MétaCan
Menu
Back to cohort
Record W2003561433 · doi:10.1021/nl302200w

Plasma Mediated off-Resonance Plasmonic Enhanced Ultrafast Laser-Induced Nanocavitation

2012· article· en· W2003561433 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

VenueNano Letters · 2012
Typearticle
Languageen
FieldEngineering
TopicLaser-Ablation Synthesis of Nanoparticles
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsFemtosecondPlasmonLaserMaterials scienceUltrashort pulseResonance (particle physics)PlasmaSurface plasmon resonanceParticle (ecology)NanosecondOptoelectronicsNanoparticleNanotechnologyOpticsAtomic physicsPhysics

Abstract

fetched live from OpenAlex

The generation of nanobubbles around plasmonic nanostructures is an efficient approach for imaging and therapy, especially in the field of cancer research. We show a novel method using infrared femtosecond laser that generates ≈800 nm bubbles around off-resonance gold nanospheres using 200 mJ/cm(2) 45 fs pulses. We present experimental and theoretical work that demonstrate that the nanobubble formation results from the generation of a nanoscale plasma around the particle due to the enhanced near-field rather than from the heating of the particle. Energy absorbed in the nanoplasma is indeed more than 11 times the energy absorbed in the particle. When compared to the usual approach that uses nanosecond laser to induce the extreme heating of in-resonance nanoparticles to initiate bubble formation, our off-resonance femtosecond technique is shown to bring many advantages, including avoiding the particles fragmentation, working in the optical window of biological material and using the deposited energy more efficiently.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.009
Threshold uncertainty score1.000

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)0.0000.001

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 head0.012
GPT teacher head0.210
Teacher spread0.198 · 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