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Record W3183945775 · doi:10.1021/acs.langmuir.1c01154

In Situ Preparation of Composite Redox-Active Micelles Bearing Pd Nanoparticles for the Reduction of 4-Nitrophenol

2021· article· en· W3183945775 on OpenAlexafffund
Jiayi You, Ian Manners, Hongjing Dou

Bibliographic record

VenueLangmuir · 2021
Typearticle
Languageen
FieldChemistry
TopicNanomaterials for catalytic reactions
Canadian institutionsUniversity of Victoria
FundersScience and Technology Commission of Shanghai MunicipalityShanghai Education Development FoundationShanghai Municipal Education CommissionNational Natural Science Foundation of ChinaCanada Research Chairs
KeywordsNanomaterial-based catalystMicelleNanoparticleSodium borohydrideCatalysisRedoxChemical engineeringChemistryX-ray photoelectron spectroscopyReducing agent4-NitrophenolMaterials scienceInorganic chemistryNanotechnologyOrganic chemistryAqueous solution

Abstract

fetched live from OpenAlex

Owing to the redox activity of the poly(ferrocenylsilane)-based polymer, several noble metal nanoparticles can be successfully prepared. As reported herein, the in situ preparation of Pd nanoparticles was performed using a redox-active platform of poly(ferrocenylmethylethylthiocarboxylpropylsilane) (PFC) micelles. PFC/Pd nanocomposites (NCs) with Pd nanoparticles uniformly dispersed at the surface of PFC nanospheres were obtained. The morphology of PFC/Pd NCs was further confirmed via high-resolution transmission electron microscopy and X-ray photoelectron spectroscopy. Taking advantage of Pd nanoparticles, the PFC/Pd NCs showed significant catalytic activity during the reduction process of 4-nitrophenol by sodium borohydride. Although PFC micelles themselves showed no catalytic activity, they promoted the catalytic behavior of Pd nanoparticles obviously by anchoring the Pd nanoparticles at their surface to avoid the aggregation and leaching of Pd nanoparticles. In all, PFC/Pd NCs exhibited great potential as a composite nanocatalyst. Moreover, the PFC micelle was found to be a desired platform for nanocatalysts.

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 score0.001
Threshold uncertainty score0.207

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 head0.016
GPT teacher head0.275
Teacher spread0.259 · 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

Citations8
Published2021
Admission routes2
Has abstractyes

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