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Record W4309158610 · doi:10.1021/acs.nanolett.2c03696

Mesoscale Mass Transport Enhancement on Well-Defined Porous Carbon Platform for Electrochemical H<sub>2</sub>O<sub>2</sub> Synthesis

2022· article· en· W4309158610 on OpenAlexafffund
Zhiping Deng, Mingxing Gong, Zhe Gong, Xiaolei Wang

Bibliographic record

VenueNano Letters · 2022
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Alberta
FundersCanada First Research Excellence FundChina Postdoctoral Science FoundationSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsMesoscale meteorologyMass transportElectrochemistryMaterials sciencePorosityCarbon fibersPorous mediumChemical engineeringNanotechnologyChemistryPhysicsElectrodeComposite materialEngineering physicsMeteorologyPhysical chemistry

Abstract

fetched live from OpenAlex

Two-electron oxygen reduction toward hydrogen peroxide (H2O2) offers a promising alternative for H2O2 production, but its commercial utilization is still hindered by the difficulty of transferring lab-observed catalyst performance to the practical reactor. Here we report the investigation of the porosity engineering effect on catalytic performance inconsistency through a material platform consisting of a series of hollow mesoporous carbon sphere (HMCS) samples. The performance comparison of HMCS samples in rotating ring-disk electrode and Zn-air battery together with the simulation of diffusion behavior reveals that, in low current density conditions, large surface area is preferred, but the mass transport governs the performance in high current density regions. On account of the favorable porous structure, HMCS-8 nm delivers the most excellent practical performance (166 mW cm–2) and performs well in the bifunctional Zn-air battery for the wastewater purification (70% RhB degraded after 2 min and 99% after 32 min).

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)0.001
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 score0.009
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.186
Teacher spread0.180 · 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

Citations57
Published2022
Admission routes2
Has abstractyes

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