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Record W2124338055 · doi:10.1117/12.504864

Micro x-ray sources from femtosecond laser plasmas

2004· article· en· W2124338055 on OpenAlexaff
Cristina Şerbănescu, Jaime Santiago, R. Fedosejevs

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPlasmaFemtosecondLaserAtomic physicsEnergy conversion efficiencyElectronPlasma diagnosticsAbsorption (acoustics)Materials scienceSapphireX-rayAnalytical Chemistry (journal)OpticsPhysicsOptoelectronicsChemistryNuclear physics

Abstract

fetched live from OpenAlex

Ultrabright and ultrashort x-ray pulses may be used for time resolved studies of phase transitions in materials and potentially for x-ray microscopy applications. Through the interaction of high intensity ultrashort laser pulses (~100fs, 10<sup>15</sup> -10<sup>17</sup> W/cm<sup>2</sup>) with solid targets, high temperature and high density plasma is formed on the material surface. Electrons are accelerated in the plasma and multi keV x-rays are generated when they interact with the target material. Such hot electrons are produced from resonance absorption and other nonlinear interactions both at the solid density surface and in the underdense plasma. Initial experimental measurements of keV x-ray emission from microplasmas generated by 130fs, 800nm, 0.5mJ Ti:Sapphire laser pulses focused to intensities of ~10<sup>16</sup> -10<sup>17</sup> W/cm<sup>2</sup> onto a solid target have been carried out. The keV x-ray emission has been characterized both in air and in vacuum. In particular, the scaling of x-ray conversion efficiency and the dependence on pulse energy, angle of incidence and pressure have been studied. The x-ray conversion efficiency improves through the use of a prepulse, indicating that the interactions in the underdense plasma also contribute to hot electron and keV x-ray generation.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.535
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.008
GPT teacher head0.215
Teacher spread0.207 · 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.

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

Citations6
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicLaser-Plasma Interactions and DiagnosticsFrench-language works237,207