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Record W3099169616 · doi:10.3390/met10111524

Separation of Radioactive Elements from Rare Earth Element-Bearing Minerals

2020· article· en· W3099169616 on OpenAlexafffund
Adrián Carrillo García, Mohammad Latifi, Ahmadreza Amini, Jamal Chaouki

Bibliographic record

VenueMetals · 2020
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsThoriumLeaching (pedology)UraniumRadioactive wasteSolvent extractionRadiochemistryRare-earth elementExtraction (chemistry)Rare earthEnvironmental sciencePrecipitationChemistryEnvironmental chemistryWaste managementMineralogyNuclear chemistryMaterials scienceMetallurgySoil waterChromatographyEngineeringSoil science

Abstract

fetched live from OpenAlex

Rare earth elements (REE), originally found in various low-grade deposits in the form of different minerals, are associated with gangues that have similar physicochemical properties. However, the production of REE is attractive due to their numerous applications in advanced materials and new technologies. The presence of the radioactive elements, thorium and uranium, in the REE deposits, is a production challenge. Their separation is crucial to gaining a product with minimum radioactivity in the downstream processes, and to mitigate the environmental and safety issues. In the present study, different techniques for separation of the radioactive elements from REE are reviewed, including leaching, precipitation, solvent extraction, and ion chromatography. In addition, the waste management of the separated radioactive elements is discussed with a particular conclusion that such a waste stream can be employed as a valuable co-product.

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 score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.032
GPT teacher head0.279
Teacher spread0.247 · 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

Citations84
Published2020
Admission routes2
Has abstractyes

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