Photochemical post-functionalization of polystyrene enables accelerated chemical recycling
Bibliographic record
Abstract
The development of molecular post-modification design strategies that enable low-temperature pyrolysis of polystyrene (PS) remains an underexplored area. The current state of the art for the pyrolysis of PS demands heating above 400°C which creates economic barriers to commercial-scale monomer recovery. Here, we demonstrate the post-functionalization of the PS backbone with a labile C-S bond, specifically a trifluoromethylthio group (SCF3), to accelerate the depolymerization of PS at lower temperatures. We first adapted the trifluoromethylthiolation reaction to PS which involved solvent screening and reaction optimization. We observed a significant increase in the depolymerization of PS-SCF3 compared to PS across a wide range of molecular weights and consumer-grade products at 300°C. Using the Flynn-Ozawa-Wall analysis, the average apparent activation energy for the depolymerization of PS-SCF3 is ~11kJ/mol lower than pristine PS. To benchmark this protocol, we found that the pyrolysis of several consumer-grade PS-SCF3 samples at 300°C offered greater amounts of styrene compared to pristine consumer-grade PS. This study explores the possibilities of post-functionalizing the backbone of PS to depolymerize PS at faster timescales and lower temperatures to afford greater recovery of styrene, contributing towards a circular economy of plastics.
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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) | 0.002 | 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".