Results 91 to 100 of about 278 (159)

Inversion of the Impedance Response Towards Physical Parameter Extraction Using Interpretable Machine Learning

Advanced Energy Materials, EarlyView.
ABSTRACT Interpreting the impedance response of perovskite solar cells (PSCs) is challenging due to the complex coupling of ionic and electronic motion. While drift‐diffusion (DD) modelling is a reliable method, its mathematical complexity makes directly extracting physical parameters from experimental data infeasible.
Mahmoud Nabil, Isel Grau, Ricardo Grau‐Crespo, Said Hamad, Juan A. Anta   +4 more
wiley   +1 more source

Investigation of the Lithium Extraction Mechanism from LiNi0.6Mn0.2Co0.2O2 by Using Operando Neutron Diffraction in an All‐Solid‐State Battery

Advanced Energy Materials, EarlyView.
All‐solid‐state batteries (ASSB) are promising for high‐energy applications. Using a newly designed electrochemical cell, this study demonstrates reversible Li+ insertion/extraction in a thick ASSB and enables operando neutron diffraction. The results reveal structural evolution and H1‐H2 phase coexistence in LiNi0.6Mn0.2Co0.2O2 (NMC622), while ...
Sreelakshmi Anil Kumar, Dhanush Shanbhag, Ove Korjus, Prashanth Sivakumar, Laurence Croguennec, Christian Masquelier, Jean‐Noël Chotard, Emmanuelle Suard   +7 more
wiley   +1 more source

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, 2016
Сергій Олександрович; Національний авіаційний університет Гнатюк, Тетяна Олександрівна; Національний авіаційний університет Жмурко, Василь Миколайович; Національний авіаційний університет Кінзерявий   +2 more
openalex   +1 more source

ÐÑобенноÑÑи пÑавового ÑегÑлиÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð²Ð¾ÑпÑоизводÑÑва леÑов и леÑоÑÐ°Ð·Ð²ÐµÐ´ÐµÐ½Ð¸Ñ Ð² РоÑÑийÑкой ФедеÑаÑии

, 2019
Максим Фадиенко
openalex   +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

ÐизнаÑÐµÐ½Ð½Ñ Ð¾ÑганÑÐ·Ð¾Ð²Ð°Ð½Ð¾Ñ Ð·Ð»Ð¾ÑинноÑÑÑ Ñ Ð±Ð¾ÑоÑÑба з Ð½ÐµÑ Ð·Ð° кÑимÑналÑним законодавÑÑвом РеÑпÑблÑки ÐолÑÑа

, 2016
Аneta; Університет Марії Кюрі-Склодовської Michalska-Warias
openalex   +1 more source

РазÑабоÑка пÑедложений по оÑганизаÑии ÑÑÑов Ð´Ð»Ñ Ð´ÐµÑÑÐºÐ¸Ñ ÑкÑкÑÑÑионнÑÑ Ð³ÑÑпп по СанкÑ-ÐеÑеÑбÑÑÐ³Ñ Ð¸ ÐенингÑадÑкой облаÑÑи

, 2017
Александра Артемьевна Ефимова
openalex   +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

Создание ÑиÑÑÐµÐ¼Ñ ÑпÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ Ð¿Ð¾Ð´Ð°Ñей аммиака на ÑÑÑановке полÑÑÐµÐ½Ð¸Ñ Ð²Ð¾Ð´Ð¾Ñода

, 2018
Алиса Хорошенина
openalex   +1 more source