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Record W2316990143 · doi:10.1021/acs.iecr.5b00177

Mussel-Inspired Green Metallization of Silver Nanoparticles on Cellulose Nanocrystals and Their Enhanced Catalytic Reduction of 4-Nitrophenol in the Presence of β-Cyclodextrin

2015· article· en· W2316990143 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2015
Typearticle
Languageen
FieldChemistry
TopicNanomaterials for catalytic reactions
Canadian institutionsCelluForce (Canada)University of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsSilver nanoparticle4-NitrophenolCatalysisCyclodextrinStabilizer (aeronautics)NanocrystalReducing agentChemical engineeringNanoparticleCelluloseSelective catalytic reductionNanotechnologyChemistryNitrophenolMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

A green approach to anchor silver nanoparticles (AgNPs) onto the surface of cellulose nanocrystals (CNCs) coated with mussel-inspired polydopamine (PDA) at room temperature in the absence of a stabilizer and a reducing agent is proposed. The resulting nanohybrids possessed a core–shell structure with numerous “satellites” of silver nanoparticles decorating the CNC surface. The nanocatalyst displayed superior dispersibility over pristine AgNPs and was six times more efficient in catalyzing the reduction of 4-nitrophenol. By associating the CNC hybrid with β-cyclodextrin to promote host–guest interactions, the catalytic process was accelerated. The associated physicochemical parameters associated with the catalytic process were investigated and compared.

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.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.077
GPT teacher head0.294
Teacher spread0.217 · 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