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Record W2294271700 · doi:10.1007/s12598-015-0621-3

Removing aluminum from a low‐concentration lixivium of weathered crust elution‐deposited rare earth ore with neutralizing hydrolysis

2015· article· en· W2294271700 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

VenueRare Metals · 2015
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsUniversity of Alberta
FundersProgram for New Century Excellent Talents in UniversityNational Natural Science Foundation of China
KeywordsHexamethylenetetramineHydrolysisPrecipitationAluminiumImpurityMaterials scienceHydroxideRare earthElutionCrustNuclear chemistryMetallurgyInorganic chemistryChemistryGeologyChromatographyGeochemistry

Abstract

fetched live from OpenAlex

Abstract Aluminum, the main impurity in the lixivium of weathered crust elution deposited rare earth ore, not only results in an increasing consumption of precipitant in the rare earth precipitation process, but also lowers the purity of final rare earth product. Aluminum in the weathered crust elution‐deposited rare earth ore lixivium should be removed. Neutralizing hydrolysis method was employed to remove aluminum from the lixivium. Hexamethylenetetramine was found to be the optimum pH regulator for the removal of aluminum in the low concentration. When used to adjust the pH value of the lixivium to 5.0, aluminum in the lixivium can be effectively removed in the form of aluminum hydroxide precipitation with removal rate of 97.60 %. It shows that hexamethylenetetramine has a good effect on the removing of aluminum ions from the low‐concentration lixivium. Moreover, hexamethylenetetramine in removing aluminum from lixivium has little adverse effect on the RE precipitation process.

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 score0.063
Threshold uncertainty score0.697

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 communication0.0000.001
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 head0.020
GPT teacher head0.231
Teacher spread0.211 · 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