature stimuli. Inspiringly, this simple diffusion‐driven tactic can be perfectly applicable for different responsive hydrogels with programmable multifunctionality by adding functional nanomaterials into the diffusible liquid. This green, general, and facile diffusion‐driven strategy provides significant guidance for fabricating environmentally responsive hydrogels with tailorable functionality for their multipurpose applications in drug delivery, bioengineering, smart actuators, and soft robots.
{"title":"Liquid Stratification and Diffusion‐Induced Anisotropic Hydrogel Actuators with Excellent Thermosensitivity and Programmable Functionality","authors":"P. Dong, Weizhong Xu, Zhongwen Kuang, Youxing Yao, Zhiqin Zhang, D. Guo, Huaping Wu, T. Zhao, Aiping Liu","doi":"10.1002/aisy.202100030","DOIUrl":"https://doi.org/10.1002/aisy.202100030","url":null,"abstract":"Smart hydrogel actuators with programmable anisotropic structures present fascinating prospects considering their distinctive shape transformation and controllable environmental responsiveness under external stimuli. However, the design of anisotropic hydrogels with simple and universal fabrication and programmable functionality is challenging for their valuable applications in smart actuators and soft robots. Herein, a simple, green, and devisable strategy is proposed to construct a heterogeneous porous hydrogel system by the different liquid diffusion (such as amyl alcohol, water, and ethanol) into a monomeric precursor solution of thermosensitive hydrogels. The well‐defined micro/nanoporous gradient and patterned structures related to selective liquid stratification and interfacial diffusion favor the fast response and accurate programmable deformation of hydrogels under temperature stimuli. Inspiringly, this simple diffusion‐driven tactic can be perfectly applicable for different responsive hydrogels with programmable multifunctionality by adding functional nanomaterials into the diffusible liquid. This green, general, and facile diffusion‐driven strategy provides significant guidance for fabricating environmentally responsive hydrogels with tailorable functionality for their multipurpose applications in drug delivery, bioengineering, smart actuators, and soft robots.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88372066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Andrew Y. Chen, E. Pegg, Ailin Chen, Zeqing Jin, Grace X. Gu
In recent years, the intersection of 3D printing and “smart” stimuli‐responsive materials has led to the development of 4D printing, an emerging field that is a subset of current additive manufacturing research. By integrating existing printing processes with novel materials, 4D printing enables the direct fabrication of sensors, controllable structures, and other functional devices. Compared to traditional manufacturing processes for smart materials, 4D printing permits a high degree of design freedom and flexibility in terms of printable geometry. An important branch of 4D printing concerns electroactive materials, which form the backbone of printable devices with practical applications throughout biology, engineering, and chemistry. Herein, the recent progress in the 4D printing of electroactive materials using several widely studied printing processes is reviewed. In particular, constituent materials and mechanisms for their preparation and printing are discussed, and functional electroactive devices fabricated using 4D printing are highlighted. Current challenges are also described and some of the many data‐driven opportunities for advancement in this promising field are presented.
{"title":"4D Printing of Electroactive Materials","authors":"Andrew Y. Chen, E. Pegg, Ailin Chen, Zeqing Jin, Grace X. Gu","doi":"10.1002/aisy.202100019","DOIUrl":"https://doi.org/10.1002/aisy.202100019","url":null,"abstract":"In recent years, the intersection of 3D printing and “smart” stimuli‐responsive materials has led to the development of 4D printing, an emerging field that is a subset of current additive manufacturing research. By integrating existing printing processes with novel materials, 4D printing enables the direct fabrication of sensors, controllable structures, and other functional devices. Compared to traditional manufacturing processes for smart materials, 4D printing permits a high degree of design freedom and flexibility in terms of printable geometry. An important branch of 4D printing concerns electroactive materials, which form the backbone of printable devices with practical applications throughout biology, engineering, and chemistry. Herein, the recent progress in the 4D printing of electroactive materials using several widely studied printing processes is reviewed. In particular, constituent materials and mechanisms for their preparation and printing are discussed, and functional electroactive devices fabricated using 4D printing are highlighted. Current challenges are also described and some of the many data‐driven opportunities for advancement in this promising field are presented.","PeriodicalId":7187,"journal":{"name":"Advanced Intelligent Systems","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89969182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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