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Record W4386750349 · doi:10.1111/ijac.14562

Effect of graphene on the microstructure, mechanical properties, and wear behavior of plasma‐sprayed Al <sub>2</sub> O <sub>3</sub> –Cr <sub>2</sub> O <sub>3</sub> coating

2023· article· en· W4386750349 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Applied Ceramic Technology · 2023
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of China
KeywordsGrapheneMaterials scienceMicrostructureCoatingIndentation hardnessAbrasiveComposite materialBrittlenessPorosityBrittle fractureFracture (geology)Nanotechnology

Abstract

fetched live from OpenAlex

Abstract In this study, plasma‐sprayed Al 2 O 3 –Cr 2 O 3 coatings with different contents of graphene nanosheets were prepared for investigating effects of the graphene on microstructure, mechanical properties, and wear behavior of the coating. The experimental results showed that graphene increased the porosity and the microhardness of the coating. But excessive graphene decreased the microhardness remarkably. Besides, the anti‐crack initiation and propagation abilities of the coatings with graphene improved significantly. The wear rate of the coatings decreased first, and then increased with increasing the graphene content. Impressively, the wear rates of the coating with 2.9% graphene decreased by 33.5% and 36.7%, compared with those of the coating without graphene under normal loads of 5 and 15 N, respectively. The main wear mechanisms of the coatings with and without graphene are brittle fracture and abrasive wear.

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)0.003
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 score0.041
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.001
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 head0.011
GPT teacher head0.244
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