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Record W4410328159 · doi:10.1080/08827508.2025.2502369

A New Approach to Magnesium Removal in Cobalt Precipitation

2025· article· en· W4410328159 on OpenAlex
Weiping Liu, Yujie Zhao, Junfeng Cheng, Michael Kabangu Ngoie, Wei Sun, Shafiq Alam

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

VenueMineral Processing and Extractive Metallurgy Review · 2025
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCobaltMagnesiumPrecipitationEnvironmental scienceChemistryMetallurgyMaterials sciencePhysicsMeteorology

Abstract

fetched live from OpenAlex

Sodium dodecyl sulfate (SDS) was introduced to reduce the magnesium content in cobalt hydroxide precipitates (MHPs) derived from copper-cobalt ore leaching solutions. The impact of the key parameters – SDS dosage, MgO/Co mass ratio, reaction time, temperature, stirring speed, and MgO concentration – was systematically evaluated to identify the optimal conditions for producing MHP with minimal magnesium impurity. The effect of SDS on MHP crystal morphology, surface area, pore size, reaction kinetics, and electrostatic potential was also assessed. Under optimal conditions, with SDS, a magnesium grade of 0.96% and a cobalt grade of 53.69% were achieved, alongside a cobalt recovery of 98.69%. Moreover, SDS was found to enhance the chemical control reaction, reducing the activation energy from 74.96 kJ/mol to 67.86 kJ/mol. This study offers valuable insights into the magnesium removal mechanism facilitated by SDS, addressing the challenge of high magnesium impurities in MHP products and providing guidance for impurity control in other precipitation processes.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.946
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.024
GPT teacher head0.300
Teacher spread0.277 · 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