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Record W4412181895 · doi:10.1002/cjce.70026

Molecular simulation using electronic structure methods on copper and lead ions separation with thiourea‐functionalized resin

2025· article· en· W4412181895 on OpenAlexafffundvenue
Yahui Zhang, Salem Elfeghe, Uyen Dao, Sohrab Zendehboudi

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicChemical Synthesis and Characterization
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsThioureaMulliken population analysisDensity functional theoryMolecular orbitalCopperChemistryAdsorptionIonInorganic chemistryPhysical chemistryMetal ions in aqueous solutionMoleculeComputational chemistryMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The selective separation of copper and lead ions in acidic solutions was successfully achieved utilizing a thiourea‐functionalized resin, specifically Puromet MTS 9140, which demonstrated a strong adsorption preference for copper ions. To further elucidate the adsorption mechanism of Cu(II) ions, density functional theory (DFT) calculations using the B3LYP technique and ab initio unrestricted Hartree–Fock (UHF) methods were employed to analyze electronic structure features, such as Mulliken population/charge, frontier orbital energies, and Gibbs free energy (∆G° f ) of three structural units in the resin (i.e., m‐thiourea‐styrene, o‐thiourea‐styrene, and p‐thiourea‐styrene) and their interaction with Cu(II) and Pb(II) ions. The Frontier molecular orbital (FMO) theory analysis using the B3LYP DFT method verified that the resin bearing thiourea group interacts with Cu(II) and Pb(II) ions through S atom bonding. This research enhances the understanding of mechanisms involved in the recovery and removal of metal ions from aqueous systems using selective resins, indicating potential applications in water and 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.215

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 head0.006
GPT teacher head0.249
Teacher spread0.242 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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