Stochastic neuro-fuzzy system implemented in memristor crossbar arrays.

Neuro-symbolic artificial intelligence has garnered considerable attention amid increasing industry demands for high-performance neural networks that are interpretable and adaptable to previously unknown problem domains with minimal reconfiguration. However, implementing neuro-symbolic hardware is challenging due to the complexity in symbolic knowledge representation and calculation. We experimentally demonstrated a memristor-based neuro-fuzzy hardware based on TiN/TaOx/HfOx/TiN chips that is superior to its silicon-based counterpart in terms of throughput and energy efficiency by using array topological structure for knowledge representation and physical laws for computing. Intrinsic memristor variability is fully exploited to increase robustness in knowledge representation. A hybrid in situ training strategy is proposed for error minimizing in training. The hardware adapts easier to a previously unknown environment, achieving ~6.6 times faster convergence and ~6 times lower error than deep learning. The hardware energy efficiency is over two orders of magnitude greater than field-programmable gate arrays. This research greatly extends the capability of memristor-based neuromorphic computing systems in artificial intelligence.

Facile construction of a stable core-shell spherically magnetic polyimide covalent organic framework for efficient extraction of phenylurea herbicides.

Efficient enrichment is crucial for the highly sensitive monitoring of phenylurea herbicides (PUHs) in various environmental waters. In this work, a stable core-shell spherically magnetic polyimide covalent organic framework (COF) was synthesized via a simple template-mediated precipitation polymerization method under mild conditions using tri(4-aminophenyl)amine (TAPA) and 3,3',4,4'-biphenyltetracarboxylic dianhydride (BPDA) as the building units (denoted as Fe3O4@TAPA-BPDA). The Fe3O4@TAPA-BPDA exhibits remarkable adsorption performance for PUHs with an optimized adsorption time of only 10 min. The adsorption of PUHs by Fe3O4@TAPA-BPDA followed the pseudo-second-order kinetic model and the Langmuir model. Furthermore, hydrogen bonding, halogen bonding, hydrophobic interaction, electro donor-acceptor interaction and π-π interactions are identified as the dominant mechanisms contributing to excellent adsorption performance. It was demonstrated that halogen bonds play an important role in the adsorption of substances containing chlorine atoms. The Fe3O4@TAPA-BPDA is easy to operate and highly regenerable. A simple magnetic solid-phase extraction (MSPE) method based on the Fe3O4@TAPA-BPDA was then developed for the rapid extraction of five PUHs in real samples, coupled with high-performance liquid chromatography (HPLC) determination. The analytical method developed has a linear range of 0.5-50 ng/mL, and the limit of detection (LOD) ranges from 0.06 to 0.10 ng/mL. The method exhibits good accuracy with recoveries ranged from 74.5 % to 111.4 %. The analytical method was successfully applied to the highly sensitive detection of PUHs in environmental water samples, which highlighting the potential application of the Fe3O4@TAPA-BPDA in the sample pretreatment.