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Record W4206918845 · doi:10.3390/radiation2010007

Effects of Charge Carrier Trapping on Image Resolution of Multilayer Photoconductive Detectors: Application to Amorphous Selenium X-ray Detectors

2022· article· en· W4206918845 on OpenAlexafffund
M. Z. Kabir

Bibliographic record

VenueRadiation · 2022
Typearticle
Languageen
FieldMedicine
TopicDigital Radiography and Breast Imaging
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTrappingDetectorPhotoconductivityMaterials scienceX-ray detectorOptoelectronicsOpticsPhysics

Abstract

fetched live from OpenAlex

The effects of charge carrier trapping on image resolution in multilayer photoconductive imaging detectors was analyzed by developing an analytical model for calculating the modulation transfer function (MTF) of the imaging detectors. The MTF model was developed by considering the charge carrier trapping/recombination in the blocking layer and the distributed trapping in the bulk photoconductor layer of multilayer photoconductive imaging detectors. The relative importance of the bulk and blocking layer trapping, as well as the K-fluorescence reabsorption on the resolution, was also analyzed. The model was applied to amorphous selenium- (a-Se) based multilayer detectors for both chest radiographic and mammographic applications. The theoretical model agreed well with experimental results. The charge carrier trapping/recombination in the blocking layer and K-fluorescence reabsorption mainly controlled the resolution of the a-Se mammographic detectors. In addition to these two effects, the bulk trapping also had a significant effect on the resolution of chest radiographic detectors. The blocking layer thickness had more of a profound effect on the resolution than the amount of trap centers in the blocking layer. The resolution deteriorated drastically with increased blocking layer thickness.

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 score0.068
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.005
GPT teacher head0.235
Teacher spread0.230 · 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
Published2022
Admission routes2
Has abstractyes

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