Article
Materials Science, Textiles
Jackie Y. Cai, Jie Min, Jill McDonnell, Lijing Wang, Robert Knott
Summary: Gamma irradiation was applied to vertically aligned carbon nanotube (CNT) forests to improve the interfacial adhesion and then spun into CNT yarns. The yarns spun from the irradiated forests showed higher tensile strength and lower breaking elongation. Increasing spinning tension improved the strength of both the irradiated and unirradiated yarns, but the irradiated yarns had more significant improvements. The relative improvements in yarn tenacity ranged from 14% to 26% under various spinning tensions.
JOURNAL OF INDUSTRIAL TEXTILES
(2022)
Article
Chemistry, Multidisciplinary
Wonkyeong Son, Sungwoo Chun, Jae Myeong Lee, Gichan Jeon, Hyeon Jun Sim, Hyeon Woo Kim, Sung Beom Cho, Dongyun Lee, Junyoung Park, Joonhyeon Jeon, Dongseok Suh, Changsoon Choi
Summary: In this study, a twist-stable and hydrophilic coiled carbon nanotube (CNT) yarn was prepared by the easy electrochemical oxidation (ECO) method. The resulting yarns showed increased density and capacitance, and were fabricated into stretchable supercapacitors.
Article
Chemistry, Physical
S. Lepak-Kuc, P. Taborowska, T. Q. Tran, H. M. Duong, T. Gizewski, M. Jakubowska, J. Patmore, A. Lekawa-Raus
Summary: Coating carbon nanotube fibers with a thin uniform sheath of textile polymer can overcome integration issues in textiles, allowing for repeated washing, dyeing, and texturing while providing effective electrical insulation and separation from the human body. Methods for preparing hybrid CNT fibers have been proposed and tested, with extensive mechanical, electrical, and laundry tests conducted. Examples of applications for coated fibers are also presented.
Article
Chemistry, Multidisciplinary
Yeonsu Jung, Young Shik Cho, Jae Hyun Park, Jae Yeong Cheon, Jae Won Lee, Jae Ho Kim, Chong Rae Park, Taehoon Kim, Seung Jae Yang
Summary: This study examines and controls the architectures of carbon nanotube yarn (CNTY) through chemical modification in order to develop lightweight and superstrong CNTYs. The architecture of CNTY, with polymer layers surrounding a compact bundle of CNTs, allows for further chemical cross-linking and enhances load-transfer efficiency. The resulting CNTY exhibits excellent mechanical performance and is a promising candidate for a space elevator cable.
Article
Nanoscience & Nanotechnology
Md Milon Hossain, Mostakima M. Lubna, Philip D. Bradford
Summary: CNT yarns have excellent properties for wearable electronics and can be tailored for textile processing. The hybrid CNT yarns were used for 3D knitting, resulting in seamless integration into a wristband and glove for sensing movements. The knitted fabric also showed rapid heating capabilities at a low voltage and sustained performance after repeated washing cycles.
ACS APPLIED MATERIALS & INTERFACES
(2023)
Article
Chemistry, Physical
Tae Jin Mun, Ji Hwan Moon, Jong Woo Park, Ray H. Baughman, Seon Jeong Kim
Summary: With an increasing focus on energy harvesting, studies on practical application and performance of energy harvesters are becoming more prevalent. Researchers are exploring the use of continuous energy sources, such as wind, river flow, and sea waves, for energy-harvesting devices. A new technology based on coiled carbon nanotube (CNT) yarns has emerged, which generates energy through mechanical stretch and release. This technology has been demonstrated in a variety of fluid flow environments and tested in river and ocean settings.
