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Record W4306644965 · doi:10.1002/aelm.202200640

Halide Perovskites for Direct Conversion Megavoltage X‐Ray Detectors

2022· article· en· W4306644965 on OpenAlexafffund
Soumya Kundu, J. William O’Connell, Alexander Hart, Devon Richtsmeier, Magdalena Bazalova‐Carter, Makhsud I. Saidaminov

Bibliographic record

VenueAdvanced Electronic Materials · 2022
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaProvincial Health Services Authority
KeywordsScintillatorDetectorMaterials scienceX-ray detectorHalideOptoelectronicsX-rayGadoliniumEnergy conversion efficiencySensitivity (control systems)OpticsPhotonPhysicsChemistryElectronic engineering

Abstract

fetched live from OpenAlex

Abstract Megavoltage (MV) X‐ray detectors used in cancer treatment either suffer from low sensitivity (scintillators) or prohibitively high cost (direct conversion). Here solution‐processed direct‐conversion MV X‐ray detectors are demonstrated based on halide perovskites. The authors’ prototype devices show a sensitivity of ≈0.7 µC Gyair−1 cm–2, high photon‐to‐carrier conversion efficiency of 42 500%, and a signal‐to‐noise ratio of ≈1750 to 6 MV X‐ray beam of a medical linear accelerator. The detector shows a contrast of over −1.5% per cm of solid water, comparable to state‐of‐art commercial gadolinium oxysulfide (GOS) MV X‐ray scintillators. This work demonstrates the first prototype of low‐cost and efficient direct‐conversion MV 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 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 score0.002
Threshold uncertainty score0.005

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.201
Teacher spread0.197 · 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

Citations13
Published2022
Admission routes2
Has abstractyes

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