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Polishing of Black and White CVD Grown Polycrystalline Diamond Coatings

2018· article· en· W2899799723 on OpenAlexvenueno aff
Snigdha Roy, Vamsi Krishna Balla, A.K. Mallik, Victor Ralchenko, A. P. Bolshakov, Eugene E. Ashkinazi

Bibliographic record

VenueJournal of Coating Science and Technology · 2018
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsnot available
Fundersnot available
KeywordsPolishingMaterials scienceCrystalliteWhite (mutation)DiamondPolycrystalline diamondMetallurgyChemistry

Abstract

fetched live from OpenAlex

Microwave plasma CVD growth can produce black and white varieties of polycrystalline diamond (PCD), depending on their as-grown purity. These two types of PCDs have been polished by mechanical and chemo-mechanical polishing respectively. It has been observed that initial roughness of 2.21μm for white PCD can be brought down to 175 nm after 70 hours of combined polishing, whereas, 85 hours of combined polishing could bring down the high initial roughness of 11.2μm for black PCD down to 546 nm at the end. Although, the material that was removed during polishing was higher for the black variety of PCD but it had lower polishing rate of 4nm/hr than white PCD (13nm/hr) during chemo-mechanical polishing. Such differential polishing rate was due to harder top polished surface of the black diamond than the white diamond. The nanoindentation study on the polished PCD surfaces revealed that the black PCD has a final nanohardness of 32.58±1 GPa whereas the white variety PCD had a polished surface nanohardness of 28.5±2 GPa. More conversion of diamond surface into harder amorphous sp3 than softer graphite during polishing action may have resulted such slow rate of anisotropic polishing for black diamond than white diamond.

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 head0.010
GPT teacher head0.269
Teacher spread0.259 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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