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Record W2044932915 · doi:10.1109/jsen.2014.2365412

MOS Testbed for the Characterization of Targeted Alpha Therapy Pharmaceuticals

2014· article· en· W2044932915 on OpenAlexafffund
Ryan Griffin, Erin M. McConnell, Maria C. DeRosa, N. G. Tarr

Bibliographic record

VenueIEEE Sensors Journal · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChelationDOTACharacterization (materials science)NanotechnologyAlpha particleMaterials scienceAlpha (finance)ChemistryRadiochemistryCombinatorial chemistryNuclear chemistryMetallurgyPhysics

Abstract

fetched live from OpenAlex

A metal-oxide-semiconductor (MOS) sensor has been designed, fabricated, and tested for use in the characterization of targeted alpha therapy pharmaceuticals. The sensor consists of a 16 × 16 array of 100-μm square alpha particle sensitive cells and has been fabricated in house using a simple MOS process. A subset of those cells is functionalized for the attachment of chelators under investigation for new pharmaceuticals. To demonstrate the utility of this sensor as a characterization platform, cells functionalized with 1,4,7, 10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA)-DNA conjugates were used to chelate americium-241 from solution, and the alpha particle emissions over the surface of the integrated circuit (IC) measured. The IC was able to quantitatively determine the amount of alpha emitter present over each cell, allowing the chelator and chelating chemistry to be assessed. Without any optimization of the chelation chemistry, a 21% increase of emissions was detected on cells functionalized with DOTA.

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 score0.200
Threshold uncertainty score0.221

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 science0.0000.000
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 head0.025
GPT teacher head0.286
Teacher spread0.261 · 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
Published2014
Admission routes2
Has abstractyes

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