Results 181 to 190 of about 3,118,294 (254)

Inverse Design of Nanoparticulate Materials

Advanced Materials, EarlyView.
Inverse design shifts nanomaterial development from empirical trial‐and‐error to predictive model‐driven strategies. It can rely on knowledge‐based, data‐based, or hybrid process and property functions. This perspective article provides a practical framework for applying inverse design based on instructive examples. It discusses which modeling approach
Nabi Etienne Traoré, Annika Mauch, Michelle Berthold, Lukas Pflug, Wolfgang Peukert, Nicolas Vogel   +5 more
wiley   +1 more source

Recognition of Daily Activities Through Multi-Modal Deep Learning: A Video, Pose, and Object-Aware Approach for Ambient Assisted Living. [PDF]

J Healthc Inform Res
Hashemifard K, Climent-Pérez P, Florez-Revuelta F.   +2 more
europepmc   +1 more source

Microgels Enable iPSCs to Assemble, Expand, and Differentiate Into Organoids—From Sizable to High‐Throughput

Advanced Materials, EarlyView.
A chemically defined PEG‐based microgel platform enables scalable, reproducible production of three‐dimensional microgel‐iPSC constructs in multiple sizes. The system is compatible with high‐throughput automation and supports human iPSC expansion and differentiation within a single construct, including directed differentiation into cardiac organoids ...
Laura Klasen, Matthias Mork, Ramin Nasehi, Vasudha Turuvekere Krishnamurthy, Kira Zeevaert, Aaron Babendreyer, Jacopo Di Russo, Wolfgang Wagner, Laura De Laporte   +8 more
wiley   +1 more source

Deep transferable label propagation with prototypical augmentation. [PDF]

Sci Rep
Dan Y, Zhu L, Zhou D.
europepmc   +1 more source

Fiber‐Format Flexible Linear Electrostatic Motors

Advanced Materials, EarlyView.
A flexible coaxial fibre‐format electrostatic motor enables backdrivable and reversible linear actuation with high force density and functionality under bending, offering a scalable actuation platform for soft and wearable robotics. ABSTRACT Linear motion is fundamental to robotics, yet existing solutions are either rigid electromagnetic motors or ...
Sylvain Schaller, Herbert Shea
wiley   +1 more source

Neuronal activity-driven 3D chromatin dynamics in cortical pyramidal neurons depend on SATB2. [PDF]

Nucleic Acids Res
Wahl N, Ali M, Espeso-Gil S, Dechant G, Apostolova G.   +4 more
europepmc   +1 more source

Phase‐Engineered MoS2 Quantum Monolayer Catalysts

Advanced Materials, EarlyView.
A new type of catalyst model—phase‐engineered MoS2 quantum‐monolayer catalyst (∼0.8 nm thick, < 6 nm in the lateral size; switchable freely among metallic 1T, semiconducting 2H, and a hybrid phase of metallic‐semiconducting 1T‐2H)—is reported. It can efficiently and stably drive photochemical H2, syngas and ammonia synthesis as a catalyst or co ...
Ruijie Yang, Yingying Fan, Lillian Pernitsky, Mojtaba Ebrahimian Mashhadi, Ningxin Chen, Md. Mehadi Hassan, Zheng Li, Heng Zhao, Liang Mei, Zhiyuan Zeng, Zhangxing Chen, Jinguang Hu, Qingye Lu   +12 more
wiley   +1 more source

Physics-Informed Graph Diffusion Model for Cross-Scale Performance Prediction of Silver Nanowire Percolation Networks. [PDF]

ACS Omega
Xiong W, He X, Huang Z, Wang H, Zeng X, Liu X, Lyu L, Hu B.   +7 more
europepmc   +1 more source

Breaking Bottlenecks in Solid Electrolyte Discovery With Large Artificial Intelligence Models

Advanced Materials, EarlyView.
Large AI models are presented as a route to break key bottlenecks in solid electrolyte discovery by linking materials databases, machine‐learning interatomic potentials, large language models, and autonomous experiments. This perspective highlights how AI can accelerate the identification of solid electrolytes with fast ion transport, stable interfaces,
Eric Jianfeng Cheng, Min Hong, Zhiquan Zeng, Chuanyu Liu, Qian Wang, Melissa Jane Meadowcroft, Vlad Badilita, Carlos Miguel Costa, Senentxu Lanceros‐Méndez, Ying Li, Chenyao Ma, Weibo Gong, Surendra Kumar Martha, Aloysius Soon, Piao Ma, Di Zhang, Teguh Ariyanto, Sudaryanto, Hiroshi Kakinuma, Jie Zhao, Jiayu Peng, Pengfei Ou, Jun Lu, Shin‐ichi Orimo, Hao Li   +24 more
wiley   +1 more source

Molecular graph-based invariant representation learning with environmental inference and subgraph generation for out-of-distribution generalization. [PDF]

J Cheminform
Zhu H, Yuan S, Tang M, Zhou G, Hu Z, Liu Z, Li J, Wang J, Li C.   +8 more
europepmc   +1 more source