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Record W2170999871

A ZnO quantum dot radiation dosimeter for high energy radiation measurements

2009· article· en· W2170999871 on OpenAlexaff
Joyce Xinya Gao, John T. W. Yeow, Rob Barnett

Bibliographic record

VenueInternational Conference on Nanotechnology · 2009
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsGrand River HospitalUniversity of Waterloo
Fundersnot available
KeywordsDosimeterDosimetryIonizing radiationRadiationMaterials scienceOptoelectronicsPhotonQuantum dotIrradiationOpticsMedical physicsPhysicsNuclear medicineNuclear physicsMedicine
DOInot available

Abstract

fetched live from OpenAlex

High energy radiation is extensively used in various medical and industrial applications. An accurate radiation dosimetry is crucial for both radiation treatments as well as the protection of personnel across a wide range of industries, such as medicine, radiation research, space, and nuclear power plants. The unique electrical and optical properties of quantum dot (QD), such as high radiation sensitivity and good radiation resistivity make it a superior sensing material for radiation dosimeters. This paper reports the design, fabrication, and characterization of a radiation dosimeter based on ZnO QDs under 6MV photon ionization beam. To the best of authors' knowledge, this is the first time such QD based dosimetry devices have been made and characterized under MV radiation range. This ZnO QD based radiation dosimeter exhibits a quasi-linear response to photon irradiation dose rates and a very linear response to total doses. Besides, an outstanding repeatability with standard deviation of less than 0.32% was observed for the ZnO QD radiation dosimeter when exposed to cyclic high energy ionization radiation.

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 score0.395
Threshold uncertainty score0.733

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.060
GPT teacher head0.284
Teacher spread0.224 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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