ACS Macro Letters最新文献

Polyacrylonitrile (PAN) is a key industrial polymer for the production of carbon fiber for high-strength, lightweight composite material applications, with an estimated 90% of the carbon fiber market relying on PAN-based polymers. Traditionally, PAN synthesis is achieved by conventional radical polymerization, resulting in broad molecular weight distributions and the use of toxic organic solvents or surfactants during the synthesis. Additionally, attempts to improve polymer and processing properties by controlled radical polymerization methods suffer from low monomer conversions and struggle to achieve molecular weights suitable for producing high-performance carbon fiber. In this study, we present an aqueous photoiniferter (aqPI) polymerization of acrylonitrile, achieving high monomer conversion and high PAN molecular weights with significantly faster kinetics and dispersity control when compared to traditional methods. This approach allows for the unprecedented control of polymer properties that are integral for downstream processing for enhanced carbon fiber production.

Cross-linked polyethylenes (PEs) are widely employed, but the permanent links between the chains impede recycling. We show that via imine formation with diamines keto-functionalized polyethylenes from both free-radical (keto-low-density PE, keto-LDPE) and catalytic (keto-high-density PE, keto-HDPE) nonalternating ethylene-CO copolymerization can be cross-linked efficiently in the melt, resulting in gel fractions of the formed cross-linked PEs of up to 85% and improved tensile properties. The imine-based cross-links in the material can be hydrolyzed at 140 °C to recycle up to 97% of the initial thermoplastic keto-polyethylene. Low keto contents of ≤1.5 mol % are found ideal to retain PE-like thermal properties, achieve sufficient cross-link density, and maintain circular recyclability.

Important progress has been made in micromagnetics, driven by its wide-ranging applications in magnetic storage design. Numerical simulation, a cornerstone of micromagnetics research, relies on first-principles rules to compute the dynamic evolution of micromagnetic systems using the renowned Landau–Lifshitz–Gilbert equation, named after Landau, Lifshitz and Gilbert. However, these simulations are often hindered by their slow speeds. Although fast Fourier transformation calculations reduce the computational complexity to O(Nlog(N)), it remains impractical for large-scale simulations. Here we introduce NeuralMAG, a deep learning approach to micromagnetic simulation. Our approach follows the Landau–Lifshitz–Gilbert iterative framework but accelerates computation of demagnetizing fields by employing a U-shaped neural network. This neural network architecture comprises an encoder that extracts aggregated spins at various scales and learns the local interaction at each scale, followed by a decoder that accumulates the local interactions at different scales to approximate the global convolution. This divide-and-accumulate scheme achieves a time complexity of O(N), notably enhancing the speed and feasibility of large-scale simulations. Unlike existing neural methods, NeuralMAG concentrates on the core computation—rather than an end-to-end approximation for a specific task—making it inherently generalizable. To validate the new approach, we trained a single model and evaluated it on two micromagnetics tasks with various sample sizes, shapes and material settings.

Total internal reflection (TIR)-based structural coloration is a brilliant strategy to overcome the need for periodic nanostructures and complex fabrication processes. Light entering the microdome structure undergoes TIR, and owing to varying reflection paths, it exhibits a color that changes with the microdome size. Although solution-based printing techniques have been proposed to achieve this effect, they fall short of full-color realization owing to resolution limitations. Herein, we achieved 3628 dpi of full-color and high-resolution structural color images by printing transparent microdome structures with 1.2-9.9 μm diameter using electrohydrodynamic (EHD) jet printing. Additionally, high-resolution EHD jet-printed structural color images display complex encoded information, enhancing the anticounterfeiting effectiveness through their fabrication simplicity and precise control over the microdome size. Because of these advantages, this TIR-based structural coloration technique with EHD jet printing is highly suitable for anticounterfeiting applications.