Article
Chemistry, Physical
Takumi Watanabe, Akira Itoh, Tomohisa Watanabe, Takeshi Kizaki, Masayasu Inaguma, Atushi Hosoi, Hiroyuki Kawada
Summary: By conducting post-synthesis treatments such as polymer solution impregnation, graphitization, and iodinemonochloride/dichloromethane doping, the electrical properties of CNT yarns can be significantly enhanced. The two-step treatment of GT and ICl/DCM doping shows the most significant improvement in the conductivity and current capacity of the CNT yarns. The purification of the crystalline structure through these treatments is crucial in increasing the electron carrier density and improving the electrical properties.
Article
Nanoscience & Nanotechnology
Wonkyeong Son, Jae Myeong Lee, Sungwoo Chun, Seongjun Yu, Jun Ho Noh, Hyeon Woo Kim, Sung Beom Cho, Seon Jeong Kim, Changsoon Choi
Summary: A facile strategy of electrochemical inner-bundle activation (EIBA) is reported to enhance the properties of CNT yarns. The EIBA-treated yarns exhibit enhanced wettability, improved torsional actuation, and increased capacitance.
ACS APPLIED MATERIALS & INTERFACES
(2023)
Article
Materials Science, Multidisciplinary
A. Pirmoz, J. L. Abot, F. Aviles
Summary: This study proposes a sequential, three-step, multiscale finite element modeling approach to predict the stress-strain response of carbon nanotube yarns (CNTYs) under uniaxial tensile loading. The simulations show that the elastic modulus of the bundle is independent of its diameter, but the yield stress and strength decrease with increasing bundle diameter. The aspect ratio of the individual carbon nanotubes comprising the bundles is also a key factor determining the CNTY strength. This research is important for understanding the mechanical response of CNTYs and their use in various applications.
MECHANICS OF MATERIALS
(2022)
Article
Chemistry, Physical
R. Pech-Piste, C. Perez-Aranda, A. Balam, R. Vargas-Coronado, J. V. Cauich-Rodriguez, F. Aviles
Summary: The electrical response of carbon nanotube yarns (CNTYs) under both alternating current (AC) and direct current (DC) is studied. In situ Raman spectroscopy is used to understand the loading mechanisms and electromechanical response of the CNTY during stretching. The CNTY's AC electrical response is mainly resistive, but shows capacitive behavior at high frequencies. The impedance modulus has a positive sensitivity to strain under AC, slightly higher than under DC, while the phase angle has low sensitivity. Raman spectroscopy and electrical responses are valuable for identifying damage mechanisms.
Article
Chemistry, Multidisciplinary
Seongjae Oh, Keon Jung Kim, Byeonghwa Goh, Chae-Lin Park, Gyu Dong Lee, Seoyoon Shin, Seungju Lim, Eun Sung Kim, Ki Ro Yoon, Changsoon Choi, Hyun Kim, Dongseok Suh, Joonmyung Choi, Shi Hyeong Kim
Summary: Predicting and preventing disasters in difficult-to-access environments requires self-powered monitoring devices. Energy harvesting from external stimuli to supply electricity to batteries is increasingly being considered. This study presents a new method for converting mechanical energy into electrical energy using coiled carbon nanotube yarn harvesters in aqueous environments.
Article
Chemistry, Physical
Byeonghwa Goh, Keon Jung Kim, Chae-Lin Park, Eun Sung Kim, Shi Hyeong Kim, Joonmyung Choi
Summary: This study focuses on the in-plane heat transfer behavior of a coiled multi-walled carbon nanotube (MWCNT) yarn, investigating the significant change in thermal conductivity under mechanical strain and using molecular dynamics simulations to confirm the key factor in heat transfer paths during mechanical loading. The results are crucial for understanding the thermal properties of MWCNT yarn applications such as twistron energy harvesters.
Article
Chemistry, Multidisciplinary
Ming Ren, Jian Qiao, Yulian Wang, Kunjie Wu, Lizhong Dong, Xiaofan Shen, Huichao Zhang, Wei Yang, Yulong Wu, Zhenzhong Yong, Wei Chen, Yongyi Zhang, Jiangtao Di, Qingwen Li
Summary: An ionic-liquid-in-nanofibers sheathed carbon nanotube yarn muscle has been developed, which is strong, stable, and able to achieve a high contraction rate through utilizing accumulated isometric stress. These yarn muscles are tightly bundled, making them suitable for lifting heavy weights and gripping objects, and can serve as desirable actuation components for robotic devices.
