Faraday Discussions最新文献

This study reports the first systematic study investigating the potential of using hyperbranched (HB) polymers as novel materials for improving two photon polymerisation (2PP) processing. It demonstrated that HB polymer containing additive manufacturing resins can be successfully formulated and used to print: (a) mono-/multi-material structures, the latter containing monomers of different functionality (i.e., hydrophilic/hydrophobic mixes), (b) with a broader range of printing conditions, (c) to high levels of cure and (d) at faster processing speeds than monomeric resins. A printed multi-material structure was confirmed to contain both feed materials and exhibit high cure by ToF-SIMS and Raman analysis, respectively. Thus, HB polymers were shown to improve mixing in multi-functional resins and overcome 2PP chemistry restrictions. When processing with selected HB polymers, both the polymerisation “onset” and “burning” thresholds were improved compared to monomeric resins. Processing more reactive HB polymers still increased the overall processing window compared to the 2PP processing of the equivalent monomer, but the “burning” threshold was in fact lowered, which was linked to depolymerisation events. Thus, a HB polymer was subject to degradation studies and shown to produced more residual material (i.e. “char”) than linear materials, which delivers the decolourisation in 2PP “burning”. This study confirms that using HB polymers can extend the viability and utility of 2PP processing, improvements that were delivered by understanding the reactivity of these pre-polymers toward both polymerisation and depolymerisation.

Creating and curating new data to augment heuristics is a forthcoming approach to materials science in the future. Highly improved properties are advantageous even with “commodity polymers” that do not need to undergo new synthesis, high-temperature processes, or extensive reformulation. With artificial intelligence and machine learning (AI/ML), optimizing synthesis and manufacturing methods will enable higher throughput and innovative directed experiments. Simulation and modeling to create digital twins with statistical and logic-derived design, such as the design of experiments (DOE), will be superior to trial-and-error approaches when working with polymer materials. This paper describes and demonstrates protocols for understanding hierarchical approaches in optimizing the polymerization and copolymerization process via AI/ML to target specific properties, using model monomers such as styrene and acrylate. The key is self-driving continuous flow chemistry reactors with sensors (instruments) and real-time ML with an online monitoring set-up that allows a feedback loop mechanism. We provide initial results using ML refinement of the classical Mayo–Lewis equation (MLE), time-series data, and an autonomous flow reactor system build-up as a future data-generating station. More importantly, it lays the ground for precision control of the copolymerization process. In the future, it should be possible to undertake collaborative human–AI-guided protocols for the autonomous fabrication of new polymers guided by literature and available data sources targeting new properties.

Plastics are ubiquitous in modern society; however, their disposal at end-of-life remains challenging. Enzymatic recycling offers a potential low-energy solution to recycling poly(ethylene terephthalate) (PET); however, high-crystallinity substrates such as polyester textiles are recalcitrant to enzymatic hydrolysis. Current amorphisation pretreatments yield substrates amenable to enzymatic digestion; however, they account for a significant percentage of all process electricity requirements. Here we investigate dissolution–reprecipitation with the green solvents gamma-valerolactone and 2-isopropylphenol as a lower-energy pretreatment regime. We find that whilst there is only a minimal decrease in substrate crystallinity, activity of the benchmark PET hydrolase LCC^ICCG is increased on all solvent-treated substrates. We show that GVL negatively impacts the thermostability of LCC^ICCG, and both solvents dramatically decrease enzyme activity, from concentrations as low as 4%, highlighting the need for effective solvent removal following pretreatment. Finally, we show that IPP and GVL are effective for the removal of synthetic dyes from polyester textiles, enabling new applications for these solvents in PET recycling.