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Record W2587918867 · doi:10.5539/ijc.v9n2p10

Multi-templated Pb-Zn-Hg Ion Imprinted Polymer for the Selective and Simultaneous Removal of Toxic Metallic Ions from Wastewater

2017· article· en· W2587918867 on OpenAlexvenueno aff
Morlu Stevens, Bareki S. Batlokwa

Bibliographic record

VenueInternational Journal of Chemistry · 2017
Typearticle
Languageen
FieldChemistry
TopicAnalytical chemistry methods development
Canadian institutionsnot available
FundersBotswana International University of Science and Technology
KeywordsChemistrySorbentMetal ions in aqueous solutionMethacrylic acidEthylene glycol dimethacrylatePolymerizationMolecular imprintingFourier transform infrared spectroscopyMonomerEthylene glycolPolymerNuclear chemistryAdsorptionIonInorganic chemistrySelectivityChemical engineeringOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

A multi-templated Pb-Zn-Hg ion imprinted polymer was synthesized to demonstrate the selective and simultaneous removal of the named targeted ions. Molecular Imprinting Technology was employed, guided by thermal bulk polymerization method, methacrylic acid as the functional monomer and ethylene glycol dimethylacrylate as crosslinking agent in the presence of the initiator azobisisobutyronitrile; Pb(II), Zn(II) and Hg(II) ions as template ions, and 1,10-phenanthroline as the complexing agent. Rigorous, optimal template removal method was employed. Physical and chemical properties of the sorbent were investigated using Fourier Transform Infrared Spectroscopy, X-ray Diffraction and Atomic Force Microscopy. Operational parameters: time, pH and sorbent dosage for the rebinding batch experiments were optimized and found to be 15 min, 7.5 and 666.7 mg/L respectively. Percentage recoveries of the removed targeted ions from spiked samples were: 92.14 ± 0.16%, 106.09 ± 0.17% and 99.86 ± 0.04%. The synthesized sorbents showed good selectivity towards the targeted metal ion by removing 90% - 98% of the templated ions as compared to 58% - 62% of the competitive ions.

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)0.002
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 score0.008
Threshold uncertainty score0.656

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 science0.0010.000
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 head0.029
GPT teacher head0.322
Teacher spread0.293 · 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

Citations10
Published2017
Admission routes1
Has abstractyes

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