Electron beam modification of space durable polymeric nano-adhesive bonding of ultra-high temperature resistant polymer
Bibliographic record
Abstract
Abstract This investigation highlights fabrication of ultra-high temperature resistant polymers such as polybenzimidazole (PBI) by high-performance nano-adhesive. High-performance nano-adhesive is prepared by dispersing carbon nano-fibers into ultra-high temperature resistant epoxy adhesive. Prior to fabrication of PBI, the surface of PBI is ultrasonically cleaned by acetone and then modified by atmospheric pressure plasma with 30, 60 and 90 s of exposure and low-pressure plasma with 30, 60, 120, 240 and 480 s of exposure. Surface characterization of the unmodified and modified PBI sheets is carried out by contact angle measurements and surface energy of the polymer is estimated. It is observed that the polar component of surface energy leading to total surface energy of the polymer increases significantly when exposed to atmospheric pressure plasma. Tensile lap shear strength of adhesive bonded PBI reveals that atmospheric pressure plasma is more useful than low-pressure plasma in terms of adhesive bond strength of PBI and increases further when fabricated by nano-carbon fibers dispersed epoxy adhesive. The nano-adhesive bonded PBI sheets are post-cured by electron beam radiation under the SLOWPOKE-2 nuclear reactor. Post curing under electron beam radiation further increases the adhesive bond strength considerably.
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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) | 0.001 | 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".