Pub Date : 2025-07-14DOI: 10.1021/accountsmr.5c00009
Taryn Imamura, Sarah Bergbreiter and Rebecca E. Taylor*,
<p >The concept of micrometer-scale swimming robots, also known as microswimmers, navigating the human body to perform robotic tasks has captured the public imagination and inspired researchers through its numerous representations in popular media. This attention highlights the enormous interest in and potential of this technology for biomedical applications, such as cargo delivery, diagnostics, and minimally invasive surgery, as well as for applications in microfluidics and manufacturing. To achieve the collective behavior and control required for microswimmers to effectively perform such actions within complex, in vivo and microfluidic environments, they must meet a strict set of engineering criteria. These requirements include, but are not limited to, small size, structural monodispersity, flexibility, biocompatibility, and multifunctionality. Additionally, microswimmers must be able to adapt to delicate environments, such as human vasculature, while safely performing preprogrammed tasks in response to chemical and mechanical signals.</p><p >Naturally information-bearing biopolymers, such as peptides, RNA, and DNA, can provide programmability, multifunctionality, and nanometer-scale precision for manufactured structures. In particular, DNA is a useful engineering material because of its predictable and well-characterized material properties, as well as its biocompatibility. Moreover, recent advances in DNA nanotechnology have enabled unprecedented abilities to engineer DNA nanostructures with tunable mechanics and responsiveness at nano- and micrometer scales. Incorporating DNA nanostructures as subcomponents in microswimmer systems can grant these structures enhanced deformability, reconfigurability, and responsiveness to biochemical signals while maintaining their biocompatibility, providing a versatile pathway for building programmable, multifunctional micro- and nanoscale machines with robotic capabilities.</p><p >In this Account, we highlight our recent progress toward the experimental realization of responsive microswimmers made with compliant DNA components. We present a hybrid top-down, bottom-up fabrication method that combines templated assembly with structural DNA nanotechnology to address the manufacturing limitations of flexibly linked microswimmers. Using this method, we construct microswimmers with enhanced structural complexity and more controlled particle placement, spacing, and size, while maintaining the compliance of their DNA linkage. We also present a novel experimental platform that utilizes two-photon polymerization (TPP) to fabricate millimeter-scale swimmers (milliswimmers) with fully customizable shapes and integrated flexible linkers. This platform addresses limitations related to population-level heterogeneity in micrometer-scale systems, allowing us to isolate the effects of milliswimmer designs from variations in their physical dimensions. Using this platform, we interrogate established hydrodynamic models of m
{"title":"Microswimmers That Flex: Advancing Microswimmers with Templated Assembly and Responsive DNA Nanostructures","authors":"Taryn Imamura, Sarah Bergbreiter and Rebecca E. Taylor*, ","doi":"10.1021/accountsmr.5c00009","DOIUrl":"https://doi.org/10.1021/accountsmr.5c00009","url":null,"abstract":"<p >The concept of micrometer-scale swimming robots, also known as microswimmers, navigating the human body to perform robotic tasks has captured the public imagination and inspired researchers through its numerous representations in popular media. This attention highlights the enormous interest in and potential of this technology for biomedical applications, such as cargo delivery, diagnostics, and minimally invasive surgery, as well as for applications in microfluidics and manufacturing. To achieve the collective behavior and control required for microswimmers to effectively perform such actions within complex, in vivo and microfluidic environments, they must meet a strict set of engineering criteria. These requirements include, but are not limited to, small size, structural monodispersity, flexibility, biocompatibility, and multifunctionality. Additionally, microswimmers must be able to adapt to delicate environments, such as human vasculature, while safely performing preprogrammed tasks in response to chemical and mechanical signals.</p><p >Naturally information-bearing biopolymers, such as peptides, RNA, and DNA, can provide programmability, multifunctionality, and nanometer-scale precision for manufactured structures. In particular, DNA is a useful engineering material because of its predictable and well-characterized material properties, as well as its biocompatibility. Moreover, recent advances in DNA nanotechnology have enabled unprecedented abilities to engineer DNA nanostructures with tunable mechanics and responsiveness at nano- and micrometer scales. Incorporating DNA nanostructures as subcomponents in microswimmer systems can grant these structures enhanced deformability, reconfigurability, and responsiveness to biochemical signals while maintaining their biocompatibility, providing a versatile pathway for building programmable, multifunctional micro- and nanoscale machines with robotic capabilities.