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Record W1971517707 · doi:10.1002/adfm.201303602

Nanostructured Hybrid Materials for the Selective Recovery and Enrichment of Rare Earth Elements

2014· article· en· W1971517707 on OpenAlex

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

VenueAdvanced Functional Materials · 2014
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsHydro-QuébecUniversité Laval
FundersKorea Advanced Institute of Science and Technology
KeywordsReusabilityRare earthMaterials scienceExtraction (chemistry)SelectivityNanotechnologyPorosityPorous mediumProcess (computing)Process engineeringChemical engineeringCatalysisComputer scienceOrganic chemistryMetallurgyChemistryComposite material

Abstract

fetched live from OpenAlex

The importance of rare‐earth elements (REEs) in the global economy is booming as they are used in numerous advanced technologies. Industrially, the extraction and purification of REEs involve multiple liquid–liquid extraction (LLE) steps as they exhibit very similar complexation properties with most common ligands. In order to substantially improve this process and provide a greener alternative to LLE, functional porous hybrid materials, demonstrating enhanced selectivity towards heavier REEs compared to commercially‐available products, are proposed. In addition, because of the grafting procedure used in the synthesis, the proposed materials demonstrate a higher degree of reusability, increasing their marketable potential.

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 score0.039
Threshold uncertainty score0.408

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 head0.008
GPT teacher head0.222
Teacher spread0.214 · 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