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Record W3095651846 · doi:10.1016/j.matdes.2020.109293

Quantification of the chemical reactivity of molten nitrate salts with heat treatable aluminum alloys

2020· article· en· W3095651846 on OpenAlexaff
Jean‐Philippe Harvey, Shanti Singh, K Oishi, B. Acheson, Richard Turcotte, Daniel Pilon, Jonathan Lavoie, Bernard Grange

Bibliographic record

VenueMaterials & Design · 2020
Typearticle
Languageen
FieldEngineering
TopicThermodynamic and Structural Properties of Metals and Alloys
Canadian institutionsNatural Resources CanadaPolytechnique Montréal
Fundersnot available
KeywordsMaterials scienceMetallurgyAlloyAluminiumLithium nitrateSodium nitrateLithium (medication)MagnesiumThermodynamicsWork (physics)IonChemistry

Abstract

fetched live from OpenAlex

This work explores the conditions for safe heat treatment of aluminum alloys containing lithium and magnesium in molten sodium nitrate (NaNO3) bath furnaces, and conditions where industrial accidents may occur. Using calorimetry coupled to classical thermodynamics, the strength of classical thermodynamics when analyzing thermal curves was demonstrated through a series of small-scale thermal analyses of various aluminum alloys in contact with sodium nitrate. This system was selected to illustrate reactions that may lead to severe and violent heat effect phenomena. Using idealized binary alloys, severe oxidation of magnesium- and lithium-rich aluminum alloy samples were shown to occur near 500 °C, a temperature range dangerously close to the operating temperature of solution heat treatment furnaces in manufacturing processes of heat treatable aluminum alloy sheets used in the aerospace industry. Commercial aluminum alloys AW257, 2198, 2024, and 1050 were also assessed with the same tools. The temperature that needed to be reached for these commercial aluminum alloys to react with molten sodium nitrate was significantly higher than the normal operating temperature of a conventional solution heat treatment furnace.

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)0.000
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 score0.002
Threshold uncertainty score0.267

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 science0.0000.000
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 head0.023
GPT teacher head0.183
Teacher spread0.160 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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