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.

Lignin is an attractive raw material for low-cost sustainable carbon fibres, however, the resulting mechanical properties require improvement before they can be implemented in composite applications. The mechanical properties of conventional polyacrylonitrile-derived carbon fibres depend critically on the molecular alignment induced in the polymer fibres by fibre drawing and on retention of the alignment during subsequent thermal treatments. In this study, alignment was induced in high lignin content fibres wet-spun from a low-cost ionic liquid water mixture by employing similar hot-drawing methods. 75/25 wt/wt% lignin–poly(vinyl alcohol) (lignin–PVA) fibres were continuously wet-spun from a 60/40 wt/wt% N,N-dimethylbutylammonium hydrogen sulfate, [DMBA][HSO₄] water mixture, using deionised water used as the coagulant. Hot-drawn fibres with high draw ratios of up to 20 were generated at 180 °C. By careful selection of the initial extrusion diameter and the subsequent draw ratio, the influence of fibre diameter and draw ratio was systematically distinguished. The draw ratio was found to dominate the mechanical properties of the ductile precursor fibres, while the fibre diameter was more significant after stabilisation. The precursor fibres that experienced the highest draw ratios had tensile strengths of 235–249 MPa (up to four times higher than the undrawn lignin–PVA fibres) and tensile modulus of 7.5–8.2 GPa, while the fibre diameter was reduced from 64–106 μm to 15–23 μm. Wide-Angle X-ray Scattering (WAXS) studies showed that hot-drawing induced orientation and crystallisation of PVA at high draw ratios. The crystallisation and orientation of PVA was lost during the slow oxidative stabilisation at 250 °C, associated with a plateau at around 110 MPa tensile strength and 4 GPa tensile modulus for the stabilised lignin–PVA fibres, regardless of draw ratio. Improvements to the stabilisation aimed at retaining alignment are proposed.

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.

Lignin, a complex and abundant biopolymer found in plants, holds immense potential for sustainable materials and chemicals. However, conventional extraction methods often lead to structural deterioration of the native-like aryl ether structure via condensation and other chemical alterations, limiting lignin utility. High delignification in conjunction with preservation of the versatility and functionality of lignin structure for high-value applications can be achieved using advanced mild extraction techniques. In this study, an integrated modeling–experimental approach is used to attain a scalable framework for lignin-first biorefining. Temperature and flow rate were optimized in a flow-through mild ethanosolv system utilizing crude birch-wood chips (without extractive-removal) to balance solvent use, delignification, and structure preservation. Delignification and lignin yield were monitored separately as was its quality in terms of preservation of its native aryl-ether structure, as determined via 2D HSQC NMR and GPC. Extraction kinetics were monitored using UV-Vis spectroscopy to allow for maximizing efficient solvent utilization. Response surface methodology identified optimal conditions (145–151 °C, 8 g_solvent min⁻¹ flow rate), revealing temperature as the primary driver for extraction, exhibiting synergistic effects with the flow rate. Notably, higher flow rates at elevated temperatures (≥140 °C) mitigated β-O-4 linkage degradation without compromising delignification efficiency. Experimental validation of the optimized model at 150 °C and 8 g_solvent min⁻¹ achieved 82 wt% delignification and yielded lignin with high β-O-4 linkage content (59.4 per 100 aromatic units (ArU)), aligning closely with model predictions (81–87 wt%, ≥52 β-O-4 per 100 ArU). Solvent consumption was optimized from the model (13.1 mL g⁻¹, solvent : biomass) and realized a reduction of over 40% of solvent consumption when compared with solvent consumption from typical batch organosolv systems (22.9 mL g⁻¹, solvent : biomass). Finally, the optimization reduced the extraction time significantly from typically 2 hours to 30 min when compared with previous standard extraction conditions (120 °C, 2 g_solvent min⁻¹ flow rate), without compromising on extraction efficiency and lignin quality. This study shows the potential of mild organosolv extraction with alcohol with optimized conditions.