MétaCan
Menu
Back to cohort
Record W2982199718 · doi:10.1117/12.2533528

Ultrafast laser-matter interaction with nanostructured targets

2019· preprint· en· W2982199718 on OpenAlexaff
R. S. Marjoribanks, L. Lecherbourg, J. E. Sipe, G. Kulcsár, A. Héron, J. C. Adam, Alan Miscampbell, Georgia Thomas, Ryan Royle, Oliver Humphries, Remy H. H. Ko, S. Le Moal, J. Li, Thomas R. Preston, Quincy Y. van den Berg, Muhammad Kasim, Bob Nagler, Eric Galtier, Eric Cunningham, J. S. Wark, S. M. Vinko

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsUniversity of Toronto
FundersEngineering and Physical Sciences Research Council
KeywordsMaterials scienceLaserPlasmaUltrashort pulseFemtosecondElectronNanowireIonizationDielectricOptoelectronicsOpticsIonPhysics

Abstract

fetched live from OpenAlex

Conventional solid-density laser-plasma targets quickly ionize to make a plasma mirror, which largely reflects ultra-intense laser pulses. This Fresnel reflection at the plane boundary largely wastes our e orts at ultra-intense laser/solid interaction, and limits target heating to nonlinear generation of high-energy electrons which penetrate inward. One way around this dual problem is to create a material with an anisotropic dielectric function, for instance by nanostructuring a material in such a way that it cannot support the material responses which generate a specularly reflected beam. We present linear theory for metallic and plasma nanowires, particle-incell simulations of the interaction of ultra-intense femtosecond pulses with nickel nanowires, showing penetration of laser light far deeper than a nickel skin-depth, helping to uniformly heat near-solid material to conditions of high energy-densities, and XFEL experiments giving insight into their ionization and excitation.

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 score0.005
Threshold uncertainty score0.018

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 studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.006
GPT teacher head0.208
Teacher spread0.202 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

Same topicLaser Material Processing TechniquesFrench-language works237,207