</p><p >In this Account, we highlight our recent progress toward the experimental realization of responsive microswimmers made with compliant DNA components. We present a hybrid top-down, bottom-up fabrication method that combines templated assembly with structural DNA nanotechnology to address the manufacturing limitations of flexibly linked microswimmers. Using this method, we construct microswimmers with enhanced structural complexity and more controlled particle placement, spacing, and size, while maintaining the compliance of their DNA linkage. We also present a novel experimental platform that utilizes two-photon polymerization (TPP) to fabricate millimeter-scale swimmers (milliswimmers) with fully customizable shapes and integrated flexible linkers. This platform addresses limitations related to population-level heterogeneity in micrometer-scale systems, allowing us to isolate the effects of milliswimmer designs from variations in their physical dimensions. Using this platform, we interrogate established hydrodynamic models of m","PeriodicalId":72040,"journal":{"name":"Accounts of materials research","volume":"6 8","pages":"927–938"},"PeriodicalIF":14.7,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/accountsmr.5c00009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144885242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-09DOI: 10.1021/accountsmr.5c00151
Tianle Yue, Jianxin He and Ying Li*,
<p >A new paradigm driven by artificial intelligence (AI) and machine learning (ML) is significantly accelerating the iterative pace of polymer materials research. Traditional experimental approaches to polymer discovery have long relied on trial and error, requiring extensive time and resources while offering limited access to the vast chemical design space. In contrast, ML-assisted strategies provide a transformative framework for efficiently navigating this complex landscape. This paper focuses specifically on polymer design at the molecular level. By integrating data-driven methodologies, researchers can extract structure–property relationships, predict polymer properties, and optimize molecular architectures with unprecedented speed. ML-driven polymer design follows a structured approach: (1) database construction, (2) structural representation and feature engineering, (3) development of ML-based property prediction models, (4) virtual screening of potential candidates, and (5) validation through experiments and/or numerical simulations. This workflow faces two central challenges. First is the limited availability of high-quality polymer datasets, particularly for advanced materials with specialized properties. Second is the generation of virtual polymer structures. Unlike small-molecule drug discovery, where vast libraries of candidate compounds exist, polymer chemistry lacks an equivalent repository of hypothetical structures. Recent efforts have leveraged rule-based polymerization reactions and generative models to create large-scale databases of hypothetical polymers, significantly expanding the design space. Additionally, the diversity of polymer structures, the broad range of their properties, and the limited availability of training samples add complexity to developing accurate predictive models. Addressing these challenges requires innovative ML techniques, such as transfer learning, multitask learning, and generative models, to extract meaningful insights from sparse data and improve prediction reliability. This data-driven approach has enabled the discovery of novel, high-performance polymers for applications in aerospace, electronics, energy storage, and biomedical engineering. Despite these advancements, several hurdles remain. The interpretability of ML models, particularly deep neural networks, is a pressing concern. While black-box models can achieve remarkable predictive accuracy, understanding their decision-making processes remains challenging. Explainable AI methods are increasingly being explored to provide insights into feature importance, model uncertainty, and the underlying chemistry driving polymer properties. Additionally, the synthesizability and processability of ML-generated candidates must be carefully considered to ensure practical experimental validation and real-world application. In this paper, we review recent progress in ML-assisted molecular design of polymer materials, focusing on database development, f
