MétaCan
Menu
Back to cohort

Design and evaluation of quantum dot sensors for making superficial x-ray energy radiation measurements

2010· article· en· W2129732936 on OpenAlexafffund
Xiang Gao, Qingsheng Kang, John T. W. Yeow, Rebecca Barnett

Bibliographic record

VenueNanotechnology · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvanced Semiconductor Detectors and Materials
Canadian institutionsGrand River HospitalUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceQuantum dotOptoelectronicsRadiationIrradiationPhotonFabricationRepeatabilityPhoton energyRadiant energyElectrodeOpticsPhysics

Abstract

fetched live from OpenAlex

The extraordinary physical properties of quantum dot (QD) materials such as high radiation sensitivity and good radiation resistivity indicate their potential for use in the fabrication of radiation sensors. This paper reports the design and fabrication of two kinds of radiation sensors based on ZnO and CdTe QDs. Both sensors are characterized using a Gulmay Medical D3000 DXR unit for superficial x-ray irradiation with source photon energies that range from 36.9 to 64.9 keV. The QD radiation sensors exhibit excellent linearity with respect to different photon energy doses, radiation source to device surface distances, and field sizes. The effects of the electrode separation and the area density of the QD layer are also investigated. All sensors characterized show an outstanding repeatability under photon irradiation, with a signal variation less than 1%.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.047
GPT teacher head0.281
Teacher spread0.234 · 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

Citations21
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueNanotechnologySame topicAdvanced Semiconductor Detectors and MaterialsFrench-language works237,207