RESUMEN
The conventional von Neumann architecture has proven to be inadequate in keeping up with the rapid progress in artificial intelligence. Memristors have become the favored devices for simulating synaptic behavior and enabling neuromorphic computations to address challenges. An artificial synapse utilizing the perovskite structure PbHfO3 (PHO) has been created to tackle these concerns. By employing the sol-gel technique, a ferroelectric film composed of Au/PHO/FTO was created on FTO/glass for the purpose of this endeavor. The artificial synapse is composed of Au/PHO/FTO and exhibits learning and memory characteristics that are similar to those observed in biological neurons. The recognition accuracy for both MNIST and Fashion-MNIST data sets saw an increase, reaching 92.93% and 76.75%, respectively. This enhancement resulted from employing a convolutional neural network architecture and implementing an improved stochastic adaptive algorithm. The presented findings showcase a viable approach to achieve neuromorphic computation by employing artificial synapses fabricated with PHO.
RESUMEN
Compared with purely electrical neuromorphic devices, those stimulated by optical signals have gained increasing attention due to their realistic sensory simulation. In this work, an optoelectronic neuromorphic device based on a photoelectric memristor with a Bi2FeCrO6/Al-doped ZnO (BFCO/AZO) heterostructure is fabricated that can respond to both electrical and optical signals and successfully simulate a variety of synaptic behaviors, such as STP, LTP, and PPF. In addition, the photomemory mechanism was identified by analyzing the energy band structures of AZO and BFCO. A convolutional neural network (CNN) architecture for pattern classification at the Mixed National Institute of Standards and Technology (MNIST) was used and improved the recognition accuracy of the MNIST and Fashion-MNIST datasets to 95.21% and 74.19%, respectively, by implementing an improved stochastic adaptive algorithm. These results provide a feasible approach for future implementation of optoelectronic synapses.
RESUMEN
Neuromorphic computing, which mimics biological neural networks, is widely regarded as the optimal solution for addressing the limitations of traditional von Neumann computing architecture. In this work, an adjustable multistage resistance switching ferroelectric Bi2FeCrO6 diode artificial synaptic device was fabricated using a sol-gel method with a simple process. The device exhibits nonlinearity in its electrical characteristics, demonstrating tunable multistage resistance switching behavior and a strong ferroelectric diode effect through the manipulation of ferroelectric polarization. One of its salient advantages resides in its capacity to dynamically regulate its polarization state in response to an external electric field, thereby facilitating the fine-tuning of synaptic connection strength while maintaining synaptic stability. The device is capable of accurately simulating the fundamental properties of biological synapses, including long/short-term plasticity, paired-pulse facilitation, and spike-timing-dependent plasticity. Additionally, the device exhibits a distinctive photoelectric response and is capable of inducing synaptic plasticity by light signal activation. The utilization of a femtosecond laser for the scrutiny of carrier transport mechanisms imparts profound insights into the intricate dynamics governing the optical memory effect. Furthermore, utilizing a convolutional neural network (CNN) architecture, the recognition accuracy of the MNIST and fashion MNIST datasets was improved to 95.6% and 78%, respectively, through the implementation of improved random adaptive algorithms. These findings present a new opportunity for utilizing Bi2FeCrO6 materials in the development of artificial synapses for neuromorphic computation.
RESUMEN
Antibiotics play an essential role in the treatment of various diseases. However, the overuse of antibiotics has led to the pollution of water bodies and food safety, affecting human health. Herein, we report a dual-emission MOF-based flexible sensor for the detection of antibiotics in water, which was prepared by first encapsulating rhodamine B (RhB) by a zeolite imidazolium ester skeleton (ZIF-8) and then blending it with polyvinylidene difluoride (PVDF). The luminescent properties, structural tunability, and flexible porosity of the MOF-based composites were combined with the processability and flexibility of polymers to prepare luminescent membranes. The sensor is capable of dual-emission ratiometric fluorescence sensing of nitrofurantoin (NFT) and oxytetracycline (OTC), exhibiting sensitive detection of fluorescence burst and fluorescence enhancement, respectively, with detection limits of 0.012 µM and 8.9 nM. With the advantages of visual detection, high sensitivity, short detection time, and simplicity, the highly sensitive ratiometric fluorescent flexible sensor has great potential for detecting antibiotics in an aqueous environment. It will further stimulate interest in luminescent MOF-based mixed matrix membranes and their sensing applications.
RESUMEN
Transmembrane protein family members (TMEMs) span the entire lipid bilayer and act as channels that allow the transport of specific substances through biofilms. The functions of most TMEMs are unexplored. Numerous studies have shown that TMEMs are involved in the pathophysiological processes of various nervous system diseases, but the specific mechanisms of TMEMs in the pathogenesis of diseases remain unclear. In this review, we discuss the expression, physiological functions, and molecular mechanisms of TMEMs in brain tumors, psychiatric disorders, abnormal motor activity, cobblestone lissencephaly, neuropathic pain, traumatic brain injury, and other disorders of the nervous system. Additionally, we propose that TMEMs may be used as prognostic markers and potential therapeutic targets in patients with various neurological diseases.
