Long chain branched isotactic polypropylenes (LCBPP) prepared via the combination of rac-Me 2 -Si(2-Me-4-Ph-Ind)ZrCl 2 /MAO catalyst and a p-(3-butenyl)styrene (T-reagent) were characterized to investigate their synthesis, structure, solution properties, and melt properties. The T-reagent, in the presence of hydrogen, simultaneously served as a comonomer and chain transfer agent, resulting in a LCBPP with high molecular weight, desirable branch point density, and relatively well-defined molecular structure. Additionally, the metallocene catalyst remained highly reactive. To understand the structure-property relationships, a series of LCBPPs were prepared with similar weight-average molecular weights of about 250 000 g/mol and different branch densities ranging from 0 (linear iPP) to 3.3 branch points per 10 000 carbons. 1 H NMR and SEC equipped with triple detectors revealed structural information. Melt properties were examined by small-amplitude dynamic oscillatory shear and extensional flow measurements. LCBPPs of similar molecular weights displayed a systematic increase in zero-shear viscosity and Arrhenius flow activation energy as branch density increased. LCBPPs with high branch point density displayed thermorheologically complex behavior. Strain hardening was observed in extensional flow of LCBPPs.

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A new class of long chain branched isotactic polypropylene (LCBPP) polymers with high molecular weights and well‐controlled structures are prepared via a combination of rac‐Me2Si(2‐Me‐4‐Ph‐Ind)ZrCl2/MAO catalyst and a T‐reagent, such as p‐(3‐butenyl)styrene. The T‐reagent can simultaneously serve two functions (comonomer and chain transfer agent) during metallocene‐mediated propylene polymerization in the presence of a small amount of hydrogen. 1H NMR and SEC equipped with triple detectors revealed most of polymer chain containing LCB structure. LCBPPs displayed a systematic increase in zero‐shear viscosity and Arrhenius flow activation energy as branch density increased. LCBPPs with high branch point density displayed thermorheologically complex behavior, and strain hardening was observed in extensional flow.

Driven by diminishing fossil fuel resources, global warming and subsequently rigid legislation on CO 2 emission, fuel economy is a major challenge for the automotive industry. Each element of the powertrain has been optimized or newly designed to increase efficiency. In this optimization process the engine oils and transmission fluids are important design elements and their contribution to improved efficiency is significant. Polyalkylmethacrylates (PAMAs) are widely used as viscosity index improvers in engine, transmission and hydraulic oils. They have been shown to adsorb from oil solution onto metal surfaces to produce thick and viscous boundary films. These films are maintained even in low speed and high temperature conditions and thus produce a reduction of friction and wear. It was found that specifically designed film-forming PAMAs can improve pitting performance of lubricant formulations. The paper describes the impact of tailor-made functionalized PAMAs on boundary film formation and explores their ability to increase the fatigue life of lubricants.

People regularly share retellings of their personal events through social media websites to elicit feedback about the reasonability of their actions in the event's context. In this paper, we explore how learning approaches can be used toward the goal of classifying reasonability in personal retellings of events shared on social media. We collect 13,748 community-labeled posts from /r/AmITheAsshole, a subreddit in which Reddit users share retellings of personal events which are voted upon by community members. We build and evaluate a total of 21 machine learning models across seven types of models and three distinct feature sets. We find that our best-performing model can predict the reasonability of a post with an F1 score of .76. Our findings suggest that features derived from the post and author metadata were more predictive than simple linguistic features like the post sentiment and types of words used. We conclude with a discussion on the implications of our findings as they relate to sharing retellings of personal events on social media and beyond.