MétaCan
Menu
Back to cohort
Record W2921606260 · doi:10.14288/1.0375855

Floatability of jarosites in the presence of anionic surfactants

2019· article· en· W2921606260 on OpenAlexaff
Avery Payne

Bibliographic record

VenuecIRcle (University of British Columbia) · 2019
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsChemistry

Abstract

fetched live from OpenAlex

Five jarosite samples were synthesized and their surface properties relevant to froth flotation were examined. Sodium jarosite, low-silver sodium jarosite (3.47 % Ag), high-silver sodium jarosite (12.84% Ag), potassium jarosite, and high-silver potassium jarosite (5.56% Ag) were synthetized in an autoclave. No significant differences in zeta potential characteristics [pH 2-10] were observed between the samples, with a common isoelectric point at pH 5. Reagent adsorption and Hallimond tube (single mineral) flotation experiments were conducted with three anionic surfactants at pH 3.5 and at pH 10. The three reagents were: sodium laurate, sodium dodecyl sulfate, and octanohydroxamic acid. These surfactants represented different classes of flotation collectors: weak-electrolyte type (lauric acid and hydroxamic acid), strong-electrolyte type (dodecyl sulfate), highly soluble in water (dodecyl sulfate and hydroxamate) and poorly soluble in water (lauric acid). The experimental results were analyzed in terms of correlations between zeta potential, surfactant adsorption, and flotation results in order to enhance the fundamental understanding of surfactant-jarosite interactions. It was found from the adsorption and flotation results that the floatability of jarosites in the presence of the tested reagents followed two main trends. For several jarosite-reagent-pH combinations, the floatability of the mineral increased steadily with the increasing adsorption density of the reagent. In other cases, a minimum concentration of the reagent was required to initiate flotation even though the adsorption density of the surfactant continuously increased. It was postulated that in the latter case, the transition from poor to good flotation coincided with a change in the adsorption mechanism of the surfactant, while in the former case, one mechanism dominated the interaction. It was concluded that the adsorption of the anionic surfactants was driven by chemical interactions with iron sites on the jarosite surfaces regardless of the jarosite type. When analyzing the adsorption data, it was recognized that the jarosite surface at pH 10 was coated by a goethite (FeO(OH)) layer, while at pH 3.5 the surfactants adsorbed on a clean jarosite surface. No systematic trends were observed as a function of the silver content. Based on all the data, recommendations on the most promising flotation conditions were also made.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score0.980

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 integrity0.0000.000
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 head0.008
GPT teacher head0.169
Teacher spread0.161 · 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 designObservational
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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venuecIRcle (University of British Columbia)Same topicMetal Extraction and BioleachingFrench-language works237,207