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Record W2023868405 · doi:10.1088/0022-3727/33/5/310

Calculation of inelastic cross-sections for the interaction of electrons with amorphous selenium

2000· article· en· W2023868405 on OpenAlexafffund
M. Lachaı̂ne, B. G. Fallone

Bibliographic record

VenueJournal of Physics D Applied Physics · 2000
Typearticle
Languageen
FieldMaterials Science
TopicLuminescence Properties of Advanced Materials
Canadian institutionsMcGill UniversityUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectronPhysicsAtomic physicsNuclear cross sectionCross section (physics)DipoleAmorphous solidMonte Carlo methodNuclear physicsChemistryElastic scatteringScatteringOptics

Abstract

fetched live from OpenAlex

Research into medical x-ray detectors based on amorphous selenium (a-Se) has seen a resurgence lately due to the advent of flat-panel active matrix technology. There is presently no quantitative theory, however, to explain the signal formation due to the interaction of ionizing radiation with a-Se. One of the main obstacles in the characterization of this process is the lack of suitable interaction cross-sections. In this work we develop an expression for the calculation of inelastic electron cross-sections from a dipole oscillator strength distribution, which includes relativistic and exchange effects. We construct the oscillator strength distribution from known experimental results and calculate the total and cumulative differential cross-sections for incident electron energies ranging from a few eV to 23 MeV. The total cross-section agrees with that of isolated Se atoms at high energies, as expected, and provides the low energy cross-sections which depend on the dielectric properties of the amorphous state. From these cross-sections, Monte Carlo codes can be developed which simulate the creation of individual electron-hole pairs, which will lead to a better understanding of the signal-formation process in a-Se based x-ray detectors.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.387

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 head0.016
GPT teacher head0.270
Teacher spread0.254 · 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 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
Published2000
Admission routes2
Has abstractyes

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