Results 261 to 270 of about 2,163,565 (354)

Delocalized Charge Transport in Thermoelectric Composites of Semiconducting Carbon Nanotubes Wrapped with a P‐Type Polymer

open access: yesAdvanced Electronic Materials, EarlyView.
Composites made of semiconducting carbon nanotubes and p‐type polymers, when adequately doped with molecular dopants, can exhibit highly delocalized charge carrier transport, showing high thermoelectric performances. The efficient charge delocalization is enabled by the reduced coulombic binding energy at high carrier concentration and the small ...
Ye Liu   +7 more
wiley   +1 more source

Toward High‐Performance Electrochemical Energy Storage Systems: A Case Study on Predicting Electrochemical Properties and Inverse Material Design of MXene‐Based Electrode Materials with Automated Machine Learning (AutoML)

open access: yesAdvanced Electronic Materials, EarlyView.
This study demonstrates PyCaret's AutoML framework for predicting the electrochemical and structural properties of MXene‐based electrodes, including intercalation voltage, capacity, and lattice constants. AutoML streamlines workflows, ranks key elemental descriptor, and enables inverse molecular formula prediction based on performance targets.
Berna Alemdag   +3 more
wiley   +1 more source

Enhancing Thermoelectric Performance of Cd₃P₂ by Alloying with Dirac Material Cd₃As₂

open access: yesAdvanced Electronic Materials, EarlyView.
This study demonstrates that alloying Cd₃P₂ with Dirac material Cd₃As₂ significantly enhances its thermoelectric performance. Key improvements include increased carrier mobility, reduced effective mass, and lower deformation potential, resulting in a doubled power factor and improved thermoelectric efficiency, which renders this material applicable for
Kunling Peng   +11 more
wiley   +1 more source

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

open access: yesAdvanced 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   +7 more
wiley   +1 more source

State‐of‐the‐Art Machine Learning Technology for Sustainable Lithium Battery Cathode Design: A Perspective

open access: yesAdvanced Energy Materials, EarlyView.
Machine learning applications in Li‐ion batteries. Abstract Technology for lithium‐ion batteries (LIBs) is developing rapidly, which is essential to modern devices and renewable energy sources. The latest development focuses on the optimization of cathode materials, which is critical in determining battery performance and durability.
Adil Saleem   +3 more
wiley   +1 more source

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