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XW
Verified CAS / Academic Author2 Decoded Studies

Prof. Xinglong Wang

Sun Yat-sen University

Research Publications & English Decoded Briefs

Showing 2 publications
SCIENCE CHINA Materials2026DOI: 10.1007/s40843-025-3798-x

Lesion-targeted probiotic delivery via ROS-activated gelation and mucoadhesion for transanal treatment of colitis

Ulcerative colitis (UC) is a chronic inflammatory disorder of the colorectal mucosa, where conventional enema therapies suffer from poor retention and limited inflammation modulation. Here, we report a highly fluid probiotic-containing enema solution (s-BSA-Fe+EcN) integrating bovine serum albumin (BSA), Fe2+, and probiotic Escherichia coli Nissle 1917 (EcN). The solution's high fluidity enables comprehensive coverage of irregular colorectal mucosa. Upon encountering reactive oxygen species (ROS)-rich inflamed lesions, Fe2+ mediates H2O2 scavenging and hydroxyl radical generation, triggering BSA crosslinking and in situ gelation into a conformal hydrogel (h-BSA-Fe+EcN). This targeted adhesion mitigates oxidative damage to host tissues and preserves probiotic viability. In a porcine model, endoscopic imaging confirmed inflammation-targeted gelation in vivo. In a dextran sulfate sodium-induced mouse colitis model, h-BSA-Fe+EcN demonstrated excellent therapeutic efficacy, reducing disease activity index and restoring colonic architecture. This strategy addresses the dual challenges of fluid perfusion and rapid ROS-responsive gelation, offering an advanced transanal treatment for UC.

SCIENCE CHINA Materials2026DOI: 10.1007/s40843-025-3874-1

Thriving In-Memory Computing and Neuromorphic Applications of Ferroelectric-Based Devices

The rapid expansion of artificial intelligence (AI) model sizes to trillions of parameters has intensified the demand for computational paradigms that overcome the von Neumann bottleneck. Emerging memory technologies, while advancing, fall short of meeting the massive requirements of large-scale models. Ferroelectric materials, with their continuous tunability of domain patterns, offer a promising route to emulate synaptic weights in biological learning. This review systematically examines four fundamental ferroelectric-based device architectures: ferroelectric capacitors, ferroelectric field-effect transistors (FeFETs), ferroelectric tunnel junctions (FTJs), and ferroelectric domain wall memories. We analyze their latest progress, application domains, and inherent advantages, while critically assessing the challenges impeding their commercialization. Key issues include scalability, endurance, retention, and integration with CMOS technology. We also highlight optimization strategies for material and device performance, array-level design, and neuromorphic computing architectures. Future research directions are proposed, emphasizing the expansion of novel applications and the realization of energy-efficient, high-density in-memory computing systems. This review provides a comprehensive framework for researchers and engineers aiming to harness ferroelectric devices for next-generation computing.