Recycled PET foam core sandwich panels with reinforced hybrid composite facesheets: A sustainable approach for enhanced impact resistance
Bibliographic record
Abstract
This research explores the Low-Velocity Impact behavior of thermoplastic composite sandwich panels with 100% recycled Polyethylene Terephthalate (PET) foam sourced from post-consumer plastic water bottles. Being recognized as a reliable technique, hybridization using stainless-steel mesh layers was employed to reinforce the panels’ composite facesheets of sandwich panels accessible for modular housing, cold storage rooms and cargo trucks. Adequate impregnation of the reinforcement metallic mesh layer alongside proper skin-to-core adhesion was accomplished by optimizing a two-phase compression molding method. The effect of hybridization on impact response of sandwich panels with two different PET foam core thicknesses, and stacking sequence were evaluated. It was revealed that reinforcing the impacted surface of the composite sandwich panels significantly increased the perforation threshold. Moreover, analyzing the post-impact section view of the samples indicated that hybridization modified the damage propagation response of the PET foam core sandwich composites, through which the energy absorption capacity was improved.
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.
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".