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Record W2124789191 · doi:10.3390/met5042034

Copper and Cyanide Extraction with Emulsion Liquid Membrane with LIX 7950 as the Mobile Carrier: Part 1, Emulsion Stability

2015· article· en· W2124789191 on OpenAlexaff
Diankun Lu, Yongfeng Chang, Wei Wang, Feng Xie, Edouard Asselin, David Dreisinger

Bibliographic record

VenueMetals · 2015
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsUniversity of British Columbia
FundersFundamental Research Funds for the Central UniversitiesCentral University Basic Research Fund of China
KeywordsCyanideEmulsionMembraneChemistryCopperExtraction (chemistry)ChromatographyInorganic chemistryChemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

The potential use of emulsion liquid membranes (ELMs) with LIX 7950 as the mobile carrier to remove heavy metals from waste cyanide solutions has been proposed. Relatively stable ELMs with reasonable leakage and swelling can be formed under suitable mixing time and speed during emulsification. The concentration of LIX 7950 and Span 80 in the membrane phase, KOH in the internal phase and the volume ratio of membrane to internal phases are also critical to ELM formation. The efficiency of copper and cyanide removal from dilute cyanide solution by ELMs is related to ELM stability to some extent. More than 90% copper and cyanide can be removed from dilute cyanide solutions by ELMs formed under suitable experimental conditions.

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.023
GPT teacher head0.261
Teacher spread0.238 · 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

Citations19
Published2015
Admission routes1
Has abstractyes

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