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Record W1503977152 · doi:10.1002/clen.201300210

Comparative Binding of Endocrine Disrupting Compounds and Pharmaceuticals with Polydopamine‐ and Polypyrrole‐coated Magnetic Nanoparticles

2014· article· en· W1503977152 on OpenAlexafffund
Musharraf Miah, Zafar Iqbal, Edward P. C. Lai

Bibliographic record

VenueCLEAN - Soil Air Water · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsCarleton University
FundersCanadian Water Network
KeywordsPolypyrroleChemistryBisphenol ANanoparticleMagnetic nanoparticlesNuclear chemistryCapillary electrophoresisPyreneChromatographyNanotechnologyOrganic chemistryPolymerMaterials scienceEpoxyPolymerization

Abstract

fetched live from OpenAlex

Global concern about pharmaceutical and endocrine disrupting compounds in water resources is at its most intense due to their adverse effects on human health and environmental sustainability. Polydopamine‐coated magnetic nanoparticles (MNPs@PDA) and polypyrrole‐coated magnetic nanoparticles (MNPs@PPy) were successfully synthesized with strong magnetic attraction for easy separation. Their binding efficiencies with bisphenol A (BPA), metformin (MF), naphthalene acetic acid (NAA), phenformin (PF), quinine sulfate (QS), and triclosan (TC) in water were determined using capillary electrophoresis with UV detection. At a concentration of 200 mg mL −1 , the binding efficiencies of MNPs@PPy were found to be 99 ± 1% BPA, 34 ± 4% MF, 39 ± 6% NAA, 99 ± 1% PF, 98 ± 2% QS, and 99 ± 1% TC, whereas MNPs@PDA afforded 65 ± 5% BPA, 14 ± 6% MF, 21 ± 4% NAA, 99 ± 1% PF, 94 ± 2% QS, and 92 ± 3% TC. Hence, both MNPs@PDA and MNPs@PPy have a high capacity for efficient removal of aromatic compounds from water.

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 score0.003
Threshold uncertainty score0.444

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 head0.013
GPT teacher head0.281
Teacher spread0.267 · 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

Citations7
Published2014
Admission routes2
Has abstractyes

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