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Record W3156378710 · doi:10.1038/s41529-021-00160-x

The fate of Si and Fe while nuclear glass alters with steel and clay

2021· article· en· W3156378710 on OpenAlexafffund
Charly Carrière, Philippe Dillmann, Stéṕhane Gin, Delphine Neff, Lucile Gentaz, F. Bruguier, I. Monnet, Emmanuel Gardès, Mandana Saheb, Eddy Foy, Nicolas Nuns, Alexis Delanoë, James J. Dynes, Nicolas Michau, Christelle Martin

Bibliographic record

Venuenpj Materials Degradation · 2021
Typearticle
Languageen
FieldMaterials Science
TopicNuclear materials and radiation effects
Canadian institutionsCanadian Light Source (Canada)
FundersCentral Institute of Medicinal and Aromatic PlantsNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchCentre National de la Recherche ScientifiqueCommissariat à l'Énergie Atomique et aux Énergies AlternativesAgence Nationale de la RechercheEuropean Regional Development FundRégion NormandieUniversity of SaskatchewanCanadian Light Source
KeywordsNontroniteDissolutionRadioactive wasteMaterials scienceClay mineralsDispose patternMineralogyLayer (electronics)Chemical engineeringGeologyComposite materialChemistryNuclear chemistryWaste management

Abstract

fetched live from OpenAlex

Abstract The French concept developed to dispose high-level radioactive waste in geological repository relies on glassy waste forms, isolated from the claystone host rock by steel containers. Understanding interactions between glass and surrounding materials is key for assessing the performance of a such system. Here, isotopically tagged SON68 glass, steel and claystone were studied through an integrated mockup conducted at 50 °C for 2.5 years. Post-mortem analyses were performed from nanometric to millimetric scales using TEM, STXM, ToF-SIMS and SEM techniques. The glass alteration layer consisted of a crystallized Fe-rich smectite mineral, close to nontronite, supporting a dissolution/reprecipitation controlling mechanism for glass alteration. The mean glass dissolution rate ranged between 1.6 × 10 −2 g m −2 d −1 to 3.0 × 10 −2 g m −2 d −1 , a value only 3–5 times lower than the initial dissolution rate. Thermodynamic calculations highlighted a competition between nontronite and protective gel, explaining why in the present conditions the formation of a protective layer is prevented.

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 score0.015
Threshold uncertainty score0.396

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 head0.009
GPT teacher head0.211
Teacher spread0.202 · 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
Published2021
Admission routes2
Has abstractyes

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