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Record W2614381225 · doi:10.1103/physreve.95.053208

Measurements of ionization states in warm dense aluminum with betatron radiation

2017· article· en· W2614381225 on OpenAlexafffund
Mianzhen Mo, Zhijiang Chen, S. Fourmaux, A. Saraf, S. Kerr, K. Otani, Ramona Masoud, J. C. Kieffer, Ying Y. Tsui, A. Ng, R. Fedosejevs

Bibliographic record

VenuePhysical review. E · 2017
Typearticle
Languageen
FieldEngineering
TopicIon-surface interactions and analysis
Canadian institutionsUniversity of British ColumbiaInstitut National de la Recherche ScientifiqueUniversity of Alberta
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaUniversity of OxfordCanada Foundation for Innovation
KeywordsBetatronAtomic physicsIonizationAbsorption (acoustics)Spectral lineIonFemtosecondLaserSpectroscopyAbsorption spectroscopyPhysicsElectronMaterials scienceOpticsNuclear physics

Abstract

fetched live from OpenAlex

Time-resolved measurements of the ionization states of warm dense aluminum via K-shell absorption spectroscopy are demonstrated using betatron radiation generated from laser wakefield acceleration as a probe. The warm dense aluminum is generated by irradiating a free-standing nanofoil with a femtosecond optical laser pulse and was heated to an electron temperature of ∼20-25 eV at a close-to-solid mass density. Absorption dips in the transmitted x-ray spectrum due to the Al^{4+} and Al^{5+} ions are clearly seen during the experiments. The measured absorption spectra are compared to simulations with various ionization potential depression models, including the commonly used Stewart-Pyatt model and an alternative modified Ecker-Kröll model. The observed absorption spectra are in approximate agreement with these models, though indicating a slightly higher state of ionization and closer agreement for simulations with the modified Ecker-Kröll model.

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 score0.001
Threshold uncertainty score0.004

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.301
Teacher spread0.283 · 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

Citations37
Published2017
Admission routes2
Has abstractyes

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