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Record W2148458375 · doi:10.1002/prep.201200136

Hydrophobic Nano‐Silica for the Surface Modification of Graphite Flake

2012· article· en· W2148458375 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

VenuePropellants Explosives Pyrotechnics · 2012
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsnot available
FundersChina Scholarship CouncilUniversity of Alberta
KeywordsFlakeGraphiteMaterials scienceCoatingComposite materialScanning electron microscopeNano-

Abstract

fetched live from OpenAlex

Abstract Graphite flake is an electromagnetic interference material of importance for IR screening. In this study, an attempt to improve the performance of graphite flake by coating it with nano‐silica using cyclomix (Hosokawa) and hybridizer (Nara) processes was made. Uncoated and coated graphite flakes were examined by scanning electron microscopy (SEM). It was shown that a more uniform coating was obtained using the hybridizer process. Coated graphite flake with a mass ratio of nano‐silica equal to 5.25 % exhibited the best hydrophobic properties. The test chamber experiments demonstrated that the deposition velocity of coated graphite flake decreased from 0.227 cm s −1 to 0.187 cm s −1 and its IR interference performance was improved, compared with uncoated graphite flake. The obtained results showed that the coatings on the graphite flake powder with hydrophobic nano‐silica enhanced the moisture resistance and electromagnetic interference performance of the graphite flake.

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)0.001
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 score0.011
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.056
GPT teacher head0.281
Teacher spread0.225 · 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