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Record W4391165082 · doi:10.1088/1361-6528/ad2233

Plasmon-enhanced multi-photon excited photoluminescence of Au, Ag, and Pt nanoclusters

2024· article· en· W4391165082 on OpenAlexaff
J. Bornacelli, C. Torres-Torres, A. Crespo-Sosa, J. A. Reyes‐Esqueda, A. Oliver

Bibliographic record

VenueNanotechnology · 2024
Typearticle
Languageen
FieldMaterials Science
TopicNanocluster Synthesis and Applications
Canadian institutionsUniversité de Sherbrooke
FundersUniversidad Nacional Autónoma de MéxicoConsejo Nacional de Ciencia y TecnologíaInstituto Politécnico Nacional
KeywordsPhotoluminescenceNanoclustersMaterials sciencePlasmonExcited stateOptoelectronicsNanoparticlePhotoexcitationPhotochemistryNanotechnologyAtomic physicsChemistryPhysics

Abstract

fetched live from OpenAlex

matrix by ion implantation. The thermal annealing process allows to obtain a system composed of larger plasmonic metal nanoparticles (NPs) surrounded by photoluminescent ultra-small metal NCs. By exciting at 1064 nm, visible emission, ranging from 450 to 800 nm, was detected. The second and fourth-order nature of the multiphoton process was verified in a power-dependent study measured for each sample below the damage threshold. Experiments show that Au and Ag NCs exhibit a four-fold enhanced multiphoton excited photoluminescence with respect to that observed for Pt NCs, which can be explained as a result of a plasmon-mediated near-field process that is of less intensity for Pt NPs. These findings provide new opportunities to combine plasmonic nanoparticles and photoluminescent nanoclusters inside a robust inorganic matrix to improve their optical properties. Plasmon-enhanced multiphoton excited photoluminescence from metal nanoclusters may find potential application as ultrasmall fluorophores in multiphoton sensing, and in the development of solar cells with highly efficient energy conversion modules.

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 score0.014
Threshold uncertainty score0.627

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 head0.013
GPT teacher head0.255
Teacher spread0.241 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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