Results 121 to 130 of about 14,465 (269)

Charge Transport in All‐Inorganic CuInSe2 Nanocrystal Transistors: Linking Material Design and Device Performance

Advanced Electronic Materials, EarlyView.
This work explores how colloidal CuInSe2 nanocrystals form functional field‐effect transistors when their native ligands are replaced with short inorganic species that promote charge transport. By systematically adjusting nanocrystal dimensions, surface treatments, film characteristics, and device architecture, the study maps the resulting electronic ...
Nadia Günther, Borja Rodriguez‐Barea, Jasmin L. Herzog, Bjarne E. Stern, Alexandre Ansieau, Angelika Wrzesinska‐Lashkova, Yana Vaynzof, Artur Erbe, Alexander Eychmüller   +8 more
wiley   +1 more source

Joint optimization of task offloading and energy trading in edge-enabled smart grids using deep reinforcement learning. [PDF]

PLoS One
Xue R, Li B, He W, Wang Y.
europepmc   +1 more source

Degradation Pathways of Silicon‐Based Anodes in Lithium‐Ion Batteries

Advanced Energy Materials, EarlyView.
Silicon‐based anodes undergo degradation through five primary pathways: (1) mechanical and structural deterioration of the active material, (2) loss of electrode integrity and electrical contact, (3) mechanical instability of the solid electrolyte interphase (SEI), characterized by repetitive fracture and deformation, (4) chemical instability of the ...
Yoon Jeong Choi, Ji‐Youn Bae, Ga‐On Park, Seung‐Ho Yu   +3 more
wiley   +1 more source

A server-assisted secure blockchain model for residential demand response in smart grids. [PDF]

Sci Rep
Ghosh A, Goswami AK, Shuaibu HA, Ustun TS.   +3 more
europepmc   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Expression of Concern: Ensemble learning approach for advanced metering infrastructure in future smart grids. [PDF]

PLoS One
PLOS One Editors.
europepmc   +1 more source

SigmaFormer: Augmenting transformer encoders with COSMO sigma profiles for pure component property prediction

AIChE Journal, EarlyView.
Abstract Transformer‐based molecular models pretrained on SMILES strings demonstrate strong performance in property prediction. However, these model often lack explicit integration of molecular surface charge distributions that govern intermolecular interactions such as hydrogen bonding and polarity.
Tae Hyun Kim, Silabrata Pahari, Joseph Sang‐Il Kwon   +2 more
wiley   +1 more source

Improving predictive reliability and automation of smart grids using the StarNet ensemble model. [PDF]

Sci Rep
Chhabra A, Singh SK, Kumar S, Singh M, Chauhan U, Arora S, Alhalabi W, Arya V, Alsulami BS, Hsu CH, Gupta BB.   +10 more
europepmc   +1 more source

Hybrid CNN-Transformer Network for Electricity Theft Detection in Smart Grids. [PDF]

Sensors (Basel), 2023
Bai Y, Sun H, Zhang L, Wu H.
europepmc   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source