SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-3575-4
Halide perovskite memristors, known for their ion mobility, have emerged as strong candidates for computational units in next-generation memory and neuromorphic computing systems. Nevertheless, most memristors are limited to operating in a single mode, either resistive switching or threshold switching. In this work, we overcome this limitation by developing dual-mode α-formamidinium lead triiodide (α-FAPbI3) perovskite memristors with switchable volatile/nonvolatile states, enabled by engineered SnO2 electron transport layers (ETLs). Through molecular interface optimization using 3-(N,N′-dimethylmyristylammonio) propanesulfonate (Z14) and 4,4′-(1,10-phenanthroline-3,8-diyl)bis(N,N′-bis(4-methoxyphen-yl)aniline) (PNL), we achieved exceptional device stability. Volatile devices exhibited >500 switching cycles, while nonvolatile devices surpassed 1000 cycles, both maintaining a high on/off ratio (~10^3). Beyond memory applications, these devices successfully emulated biological functionalities. The volatile mode replicated four key nociceptor characteristics (threshold, relaxation, sensitization, and no adaptation), while the nonvolatile mode demonstrated advanced synaptic plasticity, including paired-pulse facilitation (PPF) and spike-timing-dependent plasticity (STDP). Capitalizing on this dual-mode synergy, we constructed a spiking neural network (SNN) for handwritten digit recognition, achieving a 93% accuracy rate—a significant milestone for perovskite-based neuromorphic systems. This study not only provides a material-level strategy for multifunctional memristor design but also bridges the gap between biological sensing and artificial intelligence, paving the way for adaptive neuromorphic hardware.
Journal of Environmental Engineering Technology•2026•DOI: 10.13205/j.hjgc.202606015
This study evaluated the sustainability and tissue-specific mechanisms of corn stover as a solid-phase carbon source for nitrate removal from groundwater. Cyclic heterotrophic denitrification experiments were conducted using leaf, stem pith, stem bark, stem node, husk, and mixed tissues as carbon sources. Denitrification efficiency, sustainability, dissolved organic carbon (DOC) release, carbon utilization efficiency, intermediate accumulation, and environmental parameters were systematically assessed. Kinetic modeling, correlation analysis, and structural equation modeling (SEM) were applied to elucidate regulatory mechanisms. Results demonstrated that mixed tissues and husk achieved the highest denitrification efficiency, with nitrate removal rates consistently above 98% across four repeated cycles. Total nitrogen removal reached 39.30 mg/g for mixed tissues and 39.95 mg/g for husk, while byproduct concentrations (NO2-N and NH4-N) remained below 2 mg/L. DOC release profiles indicated stable carbon release and high carbon utilization efficiency (203.99 mg TN/g organic carbon for mixed tissues; 182.41 mg/g for husk). Correlation and SEM analyses revealed that carbon source type indirectly governed total nitrogen removal by modulating DOC release, which subsequently influenced pH, electrical conductivity, and nitrogen transformation pathways. Significant differences among tissues were observed in denitrification efficiency, carbon utilization, and micro-environmental regulation. Mixed tissues and husk emerged as superior carbon sources due to their combined efficiency and stability. However, husk released odorous compounds during operation, posing sensory challenges for practical application. The findings support the potential of corn stover tissues as cost-effective carbon sources for in-situ groundwater nitrate remediation, though further optimization is required for field-scale implementation.