MétaCan
Menu
Back to cohort
Record W2797524815

Sterilization of Medical 3d Printed Plastics: Is H2O2 Vapour Suitable?

2018· article· en· W2797524815 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCMBES Proceedings · 2018
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSterilization (economics)Polylactic acidMaterials sciencePolycarbonateUltimate tensile strengthPolycaprolactoneComposite material3d printedPolymerBiomedical engineering
DOInot available

Abstract

fetched live from OpenAlex

3D printers that precisely fuse plastic filament are enabling the medical device manufacturing sector to produce high-quality plastic medical devices and implants. However, the low-temperature fusing process implies that post-production sterilization must also occur at a low temperature or destroy the precision of the product. This study characterizes the effects of hydrogen peroxide (H 2 O 2 ) vapour sterilization on ASTM-compliant 3D printed tensile samples of polylactic acid (PLA), polycaprolactone (PCL), and polycarbonate (PC). The sterilization process caused physical deformations in PCL. Additionally, increases were observed in PCL and PC sample thickness, and in PC sample width. Decreases in Young’s Modulus (E) were found in all three materials, while UTS decreased in PC, and strain at UTS increased in PCL. The findings demonstrate that the 3D printed materials can be compatible with H 2 O 2 vapour sterilization, but products must be designed to accommodate for changes that occur due to sterilization.

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.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.820
Threshold uncertainty score0.549

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.014
GPT teacher head0.238
Teacher spread0.224 · 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