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231226s2022 xx |||||o 00| ||eng c |
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|a 10.1002/adma.202110696
|2 doi
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|a pubmed25n1132.xml
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|a (NLM)35460119
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|a DE-627
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|a eng
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|a Cai, Zhengyang
|e verfasserin
|4 aut
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|a Reinforced Layered Double Hydroxide Oxygen-Evolution Electrocatalysts
|b A Polyoxometallic Acid Wet-Etching Approach and Synergistic Mechanism
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|c 2022
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Revised 01.07.2022
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2022 Wiley-VCH GmbH.
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|a Nickel-iron-based layered double hydroxides (NiFe LDHs) have attracted tremendous research and industrial interests for oxygen evolution reaction (OER) electrocatalysis. However, methodologies on simultaneous regulation of performance-influencing factors remain scarce and their real synergistic effects are not clear enough. Herein, a versatile polyoxometallic acids (POMs) etching approach is reported to ingeniously reconstruct NiFe LDH, including 3D morphological nanotailoring, Fe3+ and α-Ni(OH)2 active species reconfiguration, creation of multiple Ni, Fe, and O vacancies, and intercalation of POM polyanionic clusters. The experimental and theoretical data collaboratively unveil that control of the key performance-influencing factors and their multiple synergistic mechanisms dominantly contribute to the step-like OER performance enhancement. The reinforced electrocatalyst is further produced with low cost and high performance up to Ф180 mm in diameter, showing its feasibility and advancement of the promising configuration of NiFe LDH-PMo12(+) II NiNiFe LDH(-) for alkaline anion-exchange-membrane electrode stack cells. Furthermore, to mathematically evaluate the evolution, a novel empirical formula is proposed for quantitative identification of structure-activity correlations, which offers an opportunity for first and fast reliability on materials screening
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|a Journal Article
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|a AEM electrolyzer stack cells
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|a layered double hydroxides
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|a oxygen-evolution electrocatalysis
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|a polyoxometallic chemistry
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|a semiquantitative predictions
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|a Wang, Ping
|e verfasserin
|4 aut
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|a Zhang, Jiajia
|e verfasserin
|4 aut
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|a Chen, Aiying
|e verfasserin
|4 aut
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|a Zhang, Jiangwei
|e verfasserin
|4 aut
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|a Yan, Ya
|e verfasserin
|4 aut
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|a Wang, Xianying
|e verfasserin
|4 aut
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|i Enthalten in
|t Advanced materials (Deerfield Beach, Fla.)
|d 1998
|g 34(2022), 26 vom: 15. Juli, Seite e2110696
|w (DE-627)NLM098206397
|x 1521-4095
|7 nnas
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|g volume:34
|g year:2022
|g number:26
|g day:15
|g month:07
|g pages:e2110696
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|u http://dx.doi.org/10.1002/adma.202110696
|3 Volltext
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