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Record W2995511614 · doi:10.1063/1.5128524

Quantifying the photothermal conversion efficiency of plasmonic nanoparticles by means of terahertz radiation

2019· article· en· W2995511614 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.

Bibliographic record

VenueAPL Photonics · 2019
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsConcordia UniversityInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsPhotothermal therapyNanomaterialsNanomedicineMaterials sciencePlasmonTerahertz radiationNanoparticlePlasmonic nanoparticlesNanotechnologyPhotothermal effectCharacterization (materials science)Refractive indexOptoelectronics

Abstract

fetched live from OpenAlex

The accurate determination of the photothermal response of nanomaterials represents an essential aspect in many fields, such as nanomedicine. Specifically, photothermal cancer therapies rely on the precise knowledge of the light-to-heat transfer properties of plasmonic nanoparticles to achieve the desired temperature-induced effects in biological tissues. In this work, we present a novel method for the quantification of the photothermal effect exhibited by nanoparticles in aqueous dispersions. By combining the spatial and temporal thermal dynamics acquired at terahertz frequencies, the photothermal conversion efficiency associated with the geometry of the plasmonic nanoparticles can be retrieved in a noncontact and noninvasive manner. The proposed technique can be extended to the characterization of all those nanomaterials which feature a temperature-dependent variation of the refractive index in the terahertz regime.

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)0.000
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 score0.363
Threshold uncertainty score0.387

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 head0.006
GPT teacher head0.200
Teacher spread0.193 · 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