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Record W2067533231 · doi:10.3139/146.101571

Enhanced thermal stability of a cobalt–boron carbide nanocomposite by ion-implantation

2007· article· en· W2067533231 on OpenAlexaff
Uta Klement, G.D. Hibbard

Bibliographic record

VenueInternational Journal of Materials Research (formerly Zeitschrift fuer Metallkunde) · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced materials and composites
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceTransmission electron microscopyNanocrystalline materialCobaltThermal stabilityNanocompositeAnnealing (glass)Grain growthBoron carbideGrain boundaryCarbideBoronGrain sizeAnalytical Chemistry (journal)MetallurgyComposite materialChemical engineeringNanotechnologyMicrostructureChemistry

Abstract

fetched live from OpenAlex

Abstract A first investigation of the thermal stability in a wear resistant cobalt-boron carbide (Co – B4C) nanocomposite has been performed by the combination of calorimetry and transmission electron microscopy. The calorimetric measurements show that the thermal stability of Co – B4C is not influenced by the presence of the 10 vol.% μm-sized boron carbide particles. However, grain growth is shifted to significantly higher temperatures during in-situ annealing (in the transmission electron microscope), and abnormal grain growth is not observed to be as extensive as in conventional nanocrystalline Co. This effect is mainly attributed to the observed implantation of Ga atoms during transmission electron microscope specimen thinning by focused ion beam. Grain boundary segregation mechanisms are discussed as possible reasons for the retarded grain growth.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.022
GPT teacher head0.324
Teacher spread0.302 · 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 source (direct Gemma or distilled Codex), 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
Published2007
Admission routes1
Has abstractyes

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