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This paper develops a machine learning methodology for the rapid and robust prediction of the glass transition temperature (Tg) for polymers for the targeted application of sustainable high-temperature polymers. The machine learning framework combines multiple techniques to develop a feature set encompassing all relative aspects of polymer chemistry, to extract and explain correlations between features and Tg, and to develop and apply a high-throughput predictive model. In this work, we identify aspects of the chemistry that most impact Tg, including a parameter related to rotational degrees of freedom and a backbone index based on a steric hindrance parameter. Building on this scientific understanding, models are developed on different types of data to ensure robustness, and experimental validation is obtained through the testing of new polymer chemistry with remarkable Tg. The ability of our model to predict Tg shows that the relevant information is contained within the topological descriptors, while the requirement of non-linear manifold transformation of the data also shows that the relationships are complex and cannot be captured through traditional regression approaches. Building on the scientific understanding obtained from the correlation analyses, coupled with the model performance, it is shown that the rigidity and interaction dynamics of the polymer structure are key to tuning for achieving targeted performance. This work has implications for future rapid optimization of chemistries 

This work shows the complete, solvent-free conversion of lignin-derivedcis–cis-muconic acid, a platform biochemical, into levulinic acid. 

A platform polyamide, utilizing a bio-based long-chain diacid, demonstrates tunable properties using common alkene chemistries post-polymerization, allowing for a superior plastic designed to tackle challenges with unprecedented flexibility. 

Cracked and deteriorated asphalt are common problems on our roads, leading to safety concerns and requiring significant resources for rehabilitation and reconstruction. This study investigates bio-fog seals, a promising eco-friendly solution utilizing bio-based rejuvenators. These treatments penetrate aged asphalt, restoring its flexibility and resistance to cracking. We assessed the effectiveness of two bio-fog seal formulations—one containing sub-epoxidized soybean oil (SESO) and the other combining SESO with a biopolymer (BioMag). Applied to real pavement sections, the research evaluated how these bio-seals impacted key performance factors, such as stiffness, permeability, and drying time, and safety factors, including skid resistance and pavement marking visibility. The results indicate the bio-seals did not compromise skid resistance and the reflectivity of the markings, eliminating the need for repainting stripes. Additionally, they successfully reduced pavement stiffness, making the asphalt more flexible and crack-resistant. Remarkably, with rapid setting times, under 30 min, these treatments minimize traffic disruption and do not require a blotter material. Overall, this research demonstrates the potential of bio-fog seals as a sustainable solution for extending pavement lifespan and lowering long-term maintenance costs. 

We report the tethering of flame-retardant additives like 9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide (DOPO) to the backbone of a polyamide throughtrans-3-hexenedioic acid, a bioadvantaged derivative of muconic acid.