MétaCan
Menu
Back to cohort
Record W3107748028 · doi:10.1039/d0nr06493a

Exciting clusters, what does off-resonance actually mean?

2020· article· en· W3107748028 on OpenAlexafffund
Goonay Yousefalizadeh, Shideh Ahmadi, Nicholas J. Mosey, Kevin G. Stamplecoskie

Bibliographic record

VenueNanoscale · 2020
Typearticle
Languageen
FieldMaterials Science
TopicNanocluster Synthesis and Applications
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceResonance (particle physics)Noble metalNanotechnologyMetalChemical physicsPhysicsAtomic physics

Abstract

fetched live from OpenAlex

, as well as 10 other Au and Ag clusters using femtosecond pump/probe transient absorption spectroscopy (fsTAS). Excited state absorbance (ESA) ubiquitous to thiolated Au and Ag clusters is used herein as an optical signature of two-photon absorbances of the 11 different Au and Ag clusters, which does not require high quantum yields of emission. The large selection of clusters, studied with a single laser system, allows us to draw conclusions on the role of the particular metal, cluster size/structure, and the effects of the ligands on the ability to absorb multiple NIR photons. The use of a laser with a 1028 nm excitation also allows us to investigate the dramatic effect of excitation wavelength and explain why laser wavelength has led to large variances in the non-linear responses reported for clusters to date. We discuss the double resonance mechanism, responsible for giant two photon absorbance cross-sections, helping match properties of metal clusters with experimental conditions for maximizing signal/response in multiphoton applications.

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 score0.164
Threshold uncertainty score0.926

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.019
GPT teacher head0.241
Teacher spread0.222 · 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

Citations12
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueNanoscaleSame topicNanocluster Synthesis and ApplicationsFrench-language works237,207