Suitability of novel 3D‐printed and coated vessels for water calorimetry
Bibliographic record
Abstract
Abstract Background In water calorimetry, absolute dose to water is determined by measuring radiation‐induced temperature rises. In conventional water calorimeters, temperature detectors are housed in handmade glass vessels that are filled with high‐purity water, thus mitigating radiation‐induced exo/endothermic chemical reactions of impurities that would otherwise introduce additional heat gain/loss, known as heat defect. Being hand‐crafted, these glass vessels may suffer from imperfections, have shape and design constraints, are often backordered, and can be prohibitively expensive. Purpose The purpose of this work is to determine suitability of 3D‐printed plastic vessels that are further coated for use in water calorimetry applications, and to study their stability and characterize their associated heat defect correction factor (. This novel vessel production technique would allow for cost‐effective rapid construction of vessels that can be produced with high accuracy and designs that are simply not practical with current glass vessel construction techniques. This in turn enables water calorimetry applications in many novel radiation delivery modalities, which may include spherical vessels in GammaKnife ICON water calorimetry as an example. Methods Eight vessels were 3D‐printed using Accura ClearVue in an SLA 3D‐printer. Two vessels were coated with Parylene C and four were coated with Parylene N. The water calorimetry preparation procedures followed for these vessels was identical to that of our traditional glass‐vessels (i.e., same cleaning procedures, same high purity water, and same saturation procedures with high purity hydrogen gas). The performance of each vessel was characterized using our in‐house built water calorimeter in an Elekta Versa using both 6 MV flattening filter‐free (FFF) and 18 MV beams. The stability of the coating as function of time and accumulated dose was evaluated through repeated measurements. of each vessel was determined through cross‐comparisons against an Exradin A1SL ionization chamber with direction calibration link to Canada's primary standard laboratory. Results of the two uncoated vessels differed by 2.8% under a 6 MV FFF beam. Vessels coated with Parylenes resulted in a stable and reproducible heat defect for both energies. An overall of 1.001 ± 0.010 and 1.005 ± 0.010 were obtained for Parylene N and Parylene C coated vessels respectively. All Parylene coated vessels showed agreement, within the established uncertainties, to the zero‐heat defect observed in a hydrogen‐saturated glass vessel system. An additional long‐term study (17 days) of a Parylene N vessel showed no change in response with accumulated dose and time. Electron microscopy images of a Parylene N coated vessel showed a uniform intact coating after repeated irradiations. Conclusions An uncoated 3D‐printed vessel is not viable for water calorimetry because it exhibits an unstable vessel‐dependent heat defect. However, applying a Parylene coating stabilizes the heat defect, suggesting that coated 3D‐printed vessels may be suitable for use in water calorimetry. This method facilitates the creation of intricate vessel shapes, which can be efficiently manufactured using 3D printing.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".