MétaCan
Menu
Back to cohort
Record W4364377571 · doi:10.3390/nano13081330

Competitive Coordination-Oriented Monodispersed Cobalt Sites on a N-Rich Porous Carbon Microsphere Catalyst for High-Performance Zn−Air Batteries

2023· article· en· W4364377571 on OpenAlexaff
Mengxia Shen, Hao Yang, Qingqing Liu, Qianyu Wang, Jun Liu, Jiale Qi, Xinyu Xu, Jiahua Zhu, Lilong Zhang, Yonghao Ni

Bibliographic record

VenueNanomaterials · 2023
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of New Brunswick
FundersShaanxi University of Science and TechnologyNatural Science Foundation of Shaanxi ProvinceNational Natural Science Foundation of China
KeywordsCobaltCatalysisMaterials scienceCarbon fibersPorosityMicrosphereBattery (electricity)Chemical engineeringPyrolysisMetalDispersion (optics)Metal-organic frameworkNanotechnologyChemistryComposite materialMetallurgyComposite numberOrganic chemistry

Abstract

fetched live from OpenAlex

Metal/nitrogen-doped carbon single-atom catalysts (M−N−C SACs) show excellent catalytic performance with a maximum atom utilization and customizable tunable electronic structure. However, precisely modulating the M−Nx coordination in M−N−C SACs remains a grand challenge. Here, we used a N-rich nucleobase coordination self-assembly strategy to precisely regulate the dispersion of metal atoms by controlling the metal ratio. Meanwhile, the elimination of Zn during pyrolysis produced porous carbon microspheres with a specific surface area of up to 1151 m2 g−1, allowing maximum exposure of Co−N4 sites and facilitating charge transport in the oxygen reduction reaction (ORR) process. Thereby, the monodispersed cobalt sites (Co−N4) in N-rich (18.49 at%) porous carbon microspheres (CoSA/N−PCMS) displayed excellent ORR activity under alkaline conditions. Simultaneously, the Zn−air battery (ZAB) assembled with CoSA/N−PCMS outperformed Pt/C+RuO2-based ZABs in terms of power density and capacity, proving that they have good prospects for practical application.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.221
Teacher spread0.213 · 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 teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations7
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueNanomaterialsSame topicElectrocatalysts for Energy ConversionFrench-language works237,207