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Record W4409829243 · doi:10.1016/j.seppur.2025.133232

Dual role of dissolved oxygen in sulfidation-xanthate flotation of anglesite from zinc leaching residue

2025· article· en· W4409829243 on OpenAlexaff
Qing Shi, Feng Zhang, Chao Qi, Yan Miao

Bibliographic record

VenueSeparation and Purification Technology · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsLakes Environmental (Canada)
Fundersnot available
KeywordsSulfidationXanthateZincLeaching (pedology)Residue (chemistry)ChemistryEnvironmental chemistryInorganic chemistrySulfurEnvironmental scienceOrganic chemistry

Abstract

fetched live from OpenAlex

The recovery of valuable anglesite (PbSO 4 ) from zinc leaching residue (ZLR) via sulfidation-xanthate flotation is hindered by poorly understood dissolved oxygen (DO) effects. This study clarifies the dual role of DO across sulfidation and xanthate adsorption stages. Micro-flotation experiments revealed that minimizing DO during the sulfidation stage reduced Na 2 S dosage, while optimal anglesite recovery required controlled DO levels (5.0–6.0 ppm) during xanthate conditioning stage. For sulfidation stage, zeta potential and XPS analysis revealed gradual surface oxidation with increasing DO, though PbS species remained relatively stable (0.2–6.0 ppm DO). For PAX conditioning stage, adsorption quantity, QCM-D and FTIR measurements revealed that 6.0 ppm DO enhanced xanthate adsorption on sulfurized anglesite. Bench-scale tests with nitrogen-regulated DO (1.8–4.2 ppm in sulfidation; 4.2–6.0 ppm in PAX conditioning) achieved higher Pb grade (23.3 % vs. 19.4 %) with comparable Pb recovery (60.9 % vs. 60.6 %), alongside a 26.7 % reduction in Na 2 S dosage (14,600 to 10,700 g/t). These findings establish DO as a pivotal operational parameter for balancing surface oxidation and xanthate adsorption, offering a reagent-saving strategy for anglesite recovery from ZLR.

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 score0.204
Threshold uncertainty score0.452

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 head0.006
GPT teacher head0.267
Teacher spread0.261 · 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

Citations4
Published2025
Admission routes1
Has abstractyes

Explore more

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