{"title":"Machine-Learning-Assisted Molecular Design of Innovative Polymers","authors":"Tianle Yue, Jianxin He and Ying Li*, ","doi":"10.1021/accountsmr.5c00151","DOIUrl":"https://doi.org/10.1021/accountsmr.5c00151","url":null,"abstract":"<p >A new paradigm driven by artificial intelligence (AI) and machine learning (ML) is significantly accelerating the iterative pace of polymer materials research. Traditional experimental approaches to polymer discovery have long relied on trial and error, requiring extensive time and resources while offering limited access to the vast chemical design space. In contrast, ML-assisted strategies provide a transformative framework for efficiently navigating this complex landscape. This paper focuses specifically on polymer design at the molecular level. By integrating data-driven methodologies, researchers can extract structure–property relationships, predict polymer properties, and optimize molecular architectures with unprecedented speed. ML-driven polymer design follows a structured approach: (1) database construction, (2) structural representation and feature engineering, (3) development of ML-based property prediction models, (4) virtual screening of potential candidates, and (5) validation through experiments and/or numerical simulations. This workflow faces two central challenges. First is the limited availability of high-quality polymer datasets, particularly for advanced materials with specialized properties. Second is the generation of virtual polymer structures. Unlike small-molecule drug discovery, where vast libraries of candidate compounds exist, polymer chemistry lacks an equivalent repository of hypothetical structures. Recent efforts have leveraged rule-based polymerization reactions and generative models to create large-scale databases of hypothetical polymers, significantly expanding the design space. Additionally, the diversity of polymer structures, the broad range of their properties, and the limited availability of training samples add complexity to developing accurate predictive models. Addressing these challenges requires innovative ML techniques, such as transfer learning, multitask learning, and generative models, to extract meaningful insights from sparse data and improve prediction reliability. This data-driven approach has enabled the discovery of novel, high-performance polymers for applications in aerospace, electronics, energy storage, and biomedical engineering. Despite these advancements, several hurdles remain. The interpretability of ML models, particularly deep neural networks, is a pressing concern. While black-box models can achieve remarkable predictive accuracy, understanding their decision-making processes remains challenging. Explainable AI methods are increasingly being explored to provide insights into feature importance, model uncertainty, and the underlying chemistry driving polymer properties. Additionally, the synthesizability and processability of ML-generated candidates must be carefully considered to ensure practical experimental validation and real-world application. In this paper, we review recent progress in ML-assisted molecular design of polymer materials, focusing on database development, f","PeriodicalId":72040,"journal":{"name":"Accounts of materials research","volume":"6 8","pages":"1033–1045"},"PeriodicalIF":14.7,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144885154","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}
Pub Date : 2025-07-03DOI: 10.1021/accountsmr.5c00090
Caichao Ye, Tao Feng, Weishu Liu* and Wenqing Zhang*,
{"title":"Functional Unit: A New Perspective on Materials Science Research Paradigms","authors":"Caichao Ye, Tao Feng, Weishu Liu* and Wenqing Zhang*, ","doi":"10.1021/accountsmr.5c00090","DOIUrl":"https://doi.org/10.1021/accountsmr.5c00090","url":null,"abstract":"","PeriodicalId":72040,"journal":{"name":"Accounts of materials research","volume":"6 8","pages":"914–920"},"PeriodicalIF":14.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144885275","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 >Nature presents us with numerous complex topological structures, among which ordered bicontinuous structures are widely found in biological systems and exhibit numerous functions, as exemplified by the vibrant wings of butterflies and the robust skeletons of knobby starfish. In recent decades, significant strides have been made in preparing functional materials with bicontinuous porous structures, e.g., cubosomes─spherical colloidal particles, which encompass continuous pores and frameworks arranged in a cubic crystal lattice. These cubosomes exhibit many remarkable advantages due to their unique periodic topological structure. (1) The three-dimensional (3D) interconnected pores facilitate the smooth transport of substances throughout the material, resulting in at least a three times higher utilization ratio of internal active sites compared to that of their unconnected pore or nonporous counterparts. Their complex, tortuous, and periodic porous configuration can enhance energy capture, such as solar/electric energy. (2) The 3D continuous pore channels and frameworks provide “highways” for ion and electron transport, leading to an order-of-magnitude reduction in charge-transfer resistance and an over 3-fold increase in the ion diffusion coefficient compared to those of nonporous analogues, thereby improving the electrochemical kinetics of electrodes. (3) Cubosomes have emerged as unique mechanical metamaterials, exhibiting a remarkable capability to alleviate mechanical stress and strain. (4) Their negative-Gaussian-curvature surfaces facilitate the adsorption/desorption of reaction intermediates, thereby lowering the reaction free energy in catalytic reaction processes. Additionally, this distinctive surface structure can enhance the electric field intensity at material interfaces, significantly promoting ion adsorption. With these advantages, functional cubosomes show potential for application in the field of energy storage and conversion. However, due to the big challenges in their preparation, there have been limited studies on their structure–activity relationships in energy-related applications. Therefore, there has not yet been a review regarding functional cubosomes.