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Effects of additions of nickel nanoparticles on sintering response of PM hybrid low alloy steels

2012· article· en· W1983503083 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

VenuePowder Metallurgy · 2012
Typearticle
Languageen
FieldEngineering
TopicPowder Metallurgy Techniques and Materials
Canadian institutionsNational Research Council CanadaRio Tinto (Canada)Université Laval
Fundersnot available
KeywordsMaterials scienceNickelSinteringMetallurgyAlloyPowder metallurgyMicrostructureNanoparticleGrain boundary diffusion coefficientThermal diffusivityDiffusionLattice diffusion coefficientGrain boundaryEffective diffusion coefficientNanotechnologyThermodynamics

Abstract

fetched live from OpenAlex

Addition of pure elements to powder mixes can cause the formation of heterogeneous microstructures in powder metallurgy (PM) parts upon sintering. For instance, it has been shown that additions of nickel particles to an iron powder form nickel rich areas (NRAs), since nickel has low diffusivity in iron at conventional sintering temperature [∼1121°C (2050°F)]. Thus, the present work is aimed at determining if addition of a small quantity of carbon coated nickel nanoparticles to a PM hybrid low alloy steel premix could result in a more homogeneous distribution of nickel in sintered parts. It also characterises the effect of this addition on microstructures and mechanical properties. Grain boundary and volume diffusion coefficients of nickel nanoparticles have been determined using Suzuoka's equation and wavelength dispersive X-ray spectrometry maps. Results show that addition of nanoparticles initiates lattice diffusion at lower temperature and produces less NRA.

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)0.001
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 score0.002
Threshold uncertainty score0.931

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.010
GPT teacher head0.226
Teacher spread0.216 · 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