SinoTechIntel Academic Portal
WH
Verified CAS / Academic Author2 Decoded Studies

Prof. WANG Hailong

School of Materials Science and Engineering, Zhengzhou University

Research Publications & English Decoded Briefs

Showing 2 publications
Journal of Advanced Ceramics2026DOI: 10.26599/JAC.2026.9221349

Phase Evolution and Broadband Electromagnetic Wave Absorption Mechanisms of Electrospun Polymer-Derived SiC-Based Fibrous Ceramic Membranes

Polymer-derived SiC-based ceramic fibrous membranes are lightweight, thermally stable electromagnetic wave absorbers, but simultaneously achieving strong attenuation and good impedance matching remains difficult due to limited control over phase composition and dielectric behavior. This work prepares multiphase SiC-based fibrous membranes by electrospinning combined with polycarbosilane (PCS)-derived ceramic conversion. Phase evolution, fiber morphology, dielectric response, and electromagnetic wave absorption are regulated by tuning PCS content and pyrolysis temperature. Advanced characterization confirms a heterogeneous β-SiC/SiOxCy/carbon multiphase structure with good flexibility, providing abundant polarization centers, moderate conductive pathways, and multiple reflection sites, thereby balancing impedance matching and dielectric loss. The sample with 1.4 g PCS pyrolyzed at 1400 °C achieves a minimum reflection loss (RLmin) of −27.12 dB at 2.2 mm and a maximum effective absorption bandwidth (EAB) of 8.22 GHz at 2.7 mm, covering 9.78–18 GHz. Radar cross-section simulation verifies electromagnetic scattering suppression of the optimized fibrous ceramic coating. This study provides a strategy for tailoring phase composition and dielectric behavior in polymer-derived SiC-based fibrous membranes for broadband electromagnetic wave absorption.

Nano-Micro Letters2026DOI: 10.1007/s40820-025-01999-4

Flexible Sensors for Battery Health Monitoring

With the widespread application of lithium batteries in electric vehicles and energy storage systems, battery-related safety and reliability issues have become increasingly prominent. Conventional monitoring methods often struggle to address dynamic changes under complex operando. In recent years, flexible sensing technology has emerged as a promising solution for battery health monitoring due to its high adaptability and conformability to complex structures. Meanwhile, empowered by artificial intelligence (AI) for data analysis, the collected data enables efficient and accurate state assessment, offering robust support for accident prevention. Against this background, this paper first explores the integrated applications of flexible sensors in battery health monitoring and their unique advantages in addressing complex battery operating conditions, while analyzing the potential of AI in battery state analysis. Subsequently, it systematically reviews mainstream flexible sensing technologies (e.g., film sensors, thermocouples, and optical fiber sensors), elucidating their mechanisms for revealing intricate internal battery processes during operation. Finally, the paper discusses AI’s role in enhancing monitoring efficiency and accuracy, and envisions future research directions and application prospects. This work aims to provide technical references for the battery health monitoring field as well as promote the application of flexible sensing technologies in improving battery system safety and reliability.