Results 31 to 40 of about 1,057 (173)

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Large‐Scale Determination of Frontier Orbital Energies of Disordered Small‐Molecule Organic Semiconductors Using Exciplex Emission Spectra

Advanced Materials, EarlyView.
ABSTRACT Accurately knowing the frontier orbital energies of the structurally disordered small‐molecule organic semiconductors that are used in optoelectronic devices such as organic light‐emitting diodes is required to rationally improve their performance. Here, we show that these energies can be deduced with a large accuracy from the peak energies of
Christian B. McDonald, Hiroki Tomita, Giacomo Cotelli, Louka L. Brüll, Nanno G. de Rooij, Lucas R.L. Norg, Milo Clément, Reinder Coehoorn   +7 more
wiley   +1 more source

How “Green” is Your Functional Material? Assessing Environmental Impacts of Multi‐Step Synthetic Protocols in Material Sciences at Various Technology Levels

Advanced Materials, EarlyView.
Environmental Synthesis Radar (ESR) evaluates the environmental impact of (early‐stage) multi‐step chemical syntheses, providing a holistic view by using 18 midpoint impact categories. Unlike conventional life‐cycle assessment (LCA) approaches, ESR reveals three complementary metrics: overall impact Vtotal, environmental hotspots VEF, and critical ...
Laura Plein, Alexander Schommer, Florian Dietrich, Moritz Gutsch, Heike Klesper, Kevin Schuller, Lars Jahnke, Jens Leker, Klaus Meerholz   +8 more
wiley   +1 more source

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

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

A Critical Call for Mechanistic Investigations of Metal‐Bacteria Interactions

Advanced Materials, EarlyView.
It is well established that the antibacterial properties of metals have tremendous importance for bioengineering applications. What is not as appreciated, however, is the reproducibility crisis in this field's vast literature. In this perspective, we highlight this reproducibility problem, discuss its sources, and suggest solutions towards focused ...
J. Rackwitz, A. Bandyopadhyay, S. Belkin, S. Bose, N. Eliaz, J. L. Gilbert, T. C. Lowe, F. Mücklich, R. J. Turner, C. C. Tasan   +9 more
wiley   +1 more source

Interface Engineering of Porous Transport Layer‐Catalyst Layer in Proton Exchange Membrane Water Electrolyzers: A Comprehensive Review

Advanced Materials Interfaces, EarlyView.
The PTL–CL interface plays a decisive role in PEMWE performance by governing electron conduction, water supply, oxygen removal, catalyst utilization, and interfacial stability. This review summarizes interface‐related resistance sources, characterization methods, and engineering strategies, highlighting rational PTL–CL interface design for low‐cost ...
Aobo Wan, Zhengkai Tu
wiley   +1 more source

Recent Advances of Slip Sensors for Smart Robotics

Advanced Materials Technologies, EarlyView.
This review summarizes recent progress in robotic slip sensors across mechanical, electrical, thermal, optical, magnetic, and acoustic mechanisms, offering a comprehensive reference for the selection of slip sensors in robotic applications. In addition, current challenges and emerging trends are identified to advance the development of robust, adaptive,
Xingyu Zhang, Liao Wu, Shuai He, Zhao Sha, Yuwan Huang, Shuying Wu, Dewei Chu, Chun H. Wang, Shuhua Peng   +8 more
wiley   +1 more source

Self‐Sealing Microneedles Improve Precision and Reduce Reflux in Subretinal Cell Transplantation

Advanced Materials Technologies, EarlyView.
Ultrafine 49G microneedles enable subretinal delivery of induced pluripotent stem cell‐derived retinal pigment epithelial cells through a 50 µm outer diameter tip. Compared with conventional 38G microcannulas, these microneedles reduce cell suspension reflux in a rabbit model, highlighting the potential of microinvasive delivery instruments to improve ...
Jared Ching, Hinako Ichikawa, Aya Motoyoshi, Shin Tanaka, Liam Morrow, Satoshi Shirae, Noriko Sakai, Shohei Kitahata, Michiko Mandai, Kazuaki Kadonosono   +9 more
wiley   +1 more source

The Future of Research in Cognitive Robotics: Foundation Models or Developmental Cognitive Models?

Advanced Robotics Research, EarlyView.
Research in cognitive robotics founded on principles of developmental psychology and enactive cognitive science would yield what we seek in autonomous robots: the ability to perceive its environment, learn from experience, anticipate the outcome of events, act to pursue goals, and adapt to changing circumstances without resorting to training with ...
David Vernon
wiley   +1 more source