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Record W2155233413 · doi:10.1109/cjece.2008.4721630

Selenium-based amorphous silicon flat-panel digital X-ray imager for protein crystallography

2008· article· en· W2155233413 on OpenAlexafffundvenue
Afrin Sultana, K. S. Karim, J. A. Rowlands

Bibliographic record

VenueCanadian Journal of Electrical and Computer Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicThin-Film Transistor Technologies
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsThin-film transistorDetectorAmorphous siliconX-ray detectorOptoelectronicsBackplaneSensitivity (control systems)Dynamic rangeQuantum efficiencyTransistorMaterials sciencePhysicsDiffractionWide dynamic rangeSiliconAmorphous solidOpticsCrystallographyNanotechnologyChemistryCrystalline siliconComputer scienceComputer hardwareElectronic engineering

Abstract

fetched live from OpenAlex

This work proposes a large-area detector for protein crystallography based on an amorphous silicon (a-Si:H) thin film transistor (TFT) pixel-array backplane and an overlying amorphous selenium (a-Se) photoconductor for direct conversion of incident X-rays into an image charge. To achieve high sensitivity, avalanche multiplication in a-Se is adopted to make the detector sensitive to each incident X-ray. The use of a-Si:H technology enables large-area imaging of protein diffraction patterns at less expense compared to existing charge coupled device (CCD) and imaging plate (IP) detectors. In addition, a theoretical analysis shows that the detector exhibits fast readout speed (readout time <1 s), high dynamic range (~10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> ), high sensitivity (~1 X-ray photon), and high detective quantum efficiency (~0.7), thus validating its suitability for protein crystallography.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.363
Threshold uncertainty score0.830

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.008
GPT teacher head0.147
Teacher spread0.140 · 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 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

Citations1
Published2008
Admission routes3
Has abstractyes

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