Sci‐Sat AM: Radiation Dosimetry and Practical Therapy Solutions ‐ 01: Optimization of an organic field effect transistor for radiation dosimetry measurements
Bibliographic record
Abstract
Purpose: To use Monte Carlo simulations to optimize the design of an organic field effect transistor (OFET) to maximize water‐equivalence across the diagnostic and therapeutic photon energy ranges. Methods: DOSXYZnrc was used to simulate transport of mono‐energetic photon beams through OFETs. Dose was scored in the dielectric region of devices and used for evaluating the response of the device relative to water. Two designs were considered: 1. a bottom‐gate device on a substrate of polyethylene terephthalate (PET) with an aluminum gate, a dielectric layer of either PMMA or CYTOP (a fluorocarbon) and an organic semiconductor (pentacene). 2. a symmetric bilayer design was employed in which two polymer layers (PET and CYTOP) were deposited both below the gate and above the semiconductor to improve water‐equivalence and reduce directional dependence. The relative thickness of the layers was optimized to maximize water‐equivalence. Results: Without the bilayer, water‐equivalence was diminished relative to OFETs with the symmetric bilayer at low photon energies (below 80 keV). The bilayer's composition was designed to have one layer with an effective atomic number larger than that of water and the other with an effective atomic number lower than that of water. For the particular materials used in this study, a PET layer 0.1mm thick coupled with a CYTOP layer of 900 nm provided a device with a water‐equivalence within 3% between 20 keV and 5 MeV. Conclusions: organic electronic devices hold tremendous potential as water‐equivalent dosimeters that could be used in a wide range of applications without recalibration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".