Article
Chemistry, Physical
Gongxun Zhai, Qianqian Wang, Fuyao Liu, Zexu Hu, Chao Jia, Dengxin Li, Hengxue Xiang, Meifang Zhu
Summary: This study presents a promising solution for the continuous production of highly graphitized CNT yarns using biomass tannic acid, solving the problem of continuous production and showcasing the excellent performance of CNT yarns.
GREEN ENERGY & ENVIRONMENT
(2023)
Article
Chemistry, Multidisciplinary
Wonkyeong Son, Jae Myeong Lee, Shi Hyeong Kim, Hyeon Woo Kim, Sung Beom Cho, Dongseok Suh, Sungwoo Chun, Changsoon Choi
Summary: A rapidly recoverable high-power hydro-actuator has been achieved by designing biomimetic carbon nanotube yarns, which demonstrate structural stability and high contractile work, recovery speed, actuation frequency, and power density.
Article
Materials Science, Multidisciplinary
Shengfei Zhou, Kai Jin, Talia Khan, Zaira Martin-Moldes, David L. Kaplan, Markus J. Buehler
Summary: More complex bioadhesives were investigated, and the physical properties of the adhesive were improved through cold denaturation and swelling. This resulted in enhanced mechanical performance and water resistance of wood composites.
ADVANCED ENGINEERING MATERIALS
(2023)
Review
Chemistry, Multidisciplinary
Sabrina C. Shen, Eesha Khare, Nicolas A. Lee, Michael K. Saad, David L. Kaplan, Markus J. Buehler
Summary: Engineered materials are important for modern technology but often contribute to ecological deterioration. Next-generation materials can address sustainability goals by providing alternatives to fossil fuel-based materials and reducing extraction processes and solid waste. Challenges include investigating and designing new feedstocks, which are mechanically weak and difficult to standardize. This review outlines a framework for examining sustainability in material systems and discusses the role of computational tools in discovering novel sustainable materials, with a focus on bioinspired and biobased materials.
Article
Materials Science, Multidisciplinary
Markus J. Buehler
Summary: We propose a deep learning method for predicting high-resolution stress fields from material microstructures using a novel class of progressive attention-based transformer diffusion models. The model is trained on a small dataset of input microstructures and corresponding atomic-level Von Mises stress fields obtained from molecular dynamics simulations, and demonstrates excellent accuracy in predicting results. Computational experiments show that the model can accurately predict distinct fracture scenarios, even when trained on a small dataset featuring samples of multiple cracks. Comparison with molecular dynamics simulations confirms the model's high fidelity in all cases, highlighting the exciting potential of progressive transformer diffusion models in the physical sciences for producing high-resolution field images.
JOURNAL OF MATERIALS RESEARCH
(2023)
Article
Materials Science, Multidisciplinary
Zhenze Yang, Yu-Chuan Hsu, Markus J. Buehler
Summary: We present a method of generating new protein designs through a generative adversarial neural network. The mechanical properties of the generated designs are evaluated using simulations and a neural network is developed to predict mechanical properties directly from the molecular architecture. The study provides insights into tailored nanomechanical properties and the nanomechanical responses of molecular structures. Manufactured samples of the designs are also reported using 3D printing.
JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS
(2023)
Review
Chemistry, Physical
Dhriti Nepal, Saewon Kang, Katarina M. Adstedt, Krishan Kanhaiya, Michael R. Bockstaller, L. Catherine Brinson, Markus J. Buehler, Peter Coveney, Kaushik Dayal, Jaafar A. El-Awady, Luke C. Henderson, David L. Kaplan, Sinan Keten, Nicholas A. Kotov, George C. Schatz, Silvia Vignolini, Fritz Vollrath, Yusu Wang, Boris Yakobson, Vladimir V. Tsukruk, Hendrik Heinz
Summary: This Review discusses recent advancements in bioinspired nanocomposite design, focusing on the role of hierarchical structuring at different length scales in creating multifunctional, lightweight, and robust structural materials. By manipulating the architecture, interphases, and confinement, dynamic and synergistic responses have been achieved. The study highlights the significance of hierarchical structures across multiple length scales for achieving multifunctionality and robustness.
Article
Physics, Applied
Rachel K. Luu, Marcin Wysokowski, Markus J. Buehler
Summary: This paper presents a series of deep learning models for solving complex forward and inverse design problems in molecular modeling and design. By using diffusion models inspired by nonequilibrium thermodynamics and attention-based transformer architectures, a flexible framework to capture complex chemical structures is demonstrated. These models are trained on the QM9 dataset and then generalized to study and design key properties of deep eutectic solvents (DESs). The integrated fully prompt-based multi-task generative pretrained transformer model has the best performance and allows for flexible integration of multiple objectives, suggesting emerging synergies during training.
APPLIED PHYSICS LETTERS
(2023)
Article
Polymer Science
Jake Song, Eesha Khare, Li Rao, Markus J. Buehler, Niels Holten-Andersen
Summary: Rheology experiments and density functional theory calculations were used to characterize the stability of coordination complexes between histamine and imidazole with Ni2+, Cu2+, and Zn2+. It was found that the binding hierarchy is driven by the specific affinity of the metal ions to different coordination states, which can be macroscopically tuned by changing the metal-to-ligand stoichiometry. These findings facilitate the rational selection of metal ions for optimizing the mechanical properties of metal-coordinated materials.
MACROMOLECULAR RAPID COMMUNICATIONS
(2023)
Article
Materials Science, Multidisciplinary
Markus J. Buehler
Summary: In this study, a computational approach for analyzing and designing multiscale architected materials is presented. The challenge lies in effectively modeling complex multi-level material structures for hierarchical design approaches. The authors propose an integrated deep neural network architecture that learns coarse-grained representations of complex microstructure data and solves forward and inverse problems through an attention-based diffusion model. The application of the method in the analysis and design of highly porous metamaterials is demonstrated, and the mechanical behavior is validated using molecular dynamics simulations.
MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING
(2023)
Article
Materials Science, Biomaterials
Eesha Khare, Xiangjun Peng, Zaira Martin-Moldes, Guy M. Genin, David L. Kaplan, Markus J. Buehler
Summary: Model verification is critical for scientific accountability, transparency, and learning. In this study, a model verification approach was applied to a molecular dynamics simulation, successfully replicating the key findings of the original model and gaining new insights. Improvements in model validation processes, particularly through enhanced documentation methods, were discussed. This protocol for model verification can be further applied to validate other simulations.
ACS BIOMATERIALS SCIENCE & ENGINEERING
(2023)
Article
Physics, Applied
Markus J. Buehler
Summary: We introduce a flexible deep learning strategy based on a language model to solve complex forward and inverse problems in protein modeling. The model combines transformer and graph convolutional architectures to create a generative pretrained model. It can predict protein structural properties, solubility, and sequence tasks. By training it on inverse tasks, the model can also be used to design proteins with desired properties. The model is prompt-based and can be adapted for various downstream tasks. The study demonstrates the importance of adding additional tasks to improve model performance. The model has been validated through case studies and has shown potential in designing structural and antimicrobial biomaterials.
JOURNAL OF APPLIED PHYSICS
(2023)
Article
Materials Science, Multidisciplinary
Sofia E. Arevalo, Markus J. Buehler
Summary: Biological systems provide inspiration and knowledge for scientists in various fields, as their material architectures often exhibit complex structures and functional interactions. By utilizing molecular-based multiscale modeling, machine learning, and artificial intelligence, along with experimental approaches, researchers can analyze, predict, and design materials with improved properties. This article explores materiomic graph-based modeling frameworks for materials-focused studies in a biological context, and provides an overview of methods applicable to bottom-up manufacturing.
