|
|
|
|
| LEADER |
01000caa a22002652 4500 |
| 001 |
NLM375633146 |
| 003 |
DE-627 |
| 005 |
20240918232515.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
240730s2024 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1002/adma.202404981
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n1538.xml
|
| 035 |
|
|
|a (DE-627)NLM375633146
|
| 035 |
|
|
|a (NLM)39075826
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Zhang, Qiuhuan
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Machine Learning-Aided Design of Highly Conductive Anion Exchange Membranes for Fuel Cells and Water Electrolyzers
|
| 264 |
|
1 |
|c 2024
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Revised 18.09.2024
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a © 2024 Wiley‐VCH GmbH.
|
| 520 |
|
|
|a Alkaline anion exchange membrane (AEM)-based fuel cells (AEMFCs) and water electrolyzers (AEMWEs) are vital for enabling the efficient and large-scale utilization of hydrogen energy. However, the performance of such energy devices is impeded by the relatively low conductivity of AEMs. The conventional trial-and-error approach to designing membrane structures has proven to be both inefficient and costly. To address this challenge, a fully connected neural network (FCNN) model is developed based on acid-catalyzed AEMs to analyze the relationship between structure and conductivity among 180,000 AEM variations. Under machine learning guidance, anilinium cation-type membranes are designed and synthesized. Molecular dynamics simulations and Mulliken charge population analysis validated that the presence of a large anilinium cation domain is a result of the inductive effect of N+ and benzene rings. The interconnected anilinium cation domains facilitated the formation of a continuous ion transport channel within the AEMs. Additionally, the incorporation of the benzyl electron-withdrawing group heightened the inductive effect, leading to high conductivity AEM variant as screened by the machine learning model. Furthermore, based on the highly active and low-cost monomers given by machine learning, the large-scale synthesis of anilinium-based AEMs confirms the potential for commercial applications
|
| 650 |
|
4 |
|a Journal Article
|
| 650 |
|
4 |
|a anion exchange membrane
|
| 650 |
|
4 |
|a fuel cell
|
| 650 |
|
4 |
|a fully connected neural network
|
| 650 |
|
4 |
|a high conductivity
|
| 650 |
|
4 |
|a machine learning
|
| 650 |
|
4 |
|a water electrolyzer
|
| 700 |
1 |
|
|a Yuan, Yongjiang
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Zhang, Jiale
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Fang, Pengda
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Pan, Ji
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Zhang, Hao
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Zhou, Tao
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Yu, Qikun
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Zou, Xiuyang
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Sun, Zhe
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Yan, Feng
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t Advanced materials (Deerfield Beach, Fla.)
|d 1998
|g 36(2024), 36 vom: 29. Sept., Seite e2404981
|w (DE-627)NLM098206397
|x 1521-4095
|7 nnns
|
| 773 |
1 |
8 |
|g volume:36
|g year:2024
|g number:36
|g day:29
|g month:09
|g pages:e2404981
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1002/adma.202404981
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 36
|j 2024
|e 36
|b 29
|c 09
|h e2404981
|