Synapse-Mimetic Hardware-Implemented Resistive Random-Access Memory for Artificial Neural Network.

Memristors mimic synaptic functions in advanced electronics and image sensors, thereby enabling brain-inspired neuromorphic computing to overcome the limitations of the von Neumann architecture. As computing operations based on von Neumann hardware rely on continuous memory transport between processing units and memory, fundamental limitations arise in terms of power consumption and integration density. In biological synapses, chemical stimulation induces information transfer from the pre- to the post-neuron. The memristor operates as resistive random-access memory (RRAM) and is incorporated into the hardware for neuromorphic computing. Hardware composed of synaptic memristor arrays is expected to lead to further breakthroughs owing to their biomimetic in-memory processing capabilities, low power consumption, and amenability to integration; these aspects satisfy the upcoming demands of artificial intelligence for higher computational loads. Among the tremendous efforts toward achieving human-brain-like electronics, layered 2D materials have demonstrated significant potential owing to their outstanding electronic and physical properties, facile integration with other materials, and low-power computing. This review discusses the memristive characteristics of various 2D materials (heterostructures, defect-engineered materials, and alloy materials) used in neuromorphic computing for image segregation or pattern recognition. Neuromorphic computing, the most powerful artificial networks for complicated image processing and recognition, represent a breakthrough in artificial intelligence owing to their enhanced performance and lower power consumption compared with von Neumann architectures. A hardware-implemented CNN with weight control based on synaptic memristor arrays is expected to be a promising candidate for future electronics in society, offering a solution based on non-von Neumann hardware. This emerging paradigm changes the computing algorithm using entirely hardware-connected edge computing and deep neural networks.

Simulation of novel jellyfish type of process for bioremediation application.

A bioinspired device was fabricated as a sustainable remedial method and its performance as a membrane-enzyme reactor with cyclic ultrafiltration was investigated. The body of the jellyfish-like device was composed of two parts: 1) Jellyfish arms: Mono and co-axial electrospinning have been utilized to synthesize the flexible parts (e.g., multilayer membrane PS-PVDF/PAN/PS-PVDF) used for immobilization of aliphatic degrading enzymes, and 2) Jellyfish tentacles: Hollow fiber membranes were selected for physical immobilization of polycyclic aromatic hydrocarbon (PAH) degrading enzymes. To study the behavior of the membrane/enzyme reactor, the hollow fiber enzyme reactor with pulsation was operated by recycling an enzyme solution to assess ultrafiltration efficiency. A mathematical model was suggested to describe the experimental data obtained in this study to predict the effectiveness of the reactor for PAH removal. When testing the performance of the jellyfish-like device, those equipped with nanofibers with an oil sorption capacity of (10. ±0.7gdilbit/gfiber) were more effective at removing oil particles before they touched the hollow fiber membrane surface. Moreover, the reaction rate measured in a free soluble enzyme and a recirculating immobilized enzyme solution exhibited a slight difference in the kinetic parameter, Km (0.03 and 0.021 mM) due to the internal diffusional resistance. Based on biodegradation studies, a synergistic effect between membrane adsorption, enzymatic degradation, and ultrafiltration was proposed for the removal of anthracene from the column of water.

Bioinspired Microfluidic Device by Integrating a Porous Membrane and Heterostructured Nanoporous Particles for Biomolecule Cleaning.

Mimicking the structures and functions of biological systems is considered as a promising approach to construct artificial materials, which have great potential in energy, the environment, and health. Here, we demonstrate a conceptually distinct design by synergistically combining a kidney-inspired porous membrane and natural sponge-inspired heterostructured nanoporous particles to fabricate a bioinspired biomolecule cleaning device, achieving highly efficient biomolecule cleaning spanning from small molecules to macromolecules. The bioinspired biomolecule cleaning device is a two-layer microfluidic device that integrates a polyamide porous membrane and heterostructured nanoporous poly(acrylic acid)-poly(styrene divinylbenzene) particles. The former as a filtration membrane isolates the upper sample liquid and the latter fixed onto the bottom of the underlying channel acts as an active sorbent, particularly enhancing the clearance of macromolecules. As a proof-of-concept, we demonstrate that typical molecules, including urea, creatinine, lysozyme, and ß2-microglobulin, can be efficiently cleaned from simulant liquid and even whole blood. This study provides a method to fabricate a bioinspired biomolecule cleaning device for highly efficient biomolecule cleaning. We believe that our bioinspired synergistic design may expand to other fields for the fabrication of integrated functional devices, creating opportunities in a wide variety of applications.