Article
Materials Science, Multidisciplinary
Andrew J. Lew, Cayla A. Stifler, Astrid Cantamessa, Alexandra Tits, Davide Ruffoni, Pupa U. P. A. Gilbert, Markus J. Buehler
Summary: Bioinspired structures created by human engineering offer exciting possibilities for material configurations, but attaining desired properties is still challenging. This study examines the structure-property relationship by focusing on tooth enamel, the hardest biological tissue in humans. The use of artificial intelligence models enables rapid and non-destructive characterization of properties, and a deep image regression neural network is trained as a surrogate model. This model improves spatial resolution and sensitivity compared to experimental hardness maps, allowing for guided materials design.
Article
Chemistry, Physical
Eesha Khare, Jaden Luo, Markus J. Buehler
Summary: Several biological organisms use metal-coordination bonds to create remarkable materials, such as the jaw of the marine worm Nereis virens, which achieves impressive hardness without mineralization. This study investigates the role of metal ions, specifically zinc ions, in the structure and mechanical properties of the Nvjp-1 protein. The initial distribution of metal ions affects the protein's structure, while tensile strength is influenced by hydrogen bond content and uniform distribution of metal ions, providing insights for the development of hardened biomaterials and modeling proteins with significant metal ion content.
Article
Chemistry, Multidisciplinary
Eesha Khare, Darshdeep S. Grewal, Markus J. Buehler
Summary: Dynamic noncovalent interactions play a crucial role in the structure and function of biological proteins and have been explored in bioinspired materials. Metal-coordination bonds offer tunability and can control the properties of synthetic materials. However, understanding the exact contribution of these bonds towards mechanical strength and the effect of geometric arrangements is lacking. In this study, we engineer the cooperative rupture of metal-coordination bonds to enhance the rupture strength of metal-coordinated peptide dimers, and we provide quantitative insights into the cooperativity and intrinsic strength limit of these bonds. This work aims to advance the molecular design principles for metal-coordinated materials.
Article
Chemistry, Multidisciplinary
Wei Lu, David L. Kaplan, Markus J. Buehler
Summary: This study proposes a custom generative language model to design novel spider silk protein sequences with complex combinations of target mechanical properties. The model is fine-tuned on major ampullate spidroin (MaSp) sequences and enables the creation of silk sequences with unique combinations of properties. The study provides insights into the mechanistic roles of sequence patterns in achieving key mechanical properties and has implications for expanding the silkome dataset and synthetic silk design and optimization.
ADVANCED FUNCTIONAL MATERIALS
(2023)
Article
Chemistry, Multidisciplinary
Laetitia Bardet, Herve Roussel, Stefano Saroglia, Masoud Akbari, David Munoz-Rojas, Carmen Jimenez, Aurore Denneulin, Daniel Bellet
Summary: The thermal instability of silver nanowires leads to increased electrical resistance in AgNW networks. Understanding the relationship between structural and electrical properties of AgNW networks is crucial for their integration as transparent electrodes in flexible optoelectronics. In situ X-ray diffraction measurements were used to study the crystallographic evolution of Ag-specific Bragg peaks during thermal ramping, revealing differences in thermal and structural transitions between bare and SnO2-coated AgNW networks.
Article
Chemistry, Multidisciplinary
Nathalia Cancino-Fuentes, Arnau Manasanch, Joana Covelo, Alex Suarez-Perez, Enrique Fernandez, Stratis Matsoukis, Christoph Guger, Xavi Illa, Anton Guimera-Brunet, Maria V. Sanchez-Vives
Summary: This study provides a comprehensive characterization of graphene-based solution-gated field-effect transistors (gSGFETs) for brain recordings, highlighting their potential clinical applications.
