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Record W4409485313 · doi:10.1038/s41598-025-96276-0

Magnetic properties of polymeric acrylic acid hydrogel dosimeter for radiotherapy applications

2025· article· en· W4409485313 on OpenAlexaff
Belal Moftah, Khalid A. Rabaeh, A. Moussa, Md Abdullah Al Kafi, Abdullah S. Bani Issa

Bibliographic record

VenueScientific Reports · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsMcGill University
FundersKing Faisal Specialist Hospital and Research Centre
KeywordsDosimeterAcrylic acidRadiation therapySelf-healing hydrogelsAcrylic resinChemistryRadiochemistryMaterials scienceBiomedical engineeringPolymerNanotechnologyPolymer chemistryMedicineComposite materialNuclear medicineDosimetrySurgeryCopolymer

Abstract

fetched live from OpenAlex

The present study introduces the first magnetic characterization of a hydrogel dosimeter comprising acrylic acid synthesized within a polyvinyl alcohol matrix. The study aims to accurately assess ionizing radiation dose distributions, making it a valuable tool for radiotherapy treatment. The hydrogel was irradiated to a 1-60 Gy dose range using a medical linear accelerator with dose rates of 100-600 MU/min and radiation beam energies of 6, 10, and 15 MV. The developed dosimeter was synthesized by irradiation-triggered polymerization, and the polymerization degree was indirectly quantified by monitoring the positive alterations in the nuclear magnetic resonance spin‒spin relaxation rate. The polymeric hydrogel dosimeter demonstrated an exceptional dose response with an NMR sensitivity of 0.26 Gy⁻¹s⁻¹, which is 20 times more than the sensitivity of the same gel when measured optically in our previous study. Moreover, it exhibited consistent performance regardless of the beam energy or dose rate.

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)0.001
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 score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.009
GPT teacher head0.265
Teacher spread0.256 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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Same venueScientific Reports→Same topicAdvanced Radiotherapy Techniques→French-language works237,207→