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Record W2943578166 · doi:10.1039/c9dt00937j

Iron-loaded amine/thiol functionalized polyester fibers with high catalytic activities: a comparative study

2019· article· en· W2943578166 on OpenAlexaff
Mohammad Neaz Morshed, Nabil Bouazizi, Nemeshwaree Behary, Julien Vieillard, Olivier Thoumire, Vincent Nierstrasz, Abdelkrim Azzouz

Bibliographic record

VenueDalton Transactions · 2019
Typearticle
Languageen
FieldChemistry
TopicNanomaterials for catalytic reactions
Canadian institutionsUniversité du Québec à Montréal
FundersEuropean Commission
KeywordsThiolPolyesterAmine gas treatingCatalysisChemistryPolymer chemistrySurface modificationCombinatorial chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Dispersion of iron nanoparticles (Fe-NPs) was achieved on polyester fabrics (PET) before and after the incorporation of dendrimers (PAMAM), 3-(aminopropyl) triethoxysilane (APTES) or thioglycerol (SH). The catalytic activity of the resulting materials (PET-Fe, PET-PAMAM-Fe, PET-APTES-Fe and PET-SH-FE) was comparatively investigated in the degradation of 4-nitrophenol (4-NP) and methylene-blue (MB). Full characterization through diverse instrumental methods allowed correlating the type of the organic moiety incorporated with the Fe content, catalytic properties and stability. The highest 4-NP degradation yield reached 99.6% in 12 min for PET-SH-Fe. The catalytic activity was explained in terms of reactant interaction with Fe-NPs. The 1st order reaction kinetics and pseudo-1st order adsorption kinetics provide evidence of the key role of reactant adsorption. These findings allow envisaging the preparation of fiber-based catalysts for potential uses in environmental and green chemistry.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.108
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.001

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 head0.015
GPT teacher head0.244
Teacher spread0.229 · 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.

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

Citations26
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueDalton TransactionsSame topicNanomaterials for catalytic reactionsFrench-language works237,207