Results 331 to 340 of about 19,473,068 (392)

Biophysical characterization and ion transport with cell‐based and proteoliposome reconstitution assays of invertebrate K+‐Cl− cotransporters

FEBS Open Bio, EarlyView.
The K+‐Cl− cotransporters (KCCs) facilitate the symport of ions across the plasma membrane. They participate in physiological processes including neuronal regulation. Here, we characterized KCCs from Drosophila and Hydra vulgaris. Comparative analyses of transporters provide insights into the mechanism of KCC ion transport, regulation, and evolution ...
Satoshi Fudo, Marina Verkhovskaya, Coralie Di Scala, Claudio Rivera, Tommi Kajander   +4 more
wiley   +1 more source

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Materials Today, 2012
openaire   +2 more sources

Materials Science Toolkit for Carbon Footprint Assessment: A Case Study for Endoscopic Accessories of Common Use. [PDF]

ACS Environ Au
Martín-Cabezuelo R, Vilariño-Feltrer G, Campillo-Fernández AJ, Lorenzo-Zúñiga V, Pons V, López-Muñoz P, Tort-Ausina I.   +6 more
europepmc   +1 more source

JUAMI, the joint undertaking for an African materials institute: building materials science research collaborations and capabilities between continents. [PDF]

Acta Crystallogr E Crystallogr Commun
Billinge SJL.
europepmc   +1 more source

Celebrating and supporting early career researchers within underrepresented groups in materials science. [PDF]

Nat Commun
europepmc   +1 more source

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Data-driven materials science: application of ML for predicting band gap

Advances in Materials and Processing Technologies, 2023
The conventional technique of materials design and discovery relies on experimentation and complex computational methods. The sheer number of materials present makes testing each one for specific properties and application makes the entire process time ...
S. Prateek, R. Garg, Kuldeep Kumar Saxena, V. Srivastav, Hitesh Vasudev, Nikhil Kumar   +5 more
semanticscholar   +1 more source

Innovative Materials Science via Machine Learning

Advanced Functional Materials, 2021
Nowadays, the research on materials science is rapidly entering a phase of data‐driven age. Machine learning, one of the most powerful data‐driven methods, have been being applied to materials discovery and performances prediction with undoubtedly ...
Chao Gao, X. Min, M. Fang, Tianyi Tao, Xiaohong Zheng, Yan’gai Liu, Xiaowen Wu, Zhaohui Huang   +7 more
semanticscholar   +1 more source

Materials Science Challenges to Graphene Nanoribbon Electronics.

ACS Nano, 2021
Graphene nanoribbons (GNRs) have recently emerged as promising candidates for channel materials in future nanoelectronic devices due to their exceptional electronic, thermal, and mechanical properties and chemical inertness. However, the adoption of GNRs
V. Saraswat, R. Jacobberger, M. Arnold
semanticscholar   +1 more source