Suitability of novel 3D‐printed and coated vessels for water calorimetry
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».