Article
Chemistry, Multidisciplinary
Sikandar Aftab, Hailiang Liu, Dhanasekaran Vikraman, Sajjad Hussain, Jungwon Kang, Abdullah A. Al-Kahtani
Summary: This study examines the effects of hybrid nanoparticles made of NiO@rGO and NiO@CNT on the active layers of polymer solar cells and X-ray photodetectors. The findings show that these hybrid nanoparticles can enhance the charge carrier capacities and exciton dissociation properties of the active layers. Among the tested configurations, the NiO@CNT device demonstrates superior performance in converting sunlight into electricity, and achieves the best sensitivity for X-ray detection.
Article
Chemistry, Multidisciplinary
Hyo Jung Shin, Seung Gyu Choi, Fengrui Qu, Min-Hee Yi, Choong-Hyun Lee, Sang Ryong Kim, Hyeong-Geug Kim, Jaewon Beom, Yoonyoung Yi, Do Kyung Kim, Eun-Hye Joe, Hee-Jung Song, Yonghyun Kim, Dong Woon Kim
Summary: This study investigates the role of SOX9 in reactive astrocytes following ischemic brain damage using a PLGA nanoparticle plasmid delivery system. The results demonstrate that PLGA nanoparticles can reduce ischemia-induced neurological deficits and infarct volume, providing a potential opportunity for stroke treatment.
Article
Chemistry, Multidisciplinary
Anurag Chaudhury, Koushik Debnath, Nikhil R. Jana, Jaydeep K. Basu
Summary: The study investigates the interaction between nanoparticles and cell membranes, and identifies key parameters, including charge, crowding, and membrane fluidity, that determine the adsorbed concentration and unbinding transition of nanoparticles.
Article
Chemistry, Multidisciplinary
Sina Sadeghi, Fazel Bateni, Taekhoon Kim, Dae Yong Son, Jeffrey A. Bennett, Negin Orouji, Venkat S. Punati, Christine Stark, Teagan D. Cerra, Rami Awad, Fernando Delgado-Licona, Jinge Xu, Nikolai Mukhin, Hannah Dickerson, Kristofer G. Reyes, Milad Abolhasani
Summary: In this study, an autonomous approach for the development of lead-free metal halide perovskite nanocrystals is presented, which integrates a modular microfluidic platform with machine learning-assisted synthesis modeling. This approach enables rapid and optimized synthesis of copper-based lead-free nanocrystals.
Article
Chemistry, Multidisciplinary
Zahir Abbas, Nissar Hussain, Surender Kumar, Shaikh M. Mobin
Summary: The rational construction of free-standing and flexible electrodes for electrochemical energy storage devices is an emerging research focus. In this study, a redox-active metal-organic framework (MOF) was prepared on carbon nanofibers using an in situ approach, resulting in a flexible electrode with high redox-active behavior and unique properties such as high flexibility and lightweight. The prepared electrode showed excellent cyclic retention and rate capability in supercapacitor applications. Additionally, it could be used as a freestanding electrode in flexible devices at different bending angles.
Article
Chemistry, Multidisciplinary
Lishan Zhang, Xiaoting Zhang, Hui Ran, Ze Chen, Yicheng Ye, Jiamiao Jiang, Ziwei Hu, Miral Azechi, Fei Peng, Hao Tian, Zhili Xu, Yingfeng Tu
Summary: Photodynamic therapy (PDT) is a promising local treatment modality in cancer therapy, but its therapeutic efficacy is restricted by ineffective delivery of photosensitizers and tumor hypoxia. In this study, a phototactic Chlorella-based near-infrared (NIR) driven green affording-oxygen microrobot system was developed for enhanced PDT. The system exhibited desirable phototaxis and continuous oxygen generation, leading to the inhibition of tumor growth in mice. This study demonstrates the potential of using a light-driven green affording-oxygen microrobot to enhance photodynamic therapy.
