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Record W4206318669 · doi:10.1002/smtd.202100947

Electronic Metal–Support Interaction Modulation of Single‐Atom Electrocatalysts for Rechargeable Zinc–Air Batteries

2022· review· en· W4206318669 on OpenAlexafffund
Mingjie Wu, Gaixia Zhang, Weichao Wang, Huaming Yang, Diane Rawach, Mengjun Chen, Shuhui Sun

Bibliographic record

VenueSmall Methods · 2022
Typereview
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship CouncilInstitut national de la recherche scientifique
KeywordsElectrocatalystCatalysisMaterials scienceNanotechnologyMetalOxygen evolutionOxygen atomOxygen reduction reactionElectronic structureZincAtom (system on chip)OxygenChemical engineeringChemistryElectrochemistryComputer scienceElectrodePhysical chemistryMetallurgyComputational chemistryMoleculeOrganic chemistry

Abstract

fetched live from OpenAlex

High-performance oxygen electrocatalysts play a key role in the widespread application of rechargeable Zn-air batteries (ZABs). Single-atom catalysts (SACs) with maximum atom efficiency and well-defined active sites have been recognized as promising alternatives of the present noble-metal-based catalysts for oxygen reduction reaction and oxygen evolution reaction. To improve their oxygen electrocatalysis activities and reveal the structure-activity relationship, many advanced synthesis and characterization methods have been developed to study the effects of 1) coordination and electronic structure of the metal centers and 2) morphology and stability of the conductive substrates. Herein, a detailed review of the recent advances of SACs with strong electronic metal-support interaction (EMSI) for rechargeable ZABs is provided. Great emphasis was placed on the EMSI forms and design strategies. Moreover, the importance and the impact of the atomic coordinating structure and the substrates on the oxygen electrocatalytic activity and stability are highlighted. Finally, future directions and perspectives on the development of SACs are also presented.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.092
GPT teacher head0.371
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations55
Published2022
Admission routes2
Has abstractyes

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