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Prof. ZHANG Weifeng

Anhui Agricultural University, College of Resources and Environment

Research Publications & English Decoded Briefs

Showing 2 publications
Environmental Chemistry2026DOI: 10.7524/j.issn.0254-6108.2025012302

Effects of Different Novel Fertilizer Applications on Phosphorus Loss from Surface and Seepage Water in Paddy Fields in the Chaohu Lake Watershed

Phosphorus (P) loss from paddy fields contributes to eutrophication in Chaohu Lake. This study evaluated the effects of novel fertilizers and P reduction on P loss and rice yield. Seven treatments were established: no P (CK), rice-specific fertilizer (ZYF), slow-release blended fertilizer (SRF), Xinjutian compound fertilizer (XJT), enhanced loss-controlled fertilizer (CRF), CRF with 10% P reduction (CRF-10P%), and CRF with 30% P reduction (CRF-30P%). Results showed that novel fertilizers and P reduction significantly reduced concentrations of total phosphorus (TP), dissolved phosphorus (DP), and particulate phosphorus (PP) in surface water and leachate. The first 5 days after basal fertilization and heavy rainfall were high-risk periods for P loss. Rainfall increased TP concentrations by 417.74%–432.86% and 94.85%–351.35% in surface water and leachate, respectively; DP increased by 120.80%–322.44%, and PP by 280.66%–501.77% and 80.23%–297.55%. Compared with ZYF, SRF, XJT, and CRF reduced TP loss by 15.43%–33.95%, with SRF showing the lowest loss. Under P reduction, CRF-10P% and CRF-30P% reduced TP loss by 31.48% and 37.04%, respectively, with CRF-30P% achieving the lowest loss. Notably, CRF-10P% increased rice yield by 22.37% relative to ZYF, indicating that moderate P reduction with enhanced loss-controlled fertilizer can maintain or increase yield while reducing environmental risk. The study concludes that CRF-10P% offers a promising strategy for sustainable rice production in the Chaohu Lake watershed.

SCIENCE CHINA Materials2026DOI: 10.1007/s40843-025-3817-3

Ultra-robust Y-doped hafnium oxide ferroelectric memristors for intelligent edge computing

The rapid development of artificial intelligence (AI) and big data-driven edge intelligence applications has created an urgent demand for highly efficient computing hardware. Ferroelectric memristors have emerged as promising candidates for edge hardware due to their multi-level conductance tunability and high integration potential. In this work, we fabricated yttrium-doped hafnium oxide (YHO) memristors with a remanent polarization of ~30 μC/cm2, a multi-level resistive state retention time of approximately 10^5 s, and an endurance of up to 10^9 cycles. Based on this device, we constructed a real-time path-tracking system for intelligent vehicles—which achieves 100% path recognition accuracy—and a traffic sign denoising network optimized for hardware mapping via a hierarchical mixed-precision quantization strategy; this network yields denoised images with a peak signal-to-noise ratio (PSNR) of 27.04 and a structural similarity index measure (SSIM) of 0.80. This work paves an innovative pathway for the practical application of hafnium-based ferroelectric memristors, accelerating the development of highly efficient hardware for edge intelligence.

Prof. ZHANG Weifeng | Publications & Academic Profile | SinoGreenTech | SinoGreenTech