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Record W7128251515 · doi:10.1063/5.0272632

Effects of non-uniform electric field on the charge collection efficiency in radiation detectors: Deviation from Hecht formula

2025· article· en· W7128251515 on OpenAlexafffund
M. Z. Kabir, Mahboob Liaquat

Bibliographic record

VenueJournal of Applied Physics · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectric fieldSaturation velocityCharge carrierDetectorSpace chargeParticle detectorIonizationVelocity saturationElectron mobility

Abstract

fetched live from OpenAlex

The Charge Collection Efficiency (CCE) of a radiation detector under a non-uniform electric field has been examined by developing a semi-analytical and a numerical model. We consider the Carrier Packet Drift Analysis for the semi-analytical model and a numerical solution of the continuity and Poisson‘s equations for the numerical model. The space charge due to ionized dopants and the trapped charges of photogenerated carriers in the bulk photoconductor layer of the detector are considered. We analyze the electric field distributions and CCE of the detector under various charge carrier transport parameters and detector operating conditions. The CCE under the non-uniform electric field deviates significantly from the uniform field case as determined by the Hecht Collection Efficiency (HCE) formula. In most cases, the CCE under the non-uniform electric field deteriorates as compared to HCE. However, the CCE under the non-uniform electric field can also be improved for certain values of normalized carrier lifetimes (ratio of carrier lifetime to its transit time) provided that the radiation (photons or particles) absorption occurs mostly near the radiation-receiving electrode. The theoretical model has been applied to the published experimental results in perovskite x-ray detectors for general radiographic applications and found good agreement.

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 score0.466
Threshold uncertainty score0.306

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.004
GPT teacher head0.216
Teacher spread0.213 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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