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Record W2981981868 · doi:10.4236/jsemat.2019.94007

Erosion and Toughening Mechanisms of Electroless Ni-P-Nano-NiTi Composite Coatings on API X100 Steel under Single Particle Impact

2019· article· en· W2981981868 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

VenueJournal of Surface Engineered Materials and Advanced Technology · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced materials and composites
Canadian institutionsDalhousie University
FundersQatar National Research FundFonds National de la Recherche LuxembourgQatar Foundation
KeywordsMaterials scienceNickel titaniumBrittlenessComposite numberCrackingMetallurgyComposite materialCoatingTougheningToughnessShape-memory alloy

Abstract

fetched live from OpenAlex

The addition of superelastic NiTi to electroless Ni-P coating has been found to toughen the otherwise brittle coatings in static loading conditions, though its effect on erosion behaviour has not yet been explored. In the present study, spherical WC-Co erodent particles were used in single particle impact testing of Ni-P-nano-NiTi composite coatings on API X100 steel substrates at two average velocities—35 m/s and 52 m/s. Erosion tests were performed at impact angles of 30°, 45°, 60°, and 90°. The effect of NiTi concentration in the coating was also examined. Through examination of the impact craters and material response at various impact conditions, it was found that the presence of superelastic NiTi in the brittle Ni-P matrix hindered the propagation of cracks and provided a barrier to crack growth. The following toughening mechanisms were identified: crack bridging and deflection, micro-cracking, and transformation toughening.

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)0.000
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 score0.090
Threshold uncertainty score0.843

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.005
GPT teacher head0.204
Teacher spread0.199 · 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