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Record W2121093170 · doi:10.1002/cctc.201402607

Heterobimetallic Metal–Organic Framework as a Precursor to Prepare a Nickel/Nanoporous Carbon Composite Catalyst for 4‐Nitrophenol Reduction

2014· article· en· W2121093170 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueChemCatChem · 2014
Typearticle
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsnot available
FundersOffice of ScienceChina University of Petroleum, BeijingNational Natural Science Foundation of ChinaArgonne National LaboratoryBasic Energy SciencesMinistry of Science and Technology of the People's Republic of ChinaCanadian Light SourceUniversity of WashingtonU.S. Department of Energy
KeywordsNickelMetal-organic frameworkMaterials scienceNanoporousPyrolysisCatalysisZincComposite numberPorosity4-NitrophenolChemical engineeringCarbon fibersMetalInorganic chemistryNanotechnologyNanoparticleChemistryMetallurgyOrganic chemistryComposite materialAdsorption

Abstract

fetched live from OpenAlex

Abstract Nickel/nanoporous carbon (Ni/NPC) composites are facilely prepared by direct pyrolysis of nonporous heterobimetallic zinc–nickel–terephthalate frameworks (Zn 1− x Ni x MOF, x ≈0–1, MOF=metal–organic framework) at 1223 K in situ. Tailoring the Ni/Zn ratio creates densely populated and small Ni nanocrystals (Ni NCs) while maintaining sufficient porosity and surface area in the final product, which exhibits the largest activity factor (9.2 s −1 g −1 ) and excellent stability toward 4‐nitrophenol reduction.

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.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.012
GPT teacher head0.245
Teacher spread0.233 · 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