Design, Exploitation, and Rational Improvements of Diazirine-Based Universal Polymer Crosslinkers
Bibliographic record
Abstract
Conspectus Addition of new covalent bonds between the chains of thermoplastic polymers (i.e., crosslinking) provides improved mechanical strength and enhanced high-temperature performance while also providing an effective strategy for photopatterning. Traditionally, however, crosslinking of each polymer substrate has required the use of a specific crosslinking technology (hydrosilylation for PDMS, vulcanization for rubber, etc.). The lack of a general solution to the challenge of polymer crosslinking means that there are many thermoplastics (e.g., polypropylene or polyhydroxyalkanoates) that have desirable properties, but which cannot be upgraded by traditional crosslinking technologies. Our lab developed the first universal crosslinkers for aliphatic polymers by leveraging trifluoromethyl aryl diazirine motifs, functional groups that have been widely used in chemical biology for >30 years, but which have seldom been exploited in materials science. These novel reagents work (via C–H insertion) on essentially any commodity polymer that contains aliphatic C–H bonds, including industrial plastics like polypropylene (the crosslinking of which has been an outstanding challenge in the field for >50 years), as well as commercially important elastomers (e.g., polydimethylsiloxane), biodegradable polymers (e.g., polycaprolactone), and green polymer materials derived from biomass (e.g., polyhydroxyalkanoates). Subsequent structure–function work from our group led to crosslinkers that were >10-fold more effective in undergoing C–H insertion with aliphatic substrates. We then developed an improved synthesis of our electronically optimized diazirines and incorporated them into a family of cleavable crosslinker reagents, which permit the on-demand generation of reprocessable thermosets. At the same time, other groups replaced the perfluoropropyl linker in our first-generation crosslinker with a series of dynamic linkages; these permit the ready generation of vitrimeric materials and can be used in the reactive compatibilization of immiscible plastic waste. Since the publication of our initial Science paper in 2019, this burgeoning field of diazirine-based polymer crosslinkers has experienced an explosion of interest. Publications from our lab and others have described the use of these reagents in covalent adhesion, photopatterning of low dielectric materials for microelectronics, and direct optical printing of quantum dots. Our crosslinkers have also been shown to heighten the robustness of ice-phobic coatings and improve the performance of woven ballistic fabric, while─perhaps most unexpectedly─substantially improving the stability of high-performance perovskite solar cells. Electronically optimized diazirines can also be used to covalently link proteins to polymer surfaces, suggesting a broad range of applications in the biocompatibilization of medical devices. This Account will summarize the development of trifluoromethyl aryl diazirine reagents for materials science over the past 5 years. A brief comparison will also be made, in the Summary and Outlook section at the end of the Account, to competing (and often complementary) reagents based upon azide and diazoalkyl motifs. Finally, we have compiled a Frequently Asked Questions list that covers many practical aspects of crosslinker design and application; this is appended as Supporting Information.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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".