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Record W7117483460 · doi:10.3390/min16010031

Key Factors Impacting the Decomposition Rate of REE Silicates During Sulfuric Acid Treatment

2025· article· en· W7117483460 on OpenAlexafffund
Yves Thibault, Joanne Gamage McEvoy, Dominique Duguay

Bibliographic record

VenueMinerals · 2025
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsNatural Resources Canada
FundersNatural Resources Canada
KeywordsSulfuric acidDecompositionAmorphous solidPassivationOrthosilicateOxide mineralsChemical decompositionMineral

Abstract

fetched live from OpenAlex

The decomposition of silicates in sulfuric acid to extract rare earth elements (REE) is typically characterized by the formation of an amorphous silica layer surrounding the receding crystal that may act as a passivation barrier limiting the rate of mineral dissolution. In this context, sulfuric acid treatment experiments coupled with detailed characterization of the evolution of the decomposition reaction were performed on natural allanite (CaREEAl2Fe2+Si3O11O[OH]), as well as synthetic neodymium disilicate (Nd2Si2O7), orthosilicate (Ca2Nd8(SiO4)6O2), and orthophosphate (NdPO4) phases in order to investigate if there are key factors, operating on a wide range of silicates, that negatively impact REE recovery. While, as expected, the acid strength is the driver in promoting the decomposition of the orthophosphate, for the silicates investigated, no matter their crystalline structure and chemical resistance, there is a severe passivation mechanism at play in concentrated H2SO4. However, in all cases, this effect can be minimized by water dilution, which strongly enhances sulfate-forming cation transfer across the produced amorphous silica layer. Taking into consideration this distinct characteristic of the mode of decomposition of silicates in sulfuric acid should help in defining optimal extraction strategies.

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 score0.007
Threshold uncertainty score0.261

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 head0.013
GPT teacher head0.282
Teacher spread0.268 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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