Cleavable Additives for Deconstructable, Recyclable Polyurethane Thermosets
Bibliographic record
Abstract
Polyurethane (PU) thermosets, particularly those derived from aliphatic components, are challenging to chemically deconstruct due to their permanent cross-linking. Current approaches to impart deconstructability typically rely on complete substitution of network precursors with cleavable analogs, limiting practicality. Cleavable additives (CAs) offer a potentially simple and cost-effective alternative, yet their application has been largely confined to chain-growth networks and remains unexplored in end-linked systems such as PUs. Here, we present a generalizable reverse gel-point theory that predicts the minimum CA loading required for deconstruction of end-linked networks. We validate this framework experimentally through the incorporation of two classes of silyl ether-based CAsbifunctional cleavable strands (BCSs) and trifunctional cleavable junctions (TCJs)into PU thermosets. Both additives enable selective PU dissolution at low loadings (5-12 wt %), with TCJs demonstrating enhanced efficiency. The combined use of BCSs and TCJs also allows fine-tuning of material properties. Furthermore, we show that polyol fragments generated from the deconstruction of TCJ-containing PUs can be chemically repolymerized to regenerate PU materials without loss of mechanical performance over multiple cycles. This work establishes CAs as a viable strategy for advancing PU circularity and offers a foundational framework for their broader application in end-linked polymer networks.
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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 | 0.001 |
| 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".