Article
Chemistry, Multidisciplinary
Yujin Li, Jing Xu, Xinqi Luo, Futing Wang, Zhong Dong, Ke-Jing Huang, Chengjie Hu, Mengyi Hou, Ren Cai
Summary: In this study, hollow heterostructured materials were constructed using an innovative template-engaged method as cathodes for zinc-ion batteries. The materials exhibited fast Zn2+ transport channels, improved electrical conductivity, and controlled volume expansion during cycling. The designed structure allowed for an admirable reversible capacity and high coulombic efficiency.
Article
Chemistry, Multidisciplinary
Paritosh Mahato, Shashi Shekhar, Rahul Yadav, Saptarshi Mukherjee
Summary: This study comprehensively elucidates the role of the core and electrostatic surface of metal nanoclusters in catalytic reduction reactions. The electrostatic surface dramatically modulates the reactivity of metal nanoclusters.
Article
Chemistry, Multidisciplinary
Pei Liu, Mengdi Liang, Zhengwei Liu, Haiyu Long, Han Cheng, Jiahe Su, Zhongbiao Tan, Xuewen He, Min Sun, Xiangqian Li, Shuai He
Summary: This study demonstrates a simple and environmentally-friendly method for the synthesis of zinc oxide nanozymes (ZnO NZs) using wasted hop extract (WHE). The WHE-ZnO NZs exhibit exceptional peroxidase-like activity and serve as effective catalysts for the oxidation of 3,3,5,5-tetramethylbenzidine (TMB) in the presence of hydrogen peroxide (H2O2). In addition, a straightforward colorimetric technique for detecting both H2O2 and glucose was developed using the WHE-ZnO NZs as peroxidase-like catalysts.
Article
Chemistry, Multidisciplinary
Hyunkyu Oh, Young Jun Lee, Eun Ji Kim, Jinseok Park, Hee-Eun Kim, Hyunsoo Lee, Hyunjoo Lee, Bumjoon J. Kim
Summary: Mesoporous carbon particles have unique structural properties that make them suitable as support materials for catalytic applications. This study investigates the impact of channel nanostructures on the catalytic activity of porous carbon particles (PCPs) by fabricating PCPs with controlled channel exposure on the carbon surface. The results show that PCPs with highly open channel nanostructures exhibit significantly higher catalytic activity compared to those with closed channel nanostructures.
Article
Chemistry, Multidisciplinary
Yunjie Lu, Zhaohui Li, Zewei Li, Shihao Zhou, Ning Zhang, Jianming Zhang, Lu Zong
Summary: A tough, long-lasting adhesive and highly conductive nanocomposite hydrogel (PACPH) was fabricated via the synergy of interfacial entanglement and adhesion group densification. PACPH possesses excellent mechanical properties, interfacial adhesion strength, and conductivity, making it a promising material for long-term monitoring of human activities and electrocardiogram signals.
Article
Chemistry, Multidisciplinary
Zichao Wei, Audrey Vandergriff, Chung-Hao Liu, Maham Liaqat, Mu-Ping Nieh, Yu Lei, Jie He
Summary: We have developed a simple method to prepare polymer-grafted plasmonic metal nanoparticles with pH-responsive surface-enhanced Raman scattering. By using pH-responsive polymers as ligands, the aggregation of nanoparticles can be controlled, leading to enhanced SERS. The pH-responsive polymer-grafted nanoparticles show high reproducibility and sensitivity in solution, providing a novel approach for SERS without the need for sample pre-concentration.
Article
Chemistry, Multidisciplinary
Melis Ozge Alas Colak, Ahmet Gungor, Merve Buldu Akturk, Emre Erdem, Rukan Genc
Summary: This research investigates the effect of functionalizing carbon dots with hydroxyl polymers on their performance as electrode materials in a supercapacitor. The results show that the functionalized carbon dots exhibit excellent electrochemical performance and improved stability.
