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Record W4396929551 · doi:10.1039/d4nh00086b

Protector-free, non-plasmonic silver quantum clusters by femtosecond pulse laser irradiation: <i>in situ</i> binding on nanocellulose filaments for improved catalytic activity and cycling performance

2024· article· en· W4396929551 on OpenAlexafffund
Toyoko Imae, Shambel Abate Marye, Ling Wang, Orlando J. Rojas

Bibliographic record

VenueNanoscale Horizons · 2024
Typearticle
Languageen
FieldMaterials Science
TopicNanocluster Synthesis and Applications
Canadian institutionsUniversity of British Columbia
FundersNational Taiwan University of Science and TechnologyMinistry of Science and Technology, TaiwanNational Taiwan UniversityCanada Excellence Research Chairs, Government of CanadaCanada Foundation for InnovationKAUTE-Säätiö
KeywordsFemtosecondNanocelluloseMaterials scienceIrradiationPlasmonLaserAqueous solutionIn situOptoelectronicsPhotochemistryNanotechnologyIonChemical engineeringOpticsChemistryOrganic chemistryCellulose

Abstract

fetched live from OpenAlex

to wet-spun filaments of cellulose nanofibrils by pulsed laser irradiation. The obtained hybrid filaments as well as free silver quantum clusters revealed a catalytic activity remarkably higher than that of free gold quantum clusters; moreover, the hybrid filaments were found to show improved stability and cycling performance for silver-based catalysis. The present results indicate the potential of femtosecond laser irradiation to generate clusters as well as hybrid systems with excellent performance and reactivity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.010
GPT teacher head0.237
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2024
Admission routes2
Has abstractyes

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