Faraday Discussions最新文献

High-entropy oxides (HEOs), as a subclass of high-entropy materials (HEMs), offer a versatile platform for catalysis by leveraging entropy-stabilized solid solutions with tunable compositions, lattice structures, and electronic properties. While exsolution–dissolution of metal species in crystalline HEOs has emerged as a promising strategy for reversible active sites regeneration, the dynamic behaviour of HEOs possessing amorphous nature remains under-explored, particularly the difference with crystalline counterparts. In this work, we systematically investigate the architecture-dependent exsolution–dissolution behavior of HEOs by comparing a crystalline-phase HEO (c-HEO) and an amorphous-phase HEO (a-HEO), both comprising Ni, Mg, Cu, Zn, and Co as principal metal elements. Using a combination of in situ variable-temperature X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), electron microscopy, and in situ CO diffuse reflectance infrared Fourier transform spectroscopy (CO-DRIFTS), the structural evolution of the two HEO phases under redox conditions was elucidated. Both materials exhibit reversible exsolution of metallic species or alloys in reducing environments, followed by re-incorporation into the host lattice upon oxidation. Remarkably, the a-HEO demonstrates more facile and dynamic self-healing behavior, with alloy exsolution and dissolution occurring under milder conditions because of its enhanced reducibility and structural disorder. This study provides critical insights into the design of next-generation regenerable catalysts based on amorphous HEOs, highlighting the role of phase structure in governing reversible metal-site formation dynamics and catalytic performance.

Multielemental alloys (MEAs) based on Earth-abundant 3d transition metals hold significant promise as low-cost electrocatalysts for the oxygen evolution reaction (OER), but their long-term stability under oxidative conditions remains a major challenge. In this study, we investigate the effect of palladium incorporation on the electrochemical performance and structural durability of FeCoNiCu MEA nanoparticles. Building upon our previous findings that trace Pd addition significantly enhances catalyst durability, an accelerated durability test (ADT) performed at 100 mA cm⁻² reveals that the degradation rate (0.356 mV h⁻¹) decreased dramatically to approximately 1/350th that of Pd-free FeCoNiCu (125 mV h⁻¹). In this study, we systematically synthesized a series of Pd–FeCoNiCu alloys with Pd contents ranging from 0.177 to 1.97 at%. Advanced characterization techniques including inductively coupled plasma optical emission spectroscopy (ICP-OES), electron microscopy, synchrotron-based spectroscopy, and electrochemical measurements, were employed to elucidate the correlation between composition, structure, and performance. Our findings reveal a highly non-linear dependence of catalyst performance on Pd content: an optimal range (0.336–0.389 at%) enables long-range d–d/sp orbital hybridization that delocalizes the local density of states (LDOS) of surrounding 3d metals, thereby suppressing oxidative dissolution. In contrast, higher Pd concentrations lead to Pd–Pd interactions, localize electronic perturbation, and accelerate degradation. This volcano-type correlation between Pd content and durability, highlights a general strategy for engineering catalyst longevity via minimal noble-metal doping and spatially cooperative electronic modulation.

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.