Nanomaterials

The electrical performance of polymer nanocomposites strongly depends on the morphology of nanofillers and the structure of the resulting conductive networks. To elucidate the mechanisms governing conductive network formation in multi-morphology nanofiller systems, a ternary coarse-grained model composed of rod-, Y-, and X-shaped nanofillers is constructed. The effects of nanofiller volume fraction (VF) and nanofiller composition ratios on percolation behavior are systematically investigated. By incorporating an efficient cKDTree-based neighbor search method, conductive networks are identified and their topological characteristics are quantified with high computational efficiency. The results demonstrate that nanofiller morphology ratios play a crucial role in controlling local structural evolution and the percolation threshold. Statistical analyses of the main cluster size (MCs) and the number of clusters (Nc) further reveal the synergistic and competitive effects among different filler morphologies. The combination of filler morphologies is shown to be a key factor in determining the percolation threshold and network topology. The multi-morphology simulation framework together with structural characterization approach proposed in this work provide theoretical guidance for the rational design of high-performance conductive polymer nanocomposites.

A $5\mathrm{n}\mathrm{m}$ thick polycrystalline $N{i}_{81}F{e}_{19}$ film was sputter-deposited onto a circular 3-inch diameter, $390\mathsf{\mu }\mathrm{m}$ thick single-crystal wafer with $Si{O}_2$ surface layers. The magnetoresistance (MR) of the sample was analyzed as a function of applied DC magnetic field and temperature using the Van der Pauw technique. Magnetic measurements were carried out over a temperature range of 25 °C to 350 °C using a Lake Shore Hall Effect Measurement System (HEMS). An external magnetic field ranging from to was applied at each temperature value to observe changes in resistance. Hall coefficients and resistance were obtained by applying current in both directions with different contact configurations. Machine learning techniques, including Random Forest Regression, were employed to predict magnetoresistivity beyond 350 °C; the best-performing model achieved $R^2$ values up to $0.9449$ with MSE as low as $0.0071$, and enabled Curie temperature estimation with . This study highlights the potential of machine learning in accurately forecasting material properties beyond experimental limits, providing enhanced predictive models for the magnetoresistive behavior and critical temperature transitions of $N{i}_{81}F{e}_{19}$ .

The selective hydrogenation of phenol to cyclohexanone is a pivotal reaction for producing nylon precursors. Conventional Pd/C catalysts, however, suffer from weak metal–support interactions, leading to size heterogeneity and agglomeration of Pd nanoparticles, which degrades their activity and stability. Herein, we report a facile argon plasma treatment to engineer rich defects on an activated carbon (AC) support, resulting in a highly dispersed and stable catalyst (denoted as PL-Pd@AC_Ar). Characterization results indicate that the abundant carbon defects in PL-Pd@AC_Ar enhance the anchoring of Pd precursors, ensure the uniform dispersion of Pd nanoparticles, and effectively modulate their electronic structure. Consequently, the plasma-enabled PL-Pd@AC_Ar catalyst achieves 99.9% phenol conversion with 97% selectivity to cyclohexanone at a mild temperature of 70 °C and maintains exceptional stability over six consecutive cycles. This work provides a robust and efficient strategy for the surface engineering of carbon supports to design high-performance hydrogenation catalysts.