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Record W2086291922 · doi:10.1080/09593332708618694

Removal of Multiple-Metals from Contaminated Clay Minerals

2006· article· en· W2086291922 on OpenAlexaff
Loretta Y. Li

Bibliographic record

VenueEnvironmental Technology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsIlliteKaoliniteChemistryClay mineralsDesorptionMetal ions in aqueous solutionAdsorptionMetalLeaching (pedology)Inorganic chemistrySurface chargeNuclear chemistryMineralogySoil waterGeology

Abstract

fetched live from OpenAlex

Clay minerals spiked with multi-component metal ions (Cu(+2), Cd(+2), Pb(+2)) were decontaminated using different soil washing solutions. The desorption characteristics were determined by batch acid leaching with various acids. Removal of Cu(2+), Cd(+2) and Pb(+2) ions from variable charge minerals (e.g. kaolinite) required much less effort than their removal from constant-charge minerals (e.g. illite). The surface charge of a clay mineral had an important influence. When the numbers of H+ and Na+ ions available in the soil were increased by adding a buffer solution such as NaOAc-HOAc, heavy metals adsorbed on the clay surface transferred to the pore fluid. When more H+ or Na+ ions were available in the pore fluid, more Cu(+2), Cd(+2) and Pb(+2) ions were released into the equilibrium solution. Decreasing the pH led to more removal of heavy metal ions from kaolinite. The presence of Na+ ions facilitated the removal of heavy metals from contaminated illite. The selectivity for desorption was in the order Cu(+2) > Cd(+2) > Pb(+2) for all washing solutions investigated.

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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.201
Teacher spread0.196 · 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

Citations13
Published2006
Admission routes1
Has abstractyes

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