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Record W3042365771 · doi:10.1039/d0ra01892a

A modified ‘skeleton/skin’ strategy for designing CoNiP nanosheets arrayed on graphene foam for on/off switching of NaBH<sub>4</sub> hydrolysis

2020· article· en· W3042365771 on OpenAlexafffund
Jinghua Li, Xianyong Hong, Yilong Wang, Yumei Luo, Bin Li, Pengru Huang, Yongjin Zou, Hailiang Chu, Shiyou Zheng, Lixian Sun, Fen Xu, Yong Du, Jianchuan Wang, Federico Rosei, Seifert Hans Jürgen, Xiang Wu

Bibliographic record

VenueRSC Advances · 2020
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsInstitut National de la Recherche Scientifique
FundersDivision of Graduate EducationNational Key Research and Development Program of ChinaGuilin University of Electronic TechnologyNational Natural Science Foundation of ChinaCanada Research ChairsScientific Research and Technology Development Program of GuangxiRoyal Society of ChemistryRoyal Society
KeywordsNanosheetSchematicGrapheneHydrolysisMaterials scienceChemical engineeringKineticsNanotechnologyChemistryOrganic chemistryElectronic engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

, respectively, outperforming most reported cobalt-based catalysts and other precious metal catalysts. Furthermore, the stability of mockstrawberry-like CoNiP catalyst was investigated, with 74.9% of the initial hydrogen generation rate remaining after 15 cycles. The catalytic properties, durability, and stability of the catalyst were better than those of other catalysts reported previously.

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 score0.043
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.035
GPT teacher head0.279
Teacher spread0.244 · 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

Citations15
Published2020
Admission routes2
Has abstractyes

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