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Record W2003535843 · doi:10.1103/physreva.67.012902

Stochastic treatment of nonequilibrium ion stopping in solids

2003· article· en· W2003535843 on OpenAlexafffund
Z. L. Mišković, F. O. Goodman, W. -K. Liu, You‐Nian Wang

Bibliographic record

VenuePhysical Review A · 2003
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Molecular Physics
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsProjectilePhysicsAtomic physicsIonStopping powerAdiabatic processNon-equilibrium thermodynamicsElectronNuclear physicsQuantum mechanics

Abstract

fetched live from OpenAlex

We study the energy loss of fast, hydrogenlike ions in thin solid foils, in the regime prior to the establishment of the ion-charge equilibrium. The projectile-charge evolution is described by a nonstationary, continuous-time Markov process, while the target response is described by a time-dependent dielectric-response formalism. We first derive the projectile self-energy in the presence of charge exchange, which is used to determine the bound-electron density in a self-consistent manner, by minimizing the total projectile energy in an adiabatic approximation. An expression for the ion energy-loss distribution is then used to derive the average value of the stopping power as a function of the traversal time in the foil, taking into account the projectile screening by the bound electron. The results of calculations for He ions in Al foils show significant coherence effects on the energy losses in the pre-equilibrium regime, which are interpreted by the overlap between the time delay in the target response and the characteristic time scale for the charge-changing collisions of the projectile with the target atoms.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.017
GPT teacher head0.306
Teacher spread0.289 · 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 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

Citations2
Published2003
Admission routes2
Has abstractyes

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