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Record W4286638466 · doi:10.1063/5.0095882

Lab-scale chemical vapor deposition onto powders

2022· article· en· W4286638466 on OpenAlexafffund
Jun Wang, M. N. Obrovac

Bibliographic record

VenueAIP Advances · 2022
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFluidized bedFluidizationChemical vapor depositionMaterials scienceGraphiteLithium (medication)Carbon fibersChemical engineeringChemical reactorDeposition (geology)Layer (electronics)NanotechnologyComposite materialChemistryOrganic chemistryComposite number

Abstract

fetched live from OpenAlex

In this paper, a laboratory chemical vapor deposition (CVD) reactor is described, which features an opposing screw rotating fluidized bed. The reactor efficiently concentrates powdered reactants in the reaction zone while maintaining fluidization independent of powder properties. This allows for lab-scale CVD processing of many small powder samples at high yields and without the need for re-adjustment of fluidization parameters for different samples. Alumina and graphite were carbon-coated and characterized in lithium batteries. The deposited carbon layer had a density of 1.84 g/ml and a capacity of 225 mA h/g when cycled between 7 mV and 0.9 V in lithium cells. Carbon coatings applied by this CVD reactor onto graphite particles were found to be effective at reducing surface reactions during cycling in lithium cells. We suggest that utilization of this opposing screw rotating fluidized bed reactor can effectively apply CVD coatings to small laboratory powder samples, with particular utility for Li-ion battery materials.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.077
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

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 head0.009
GPT teacher head0.274
Teacher spread0.264 · 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.

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

Citations6
Published2022
Admission routes2
Has abstractyes

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