</p><p >In this Account, we summarize mainly our latest progress in the study of functional cubosomes. First, we introduce the preparation of polymer cubosomes (PCs) through the self-assembly of block copolymers in solution, along with plotting their morphological phase diagram. Then, the Account describes nanocasting approaches in which polymer cubosomes are employed as templates to prepare a variety of functional cubosomes, including polymers, covalent organic frameworks (COFs), metal–organic frameworks (MOFs), metal–phenolic networks, carbons, inorganic metal compounds, and metals. Finally, to elucidate the application prospects of the functional cubosomes, this Account discusses their advantages in different energy storage and conversion applications, highlighting effi
{"title":"Functional Porous Cubosomes: Synthesis and Applications in Energy Storage and Conversion","authors":"Luoxing Xiang, Chen Tang, Zhi Xu, Fugui Xu, Chen Li* and Yiyong Mai*, ","doi":"10.1021/accountsmr.5c00073","DOIUrl":"https://doi.org/10.1021/accountsmr.5c00073","url":null,"abstract":"<p >Nature presents us with numerous complex topological structures, among which ordered bicontinuous structures are widely found in biological systems and exhibit numerous functions, as exemplified by the vibrant wings of butterflies and the robust skeletons of knobby starfish. In recent decades, significant strides have been made in preparing functional materials with bicontinuous porous structures, e.g., cubosomes─spherical colloidal particles, which encompass continuous pores and frameworks arranged in a cubic crystal lattice. These cubosomes exhibit many remarkable advantages due to their unique periodic topological structure. (1) The three-dimensional (3D) interconnected pores facilitate the smooth transport of substances throughout the material, resulting in at least a three times higher utilization ratio of internal active sites compared to that of their unconnected pore or nonporous counterparts. Their complex, tortuous, and periodic porous configuration can enhance energy capture, such as solar/electric energy. (2) The 3D continuous pore channels and frameworks provide “highways” for ion and electron transport, leading to an order-of-magnitude reduction in charge-transfer resistance and an over 3-fold increase in the ion diffusion coefficient compared to those of nonporous analogues, thereby improving the electrochemical kinetics of electrodes. (3) Cubosomes have emerged as unique mechanical metamaterials, exhibiting a remarkable capability to alleviate mechanical stress and strain. (4) Their negative-Gaussian-curvature surfaces facilitate the adsorption/desorption of reaction intermediates, thereby lowering the reaction free energy in catalytic reaction processes. Additionally, this distinctive surface structure can enhance the electric field intensity at material interfaces, significantly promoting ion adsorption. With these advantages, functional cubosomes show potential for application in the field of energy storage and conversion. However, due to the big challenges in their preparation, there have been limited studies on their structure–activity relationships in energy-related applications. Therefore, there has not yet been a review regarding functional cubosomes.</p><p >In this Account, we summarize mainly our latest progress in the study of functional cubosomes. First, we introduce the preparation of polymer cubosomes (PCs) through the self-assembly of block copolymers in solution, along with plotting their morphological phase diagram. Then, the Account describes nanocasting approaches in which polymer cubosomes are employed as templates to prepare a variety of functional cubosomes, including polymers, covalent organic frameworks (COFs), metal–organic frameworks (MOFs), metal–phenolic networks, carbons, inorganic metal compounds, and metals. Finally, to elucidate the application prospects of the functional cubosomes, this Account discusses their advantages in different energy storage and conversion applications, highlighting effi","PeriodicalId":72040,"journal":{"name":"Accounts of materials research","volume":"6 8","pages":"939–951"},"PeriodicalIF":14.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144885276","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}
Birgit Esser*, Isabel H. Morhenn and Michael Keis,
{"title":"","authors":"Birgit Esser*, Isabel H. Morhenn and Michael Keis, ","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":72040,"journal":{"name":"Accounts of materials research","volume":"6 6","pages":"XXX-XXX XXX-XXX"},"PeriodicalIF":14.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/accountsmr.5c00053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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