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Record W4224228839 · doi:10.1021/acsomega.2c00417

Separation of Lead and Copper Ions in Acidic Media Using an Ion-Exchange Resin with a Thiourea Functional Group

2022· article· en· W4224228839 on OpenAlexafffund
Salem Elfeghe, Qiuyue Sheng, Yahui Zhang

Bibliographic record

VenueACS Omega · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIon exchangeChemistryCopperIon-exchange resinThioureaInorganic chemistryAdsorptionEndothermic processFreundlich equationIonMetalMetal ions in aqueous solutionLangmuirOrganic chemistry

Abstract

fetched live from OpenAlex

This research studied the selective separation of lead and copper ions in acidic solutions using Puromet MTS 9140 resin with a thiourea functional group. The effects of operation parameters, that is, resin dosage, solution pH, ion exchange time, metal concentration, and temperature, on metal ion exchange were investigated using batch-test protocols. Ion-exchange experimental data were analyzed with Langmuir, Freundlich, and Temkin models. The results demonstrate that the MTS 9140 resin has ion-exchange selectivity for copper ions over lead ions. The ion-exchange recovery of Cu exceeded 95%, while Pb coloading was under 19% with MTS 9140 resin dosage of 0.070 g/mL in the pH range of 2.5 to 4.5. The kinetic studies showed that the ion exchange process could be better described by the pseudo-second-order model for lead and copper ions. The temperature dependence indicates the endothermic nature of the ion-exchange process. The resin also showed potential application as an effective adsorbent for removing heavy metal ions in water or wastewater treatment.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.844
Threshold uncertainty score0.530

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 head0.031
GPT teacher head0.251
Teacher spread0.220 · 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

Citations32
Published2022
Admission routes2
Has abstractyes

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