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Record W4243016971 · doi:10.1063/1.4867638

Cavity morphology in a Ni based superalloy under heavy ion irradiation with hot pre-injected helium. II

2014· article· en· W4243016971 on OpenAlexafffund
He Zhang, Zhongwen Yao, Mark R. Daymond, Marquis A. Kirk

Bibliographic record

VenueJournal of Applied Physics · 2014
Typearticle
Languageen
FieldMaterials Science
TopicFusion materials and technologies
Canadian institutionsQueen's University
FundersArgonne National LaboratoryOffice of ScienceDivision of Materials ResearchAtomic Energy of Canada LimitedU.S. Department of Energy
KeywordsHeliumIrradiationMaterials scienceMicrostructureRadiochemistryAnalytical Chemistry (journal)ChemistryMetallurgyNuclear physics

Abstract

fetched live from OpenAlex

In the current investigation, TEM in-situ heavy ion (1 MeV Kr2+) irradiation with helium pre-injected at elevated temperature (400 °C) was conducted to simulate in-reactor neutron irradiation induced damage in CANDU spacer material Inconel X-750, in an effort to understand the effects of helium on irradiation induced cavity microstructures. Three different quantities of helium, 400 appm, 1000 appm, and 5000 appm, were pre-injected directly into TEM foils at 400 °C. The samples containing helium were then irradiated in-situ with 1 MeV Kr2+ at 400 °C to a final dose of 5.4 dpa (displacement per atom). Cavities were formed from the helium injection solely and the cavity density and size increased with increasing helium dosage. In contrast to previous heavy ion irradiations with cold pre-injected helium, heterogeneous nucleation of cavities was observed. During the ensuing heavy ion irradiation, dynamical observation showed noticeable size increase in cavities which nucleated close to the grain boundaries. A “bubble-void” transformation was observed after Kr2+ irradiation to high dose (5.4 dpa) in samples containing 1000 appm and 5000 appm helium. Cavity distribution was found to be consistent with in-reactor neutron irradiation induced cavity microstructures. This implies that the distribution of helium is greatly dependent on the injection temperature, and helium pre-injection at high temperature is preferred for simulating the migration of the transmutation produced helium.

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 score0.001
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 head0.010
GPT teacher head0.221
Teacher spread0.211 · 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

Citations13
Published2014
Admission routes2
Has abstractyes

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