Results 211 to 220 of about 22,658 (293)

Ultrathin Li Metal Anodes: Quantitative Design Principles and Manufacturability Across Liquid and Solid‐State Batteries

Advanced Materials, EarlyView.
Ultrathin lithium metal anodes (≤15 µm) offer a promising route to high‐energy‐density batteries due to their high capacity and low potential. This review presents design principles for ultrathin Li, evaluates fabrication strategies, and discusses challenges in liquid and solid‐state cells.
Cheng Wang, Caoyu Wang, Shuixin Xia, Jodie A. Yuwono, Mingnan Li, Yanqiu Lyu, Ruizhi Zhang, Jun Wang, Jianfeng Mao, Zaiping Guo   +9 more
wiley   +1 more source

Fueling the fire: aspartate deficiency primes and fuels STING activation. [PDF]

J Clin Invest
Jiang H, Wang W, Fu YX.
europepmc   +1 more source

Weaving Intelligence: Thermally Drawn Multimaterial Fibers Toward AI‐Enabled Smart Textiles

Advanced Materials, EarlyView.
Thermally drawn multimaterial fibers are rapidly advancing as intelligent structural units for next‐generation smart textiles. Integrating multimaterial architectures with neuromorphic and spiking‐neural‐network principles enables fabrics that can sense, compute, and adapt autonomously.
Vuong Dinh Trung, Jiaxiang Yi, Hieu Nguyen‐Duc, Tien Nu Hoang Lo, Weili Zhao, Chaoqun Dong, Fabien Sorin, Baris Caglar, Lei Wei, Tung Nguyen‐Dang   +9 more
wiley   +1 more source

Nanotube-Assisted Motor Neuron and Neuromuscular Junction Stabilization in Spinal Muscular Atrophy: A Hypothesis for Adjunctive Therapy. [PDF]

Neurol Int
Fajkić A, Belančić A, Pilipović K, Rački V, Mežnarić S, Janković T, Gkrinia EMM, Vitezić D, Mršić-Pelčić J.   +8 more
europepmc   +1 more source

Designable van der Waals Crystal for Artificial Neuronal Cell Mimicking

Advanced Materials, EarlyView.
Designable van der Waals crystal has been demonstrated for device‐scale neuronal cell mimicking. The structural similarity between ion‐channel in biological membranes and layered vdW lattices is realized with nano‐crystallization via Ar + H2S plasma sulfurization.
Jinhyoung Lee, Gunhyoung Kim, Dongho Lee, Seowoo Son, Hyunho Seok, Sihoon Son, Hyunbin Choi, Geonwook Kim, Geumji Back, Hyunkyu Kim, Chaerin Park, Junmin Ahn, Seongyun Je, Chaeyoung Im, Junil Cho, Magdalena Grzeszczyk, Seongho Kim, Eunseo Go, Hyunwoo Shim, Donghwan Choi, Muyoung Kim, Hyoeng‐U. Kim, Won‐Jun Jang, Taesung Kim   +23 more
wiley   +1 more source

A dynamic vascular-inflammatory-fibrotic loop in systemic sclerosis: an integrative immuno-vascular framework. [PDF]

Front Immunol
Ionitescu RC.
europepmc   +1 more source

Deep Learning Inverse Design of Phase‐Change Reconfigurable Terahertz Metadevices for Multidimensional Secure Communication

Advanced Materials, EarlyView.
A deep learning inverse‐design framework is established to create versatile reconfigurable terahertz metadevices. By synergizing deep learning with phase‐change materials, this approach enables on‐demand customization of multidimensional electromagnetic responses.
Yisheng Dong, Xieyu Chen, Aarthy Nagarajan, Yi Yang, Zhihao Wang, Rui Yu, Chuang Zheng, Chunmei Ouyang, Xueqian Zhang, Tun Cao, Ranjan Singh, Zhen Tian   +11 more
wiley   +1 more source

Macrophage-Orchestrated Metabolic Sensing Drives IBD Pathogenesis: A Framework for Targeted Therapy. [PDF]

Int J Biol Sci
Guan G, Sun X, Chen Y, Guo Q, Dong Y.
europepmc   +1 more source

Soft Hardware, Flowing Software: Reconfigurable Microfluidics for Adaptable Chemical Computation

Advanced Materials, EarlyView.
A reconfigurable microfluidic platform based on soft, photo‐printable, and chemically erasable hydrogel structures printed and erased in situ is used to control flow routing, mixing, chemical patterning, and even chemical computing. Using hardware to control chemical computations decouples logic function from molecular composition, demonstrated via ...
Piet J. M. Swinkels, Brigitta Dúzs, Oliver Skarsetz, Kohei Nishiyama, Andreas Walther   +4 more
wiley   +1 more source

Ferroelectric Dynamic‐Field‐Driven Nucleation and Growth Model for Predictive Materials‐To‐Circuit Co‐Design

Advanced Materials, EarlyView.
This study presents a compact dynamic‐field‐driven nucleation and growth (DFNG) model that captures ferroelectric switching behavior under arbitrary voltage waveforms. It enables extraction of time‐dependent domain wall velocity and growth dimensionality, which can then be extended to device‐level modeling.
Yi Liang, Soohyeon Kim, Tony Chiang, Megan K. Lenox, Ian Mercer, John J. Plombon, Jon‐Paul Maria, Jon F. Ihlefeld, Wenhao Sun, Wei Lu, John T. Heron   +10 more
wiley   +1 more source