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Record W4318939038 · doi:10.1016/j.heliyon.2023.e13461

Structural and mechanical properties of Si-doped CrN coatings deposited by magnetron sputtering technique

2023· article· en· W4318939038 on OpenAlexfundno aff
Anand Vyas, Ahmed Aliyu

Bibliographic record

VenueHeliyon · 2023
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsnot available
FundersDepartment of Mechanical Engineering, University of AlbertaMinistry of Education, Culture, Sports, Science and TechnologyCity University of Hong Kong
KeywordsNanoindentationMaterials scienceCrystalliteSputter depositionCoatingElastic modulusX-ray photoelectron spectroscopySurface roughnessComposite materialSputteringCavity magnetronMetallurgySubstrate (aquarium)Thin filmNanotechnologyChemical engineering

Abstract

fetched live from OpenAlex

This study successfully deposited Si-doped CrN coatings onto Si (100) substrate by direct current magnetron sputtering. The concentration of Si in the CrSiN coatings was varied by changing the Si target current during deposition. The microstructural and mechanical properties were determined by employing X-ray diffraction, X-ray photoelectron spectroscopy, atomic force microscopy, and nanoindentation test. According to the results, the coating with 3.3 at.% Si contents (CrSiN-2) show an increase and decrease in the crystallite size and coating surface roughness, respectively, leading to solid solution hardening with an optimum hardness and elastic modulus of 21.37 GPa and 205.68 GPa, respectively. With continued Si addition, the coating roughness increased and the mechanical properties gradually decreased and attained 184.08 GPa and 18.95 GPa for the elastic modulus and hardness of the coating with a maximum Si concentration of 9.2 at.% (CrSiN-5).

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 score0.022
Threshold uncertainty score0.463

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)0.0000.000

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 head0.015
GPT teacher head0.199
Teacher spread0.184 · 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

Citations20
Published2023
Admission routes1
Has abstractyes

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