Hydrophosphinated Styrene–Butadiene Rubber: Improving Automotive Tire Performance
Bibliographic record
Abstract
Styrene–butadiene rubber (SBR) was functionalized using the phosphane–ene reaction, resulting in the installation of phosphines at the alkene functional groups within and pendant to the polymer backbone. Secondary phosphines, including diphenylphosphine (HPPh 2 ), dicyclohexylphosphine (HPCy 2 ), di- iso -butylphosphine (HP i Bu 2 ), and di- tert -butylphosphine (HP t Bu 2 ) were studied in this context, and the progress of these reactions was monitored by 1 H and 31 P{ 1 H} NMR spectroscopy. The most efficient functionalization was achieved when HP i Bu 2 was employed. The inclusion of phosphines influenced the resulting polymer’s thermal properties, decreasing the temperature required for thermal decomposition and raising the T g of the polymers. Large-scale (>200 g) batches of hydrophosphinated styrene–butadiene rubber (PSBR) rubber were produced using HP i Bu 2 and subsequently subjected to vulcanization conditions and testing standard to the automotive tire industry. The results indicated that the vulcanizate produced from PSBR containing 0.5% phosphorus would yield tire treads with improved wet traction and rolling resistance characteristics compared to vulcanizates prepared from the parent SBR. These results defy traditional limitations associated with the “magic triangle” of automotive tire characteristics, whereby gains in performance in one area (e.g., one of traction, rolling resistance, or resistance to degradation) are traditionally accompanied by losses in performance in the others.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".