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Record W4404581835 · doi:10.1088/1402-4896/ad95c7

A favorable catalytic root for the reduction reaction of SOCl<sub>2</sub> from the constructed CoPc/CuPc composite

2024· article· en· W4404581835 on OpenAlexaff
Zhanwei Xu, Jiaxing Li, Ziwei Zhang, Kai Yao, Ke Zhang, Jiayin Li, Yaze Zhao, Zhiwei Li

Bibliographic record

VenuePhysica Scripta · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCatalysisThionyl chlorideMaterials scienceAdsorptionComposite numberParticle (ecology)Lithium (medication)CathodeDensity functional theoryBattery (electricity)Chemical engineeringParticle sizeCharge densitySurface chargeComposite materialChloridePhysical chemistryChemistryComputational chemistryOrganic chemistryPhysicsMetallurgy

Abstract

fetched live from OpenAlex

Abstract CoPc, as a catalyst for lithium thionyl chloride (Li/SOCl2) battery, is generally recognized as having catalytic activity, and its catalytic activity can be further improved by compounding it on some substrates. In this paper, CoPc/CuPc with smaller particle size was synthesized by two-step method. It was found that when the product was constructed, not only a loose film could be formed on the surface of carbon cathode, but also a large number of holes could be formed inside it. In addition, the adsorption of SOCl2 by CoPc and CuPc and the surface electrostatic charge distribution was studied by the first-principles density functional theory calculations (DFT). Compared with the bare, the voltage of the battery using the CoPc/CuPc composites as catalyst increased by 0.25 V, and the discharge time was prolonged by 12 min. Observing the morphology after discharge, it can be found that there are many particles around 200 nm on the surface and a large number of particles around 50 nm inside.

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 score0.005

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 head0.015
GPT teacher head0.234
Teacher spread0.219 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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