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Record W4412800748 · doi:10.1016/j.xpro.2025.104005

Protocol for constructing asymmetric triple-atoms supported on nitrogen-doped carbon nanotubes via atomic layer deposition

2025· article· en· W4412800748 on OpenAlexafffund
Jingyan Zhang, Zhongxin Song, Xiaozhang Yao, Yi Guan, Ziwei Huo, Ning Chen, Lei Zhang, Xueliang Sun

Bibliographic record

VenueSTAR Protocols · 2025
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsCanadian Light Source (Canada)Western University
FundersNatural Sciences and Engineering Research Council of CanadaShenzhen UniversityCanada Foundation for InnovationWestern UniversityNational Natural Science Foundation of ChinaCanada Research ChairsBallard Power Systems
KeywordsAtomic layer depositionLayer (electronics)Carbon fibersNitrogenDeposition (geology)Materials scienceCarbon nanotubeDopingNanotechnologyChemical engineeringChemistryOptoelectronicsComposite numberComposite materialOrganic chemistryGeologyEngineering

Abstract

fetched live from OpenAlex

The precise construction of highly efficient triple-atom catalysts (TACs) remains a significant challenge. Here, we present a protocol for constructing asymmetric Pt-Ru-Co triple-atoms (TAs) supported on nitrogen-doped carbon nanotubes (NCNTs) via selective atomic layer deposition (ALD) technology. We describe steps for synthesizing the NCNT substrate, deposition of Pt-Ru-Co TAs, and characterizing the materials. We then detail procedures for preparing the electrolyte solution and working electrode followed by electrochemistry measurements for hydrogen evolution and hydrogen oxidative reactions with a typical three-electrode system. For complete details on the use and execution of this protocol, please refer to Zhang et al. 1

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)0.002
metaresearch head score (Gemma)0.001
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0010.000
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 head0.030
GPT teacher head0.346
Teacher spread0.316 · 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
GenreProtocol

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

Citations1
Published2025
Admission routes2
Has abstractyes

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