Mechanical robust and thermal insulated rPET microcellular foam via supercritical CO2 foaming cross-linked photovoltaic backsheet
Bibliographic record
Abstract
Upcycling of solar panels plays important role in the photovoltaic sustainability. However, their reprocessed foaming was limited due to the low melt strength of the backsheet material after long-term usage. Herein, we utilized chain extender (CE), a copolymer made of glycidyl methacrylate (GMA) and styrene to enhance the melt strength of recycled polyethylene terephthalate (rPET) via the formation of chemical cross-linking network. According to Fourier transform infrared spectroscopy (FTIR) spectrum, the epoxy groups in GMA participated into cross-linking reaction since absorption peaks of 758 cm −1 , 847 cm −1 , 902 cm −1 and 1248 cm −1 assigned with epoxy group disappeared after reactive extrusion. Consequently, the glass transition temperature increased to 78.5 ℃, and the crystallinity decreased 37.7 % gradually with CE content of 5 %. Rheological results showed that the zero-shear viscosity dramatically increased to 19692.1 Pa·s, almost 100-folds for unmodified rPET, demonstrating the viscoelasticity transition from “liquid” viscos state to “solid” elastic rPET with incorporation of cross-linking agent. With the assistance of two-step supercritical CO 2 foaming technology, the rPET microcellular foam with high compressive strength and thermal insulated performance can be developed. Specifically, the void fraction increased up to 85.6 %, the average cell size could decrease to 9.2 µm, the cell density increased up to 1.98 × 10 9 cells/cm 3 , thermal conductivity reduced to 48.5 mW/(m·K), the compressive strength of the rPET foam can reach up to 6.84 MPa. As a proof of concept, this work provides a novel route to develop mechanical robust and thermal insulated rPET microcellular foam for resource utilization of disposed photovoltaic backsheet.
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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) | 0.000 | 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".