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Record W2004336233 · doi:10.1038/srep07584

Strong-field plasmonic photoemission in the mid-IR at <1 GW/cm2 intensity

2015· article· en· W2004336233 on OpenAlexaff
Sarah A. Teichmann, Péter Rácz, Marcelo F. Ciappina, J. A. Pérez-Hernández, Alexandre Thai, J. Fekete, A. Y. Elezzabi, L. Veisz, Jens Biegert, Péter Dombi

Bibliographic record

VenueScientific Reports · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsUniversity of Alberta
FundersMax-Planck-GesellschaftMagyar Tudományos AkadémiaHungarian Scientific Research FundAgència de Gestió d'Ajuts Universitaris i de RecercaLaserlab-Europe
KeywordsPlasmonFemtosecondPhysicsWavelengthQuantum tunnellingIntensity (physics)PhotonField (mathematics)OptoelectronicsNanoscopic scaleOpticsCondensed matter physicsLaserQuantum mechanics

Abstract

fetched live from OpenAlex

We investigated nonlinear photoemission from plasmonic films with femtosecond, mid-infrared pulses at 3.1 μm wavelength. Transition between regimes of multi-photon-induced and tunneling emission is demonstrated at an unprecedentedly low intensity of <1 GW/cm(2). Thereby, strong-field nanophysics can be accessed at extremely low intensities by exploiting nanoscale plasmonic field confinement, enhancement and ponderomotive wavelength scaling at the same time. Results agree well with quantum mechanical modelling. Our scheme demonstrates an alternative paradigm and regime in strong-field physics.

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 score0.002
Threshold uncertainty score0.007

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)0.0020.000

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 head0.026
GPT teacher head0.283
Teacher spread0.258 · 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

Citations41
Published2015
Admission routes1
Has abstractyes

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