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Bioleaching of arsenic-rich cobalt mineral resources, and evidence for concurrent biomineralisation of scorodite during oxidative bio-processing of skutterudite

2020· article· en· W3035846979 on OpenAlexaboutno aff
D. Barrie Johnson, Agnieszka Dybowska, P. F. Schofield, Richard Herrington, Sarah L. Smith, Ana Laura Santos

Bibliographic record

VenueHydrometallurgy · 2020
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsnot available
FundersNatural Environment Research CouncilSight Research UK
KeywordsChemistryBioleachingArsenicCobaltMineralEnvironmental chemistryMineral processingMetallurgyInorganic chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Experiments were carried out to test the amenabilities of mineral deposits that contained cobalt deported in arseno-sulfide (cobaltite) and arsenide (skutterudite) minerals, to oxidative bioleaching at mesophilic temperatures and low pH. An ore sample from the Iron Mask deposit (Canada) and a mineral concentrate from a working mine (Bou Azzer, Morocco) were thoroughly characterised, both prior to and following bio-processing. A “top down” approach, using microbial consortia including (initially) 13 species of mineral-degrading acidophiles was used to bioleach the ore and concentrate in shake flasks and bioreactors. Cobalt was successfully liberated from both materials tested (up to 93% from the ore, and 49% from the concentrate), though the chemistries of the leach liquors were very different, with redox potentials being >200 mV lower, and concentrations of soluble arsenic about 7-fold greater, with the concentrate. Addition of pyrite to the arsenide concentrate was found to promote the biomineralisation of scorodite (ferric arsenate), which was detected by both XRD and SEM-EDX, but was not found in bioleached residues of the arseno-sulfide ore. A model was proposed wherein pyrite had three critical roles in facilitating the genesis of scorodite: (i) providing the catalytic surface to promote the oxidation of As (III) to As (V); (ii) acting as a putative “seed” for scorodite crystallisation; (iii) being a secondary source of iron, since the molar ratios of iron:arsenic in the concentrate itself (0.19:1) was well below that required for effective removal of soluble arsenic as scorodite (1:1). This work provided proof of concept that cobalt arseno-sulfide and arsenide ores and concentrates are amenable to bio-processing, and also that it is possible to induce concurrent solubilisation of arsenic from primary minerals and immobilisation in a secondary mineral, scorodite.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

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 head0.059
GPT teacher head0.278
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), 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

Citations24
Published2020
Admission routes1
Has abstractyes

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