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Record W2787184030 · doi:10.1063/1.5012960

Stress-induced solid-state amorphization of nanocrystalline Ni and NiZr investigated by atomistic simulations

2018· article· en· W2787184030 on OpenAlex
Md. Meraj, Chuang Deng, Snehanshu Pal

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Applied Physics · 2018
Typearticle
Languageen
FieldEngineering
TopicMetallic Glasses and Amorphous Alloys
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceNanocrystalline materialAlloyUltimate tensile strengthHydrostatic pressureStress (linguistics)Hydrostatic equilibriumMetallurgyThermodynamicsNanotechnologyPhysics

Abstract

fetched live from OpenAlex

In this study, the feasibility of stress induced solid-state amorphization (SSA) of nanocrystalline (NC) Ni and NiZr alloys having ∼10 nm grain size has been investigated under constant tensile load (uniaxial and triaxial) via molecular dynamics simulations. In order to track the structural evaluation in both NC Ni and NiZr alloys during the SSA process, various types of analysis have been used, including simulated X-ray diffraction, centro-symmetry parameter, Voronoi cluster, common neighbor analysis, and radial distribution function. It is found that SSA in both NC Ni and NiZr alloys can only be achieved under triaxial loading conditions, and the hydrostatic tensile stress required for SSA is significantly lower when at. % Zr is increased in the NC NiZr alloy. Specifically, SSA in NC Ni and Ni-5 at. % Zr alloy was observed only when the temperature and hydrostatic tensile stress reached 800 K and 6 GPa, while SSA could occur in NC Ni-10 at. % Zr alloy under just 2 GPa of hydrostatic tensile stress at 300 K.

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 score0.257
Threshold uncertainty score0.500

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 head0.012
GPT teacher head0.224
Teacher spread0.212 · 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