Investigation of influence factors in electron beam curing of epoxy resins using a calorimetry technique
Bibliographic record
Abstract
Abstract Because of the complexity of the electron beam (EB) curing process, current understanding of EB curing of polymer resins and composites is limited. This article describes an investigation of different factors affecting EB curing of epoxy resin such as dose rate, time interval between irradiation doses, moisture, and photoinitiator concentration using a calorimetry technique. Results show that higher dose rate resulted in a higher and faster temperature increment in the uncured resin samples, and thus a higher degree of cure. In the multiple‐step EB irradiation, a shorter time interval between irradiation doses resulted in higher temperature in the resin samples and therefore higher degree of cure. Results indicate that moisture could delay crosslinking reaction in the early stages of the cure reaction, but accelerates it later in the curing process. Given a reasonable percentage of photoinitiator, experiments confirmed that samples with higher photoinitiator concentration reach higher degree of cure under same EB irradiation conditions. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2009
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 0.000 |
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.
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".