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Record W2845538702 · doi:10.5539/jmsr.v7n3p69

Effect of Annealing on Microstructure, Grain Growth and Hardness of Nanocrystalline Cu-Zr Alloy Prepared by Cryogenic Ball Milling

2018· article· en· W2845538702 on OpenAlexvenueno aff
Nidhi Khobragade, Koushik Sikdar, Binod Kumar, Debdas Roy

Bibliographic record

VenueJournal of Materials Science Research · 2018
Typearticle
Languageen
FieldMaterials Science
TopicMicrostructure and mechanical properties
Canadian institutionsnot available
FundersIndo-US Science and Technology ForumNorth Carolina State University
KeywordsMaterials scienceNanocrystalline materialIntermetallicMicrostructureAnnealing (glass)AlloyMetallurgyGrain boundaryGrain growthGrain sizeCopperBall millThermal stabilityChemical engineeringNanotechnology

Abstract

fetched live from OpenAlex

Nanocrystalline Cu-0.75 at.%Zr alloy was synthesized by high energy ball milling under cryogenic temperature. To investigate the influence of 0.75 at.%Zr addition on thermal stabilization of nanocrystalline state of Copper, milled powder was annealed up to T/Tm = 0.79 for 1h in an inert atmosphere. The microstructural changes of both milled and annealed powders were characterized by X-ray diffraction (XRD) and transmission electron microscopy (TEM). Mechanical properties were determined in terms of hardness. It was found that addition of 0.75 at.%Zr can stabilize grain size at higher temperature, i.e., ~ 32 nm at 800oC (T/Tm = 0.79). The hardness of Cu-0.75 at.%Zr at 800oC was found to decrease by only ~ 13% as opposed to a 65% decrease in pure copper from cryomilled condition. The thermal stability of Cu-0.75 at.%Zr system at high temperatures was attributed to the kinetic stabilization, i.e., grain boundary pinning by intermetallic phases. Thermal stability contributions were assessed by thermodynamic models elicits added Zr is not sufficient for stabilization, rather kinetic stabilization (by intermetallic pinning of grain boundary) became active at higher annealing temperature.

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)0.014
metaresearch head score (Gemma)0.002
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 score0.012
Threshold uncertainty score0.811

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.026
GPT teacher head0.340
Teacher spread0.314 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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