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Record W2947630977 · doi:10.1002/cjce.23519

Bioleaching of metal from waste stream using a native strain of <i>Acidithiobacillus</i> isolated from a coal mine drainage

2019· article· en· W2947630977 on OpenAlexvenueno aff
Solange Kazue Utimura, Santiago Justo Arévalo, Carlos Gonzalo Alvarez Rosario, Mauro Quiñones Aguilar, Jorge Alberto Soares Tenório, Denise Crocce Romano Espinosa

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsnot available
Fundersnot available
KeywordsBioleachingAcidithiobacillus ferrooxidansAcid mine drainageMetallurgyCopperPulp (tooth)CoalShakerMicroorganismPulp and paper industryAcidithiobacillusWaste managementChemistryMaterials scienceGeologyBacteria

Abstract

fetched live from OpenAlex

In this study, the feasibility of using a biohydrometallurgical technique for selective metals recovery from electronic waste (e‐waste) by bacterial bioleaching was investigated. Acidithiobacillus was identified in coal mining acid mine drainage (AMD). The microorganism was studied using specific sequencing of a 16s rDNA fragment. The potential for the dissolution of copper from waste printed wire boards (PWBs) using the isolated Acidithiobacillus ferrooxidans (A. ferroxidans) was evaluated. The bioleaching experiments were performed in an orbital shaker at 30 °C and 170 rpm, with 10 % (v/v) inoculum and a pulp density of 30 g/L. The copper concentration was determined by energy dispersive x‐ray fluorescence (XRF). The result shows that copper recovery from PWBs using our A. ferrooxidans strain was 95 % after 8 days, which showed the feasibility of this 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.

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 score0.255
Threshold uncertainty score0.722

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 integrity0.0000.001
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 head0.010
GPT teacher head0.192
Teacher spread0.182 · 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

Citations10
Published2019
Admission routes1
Has abstractyes

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