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Record W4390394907 · doi:10.1021/acssusresmgt.3c00075

Vacancy-Rich Carbon-Coated Niobium Carbide Prepared via Carbothermal Reduction of Biomass-Based Carbon Precursors for Efficient Catalytic Epoxidation

2023· article· en· W4390394907 on OpenAlexafffund
Jiayi Wang, Jie Xia, Shankai Hou, Hao Liu, Yongfeng Hu, Mohsen Shakouri, Lixiu Feng, Haifeng Wang, Yong Guo, Xiaohui Liu, Yanqin Wang

Bibliographic record

VenueACS Sustainable Resource Management · 2023
Typearticle
Languageen
FieldEngineering
TopicCatalysis and Hydrodesulfurization Studies
Canadian institutionsCanadian Light Source (Canada)
FundersScience and Technology Commission of Shanghai MunicipalityEast China University of Science and TechnologyNational Natural Science Foundation of ChinaCanadian Light Source
KeywordsCarbothermic reactionCatalysisVanadium carbideNiobium carbideMaterials scienceCarbideNiobiumResorcinolCarbon fibersInorganic chemistryChemical engineeringDecompositionVacancy defectChemistryMetallurgyOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Due to their unique physical and chemical properties, transition metal carbides are expected to play a crucial role in catalysis, energy storage, and electrochemistry. In this study, a vacancy-rich carbon-coated niobium carbide material (NbC@C) was prepared via a simple and relatively milder carbothermic reduction method, sourced from niobium tartrate and sustainable biomass derivatives, glucose, and resorcinol. The formation mechanism of NbC@C materials was proposed with the help of XRD and temperature-programmed decomposition-mass spectrometer. Moreover, high activity for epoxidation of cyclooctene was obtained over the NbC@C material with an impressive yield (93.7%) of epoxycyclooctane, which was much higher than that over commercial NbC-C. The excellent catalytic performance of NbC@C can be ascribed to the high concentration of vacancies generated during the carbothermic reduction process.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.007
GPT teacher head0.217
Teacher spread0.210 · 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 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

Citations2
Published2023
Admission routes2
Has abstractyes

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Same venueACS Sustainable Resource ManagementSame topicCatalysis and Hydrodesulfurization StudiesFrench-language works237,207