MétaCan
Menu
Back to cohort
Record W2621157534 · doi:10.1111/jmi.12591

About the contrast of δ’ precipitates in bulk Al–Cu–Li alloys in reflection mode with a field‐emission scanning electron microscope at low accelerating voltage

2017· article· en· W2621157534 on OpenAlex
Nicolas Brodusch, Frédéric Voisard, Raynald Gauvin

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

VenueJournal of Microscopy · 2017
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaterials scienceTransmission electron microscopyMicrostructureAcceleration voltagePolishingElectronScanning electron microscopeField electron emissionField emission gunPrecipitationAnalytical Chemistry (journal)OpticsCathode rayNanotechnologyMetallurgyChemistryComposite materialPhysics

Abstract

fetched live from OpenAlex

Summary Characterising the impact of lithium additions in the precipitation sequence in Al–Li–Cu alloys is important to control the strengthening of the final material. Since now, transmission electron microscopy (TEM) at high beam voltage has been the technique of choice to monitor the size and spatial distribution of δ’ precipitates (Al 3 Li). Here we report on the imaging of the δ’ phase in such alloys using backscattered electrons (BSE) and low accelerating voltage in a high‐resolution field‐emission scanning electron microscope. By applying low‐energy Ar + ion milling to the surface after mechanical polishing (MP), the MP‐induced corroded layers were efficiently removed and permitted the δ’s to be visible with a limited impact on the observed microstructure. The resulting BSE contrast between the δ’s and the Al matrix was compared with that obtained using Monte Carlo modelling. The artefacts possibly resulting from the sample preparation procedure were reviewed and discussed and permitted to confirm that these precipitates were effectively the metastable δ’s. The method described in this report necessitates less intensive sample preparation than that required for TEM and provides a much larger field of view and an easily interpretable contrast compared to the transmission techniques.

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 score0.028
Threshold uncertainty score0.718

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 science0.0010.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.276
Teacher spread0.264 · 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