Results 221 to 230 of about 210,581 (313)

The Metamorphosis of Flies. I. [PDF]

The American Naturalist, 1874
openaire   +1 more source

Integrating Automated Electrochemistry and High‐Throughput Characterization with Machine Learning to Explore Si─Ge─Sn Thin‐Film Lithium Battery Anodes

Advanced Energy Materials, Volume 15, Issue 11, March 18, 2025.
A closed‐loop, data‐driven approach facilitates the exploration of high‐performance Si─Ge─Sn alloys as promising fast‐charging battery anodes. Autonomous electrochemical experimentation using a scanning droplet cell is combined with real‐time optimization to efficiently navigate composition space.
Alexey Sanin, Jackson K. Flowers, Tobias H. Piotrowiak, Frederic Felsen, Leon Merker, Alfred Ludwig, Dominic Bresser, Helge Sören Stein   +7 more
wiley   +1 more source

dMyc suppresses CTG-induced cytotoxicity in the Drosophila model of DM1 by reducing autophagy and cell death. [PDF]

Cell Death Discov
Chakraborty D, Singh NT, Borthakur S, Singh MD.   +3 more
europepmc   +1 more source

Assessing Mesoscale Heterogeneities in Hard Carbon Electrodes Through Deep Learning‐Assisted FIB‐SEM Characterization, Manufacturing and Electrochemical Modeling

Advanced Energy Materials, EarlyView.
A combination of discrete and finite element method models for the current collector deformation and electrochemical performance analysis, respectively. The models are calibrated and validated with electrochemical and imaging data of hard carbon electrodes. These electrodes were manufactured with different parameters (slurry solid contents of 35 and 40
Soorya Saravanan, Imelda Cardenas‐Sierra, Pei Sun, Hari Raj, Daniele Duranti, Mahnoor Nafees, Edoardo Rossi, Marco Sebastiani, Paul Vigneaux, Martin Petit, Maxime Moreaud, Francisco Fernandez, Alejandro A. Franco   +12 more
wiley   +1 more source

Gut yeasts accelerate chill coma recovery in Drosophila melanogaster. [PDF]

J Exp Biol
Jiménez-Padilla Y, Sinclair BJ.
europepmc   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Chlorophyll Supplementation Delays Aging in <i>Drosophila melanogaster</i> via Enhanced Stress Resistance and Detoxification Network Remodeling. [PDF]

Nutrients
Shen S, Xu N, Yang Z, Han Z, Zeng L, Ibrahim AN, Zhang Y.   +6 more
europepmc   +1 more source

A Highly Hydrophilic Air Electrode With Water‐Induced Surface Reconstruction for Efficient Reversible Protonic Ceramic Cell at Low Water Partial Pressures

Advanced Energy Materials, EarlyView.
This work demonstrates a new strategy for reversible protonic ceramic cells (R‐PCCs). By developing highly hydrophilic oxides, efficient operation is achieved under low water vapor pressures while maintaining high performance and stability. This approach addresses the challenge of hydrogen production in freshwater‐scarce regions.
Nai Shi, Yun Xie, Xueyu Hu, Junji Hyodo, Chun‐Kuo Peng, Gaopeng Zhou, Hanwen Liu, Bingbing Qiu, Daqin Guan, Jiayi Tang, Yan‐Gu Lin, Ranran Peng, Moses Oludayo Tadé, Meilin Liu, Yoshihiro Yamazaki, Zongping Shao   +15 more
wiley   +1 more source

Sex-Specific Regulation of the Turandot Gene Family Modulates Temperature-Dependent Lifespan in Drosophila melanogaster. [PDF]

Aging Cell
Hoffman JM, Lasher AT, Fitch MP, Austad SN, Sun LY.   +4 more
europepmc   +1 more source

Mechanisms of Alkali Ionic Transport in Amorphous Oxyhalides Solid State Conductors

Advanced Energy Materials, EarlyView.
Large‐scale machine learning‐based molecular dynamics simulations are used to investigate isovalent amorphous oxyhalides, revealing a remarkable chemically independent ionic conductivity. A rigorous analysis of alkali residence times across different metal–anion environments identifies divalent anions as key diffusion bottlenecks.
Luca Binci, KyuJung Jun, Bowen Deng, Gerbrand Ceder   +3 more
wiley   +1 more source