Juan Zuo

Balanced optimization of thermal, mechanical, and electrical properties at low filler loading remains challenging for thermally conductive polymer composites. Polyamide‐imide (PAI) was used as the matrix, and aluminum nitride (AlN) particles as the thermally conductive filler. AlN/PAI composite films with 1–15 wt% AlN (below the percolation threshold) were fabricated by solution casting. To further enhance thermal conductivity, silver nanoparticles (AgNPs) were anchored onto the modified AlN surface via in situ reduction to prepare AlN–Ag/PAI composite films. The thermal conductivity of pure PAI (0.093 W/(m·K)) was enhanced by approximately 5.5 times after AlN incorporation. Owing to the local bridging effect of AgNPs, the thermal conductivity of the 15 wt% AlN–Ag/PAI composite film reached 0.605 W/(m·K), 18.2% higher than the corresponding AlN/PAI film. Both AlN and AlN–Ag fillers improved the thermal stability of the composite films and increased the calculated LOI values derived from the TGA char residue. Mechanical properties gradually decreased with rising filler fraction; nevertheless, AgNP decoration partially alleviated the tensile strength deterioration. Electrical characterization revealed that the volume resistivity of the composite films remained above 10 ¹⁴ Ω·cm even at 15 wt% filler loading, indicating satisfactory electrical insulation for electronic packaging.

This study investigates the surface film evolution and contact resistance degradation of AgSnO2In2O3 electrical contacts under 30000 impact cycles using a self-developed in-situ apparatus. Experiments were performed under mechanical loading and 10 mA electrical‑mechanical coupling. Contact resistance was measured by the four-terminal method, while surface morphology, composition, wettability, and film thickness were characterized by SEM/EDS, XPS, contact angle goniometry, and in-situ impact ellipsometry. Under purely mechanical impact, a three‑stage mechanism is revealed: (i) initial damage (0-5000 cycles) with organic film removal and asperity running‑in, reducing worn‑zone resistance by 11.4%; (ii) running-in optimization (5000-10000 cycles) with continued flattening and slow oxide growth; and (iii) oxidative deterioration (15000-30000 cycles) where oxide thickening (to 2.87 nm) and cyclic stress cause worn-zone resistance to exceed the unworn zone. Under 10 mA coupling, deterioration accelerates significantly. Resistance difference between worn and unworn zones increases by 60% compared with purely mechanical conditions. This results from Joule heating‑induced morphological deterioration (Sa from 0.472 to 1.174 μm), intensified oxidation (oxide film to 3.31 nm), formation of a high-resistance contamination layer and Ag loss. These findings clarify differential surface film evolution pathways and offer theoretical guidance for lifetime design and surface treatment of silver-based contacts in low-current, weak-arc applications.

The prediction of optical properties for anodic oxide films from spectroscopic ellipsometry data constitutes a typical inverse problem. Traditional iterative fitting methods suffer from high computational cost and strong dependence on the model structure. This study develops a physics-informed deep learning framework that addresses this challenge through two complementary modeling scenarios. At its core is a Fresnel-constrained neural network, which predicts optical constants (n, k) and ellipsometric parameters [tan(Ψ), cos(Δ)] using the two inputs of base metal composition and anodizing voltage. A hybrid loss function involving the Fresnel equations is employed to guarantee physical consistency with optical reflection rules, thus avoiding non-physical solutions. In addition, two complementary data-optimization strategies were introduced: wavelength selection and model-based data augmentation. The wavelength-selection strategy compresses the spectral input by retaining the most informative 200–350 nm region, while Gaussian-noise-enhanced synthetic samples are generated to expand the training dataset and reduce the risk of surrogate-model bias propagation. The integrated model exhibits good prediction accuracy, with test-set RMSE values of 0.090 for n and 0.038 for k. Notably, the model maintains its training and prediction efficiency even as the volume of data increases. These results show that embedding fundamental optical laws into a deep learning structure yields a robust and efficient framework. Together with targeted data-optimization strategies, this framework offers a promising avenue for the high-throughput inverse design and characterization of complex functional oxide systems.

Pre-intercalation of ions is an effective strategy to enhance the electrochemical performance of tunnel/layered oxides like MnO2. In this study, K+ and NH4+ co-intercalated MnO2 was hydrothermally synthesized using (NH4)2SO4, KMnO4, and MnSO4. The optimal electrode delivered a specific capacitance of 145.42 F·g⁻¹ at 1 A·g⁻¹. When assembled with activated carbon (AC) in a hybrid capacitor, the device achieved an energy density of 60.08 Wh·kg-1 at a power density of 1100 W·kg-1 and retained 93.66% of its capacitance after 3000 cycles at 5 A·g-1. The NH4+ content was found to significantly influence the crystal structure and surface morphology of MnO2. Successful co-intercalation was confirmed by Fourier-transform infrared spectroscopy (FT-IR) and X-ray photoelectron spectroscopy (XPS). This work demonstrated that K+ and NH4+ co-intercalation can effectively improve the structural stability and electrochemical performance of MnO2-based materials for high-performance energy storage.

A novel transition-metal-free alkyne–thiol click polymerization with 100% atom economy is reported. Using ^tBuOLi as a catalyst at 80 °C, the polymerization efficiently yields poly(vinyl sulfide)s (PVSs) with molecular weights up to 11,800 g/mol and yields up to 91%. These sulfur-rich polymers exhibit high thermal stability (T_d up to 293 °C) and high refractive indices (1.8375–1.6383) across the visible range. By integrating abundant sulfur coordination sites with aggregation-induced emission (AIE) properties, the PVS aggregates serve as high-performance fluorescent chemosensors. The sensor enables exclusive, sensitive trace detection of Pd²⁺ and Au³⁺ with remarkable anti-interference capability and pH robustness (pH 1–7). Notably, an ultrafast response (1–2 min) for Pd²⁺ is achieved, with limits of detection (LOD) reaching 7.11 × 10⁻⁷ M for Pd²⁺ and 1.06 × 10⁻⁶ M for Au³⁺, and corresponding limits of quantification (LOQ) reaching 2.37 × 10⁻⁶ M and 3.53 × 10⁻⁶ M, respectively. This methodology offers a sustainable route to heteroatom-rich macromolecules for next-generation optical engineering and environmental monitoring.