Production and Dynamic Mechanical Analysis of Macro-Scale Functionalized Polydicyclopentadiene Objects Facilitated by Rational Synthesis and Reaction Injection Molding
Bibliographic record
Abstract
Polydicyclopentadiene (PDCPD) is a ring-opening metathesis polymer derived from dicyclopentadiene. Valued for its light weight, excellent material strength, and good performance at both high and low temperatures, PDCPD is used to make body panels for tractors and heavy-duty trucks. We recently described the first functionalized form of PDCPD ( f PDCPD) that maintains the thermal stability of the parent polymer. However, while commercial PDCPD components are produced through a reaction injection molding process on a very large scale, our f PDCPD polymer was developed on a small scale and has not been shown to be a viable substrate for reaction injection molding processes. Here we address these limitations by providing an improved synthesis of the f DCPD monomer mixture on a half-kilo scale and describing a method for separating the polymerizable monomer from other nonpolymerizable regioisomers without chromatography. We further demonstrate a reaction injection molding process for the creation of prototype f PDCPD components. Together, these increases in scale and advances in small object manufacturing facilitate the production of regular-dimensioned samples for dynamic mechanical analysis, permitting the first direct comparison of the mechanical properties of C -linked ester-functionalized PDCPD with those of unmodified PDCPD. Additionally, by using monomers of different regioisomer purity, products are achieved encompassing a broad range of glass transition temperatures and storage/loss moduli.
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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".