MétaCan
Menu
Back to cohort
Record W1561846197 · doi:10.1109/plasma.2002.1030270

Plasma pressure scaling of laser ablation generated surface acoustic waves

2003· article· en· W1561846197 on OpenAlexaff
Arzu Sardarli, James Gospodyn, R. Fedosejevs

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Surface Polishing Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLaserOpticsAcoustic waveMaterials scienceAmplitudeWavelengthPlasmaSurface acoustic waveLaser ablationAcousticsPhysics

Abstract

fetched live from OpenAlex

Summary form only given, as follows. Acoustic probing of materials is a powerful tool in investigating material characteristics and in identifying and locating internal defects in materials. Laser produced plasmas can be employed in order to generate large amplitude acoustic pulses in the material. Large plasma pressures can be generated at the surface even with millijoule level laser pulses leading to large acoustic amplitudes as compared to thermoelastically driven waves, which are often employed. We are studying the application of surface acoustic waves (SAWs) for the non-contact single-surface probing of materials. However, the relation between the amplitude of the SAW and the plasma pressure which excites the wave must be established. UV laser ablatively driven SAWs on aluminum are being studied, generated with either a KrF laser at a wavelength of 248 nm or a 4th harmonic Nd:YAG laser at a wavelength of 266 nm with laser pulse energies in the range of 1 to 60 mJ. The scaling of the SAW amplitude as a function of laser pulse energy and intensity has been measured. In order to compare the experimentally measured SAW waveforms with theoretical expectations a numerical model has been developed.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.516
Threshold uncertainty score0.490

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 head0.011
GPT teacher head0.227
Teacher spread0.217 · 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 designSimulation or modeling
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

Citations0
Published2003
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Surface Polishing TechniquesFrench-language works237,207