Solid state lithionics based ethylene gas-sensing and ionic decision maker devices

Abstract

In recent years, pollution and environmental hazards have been the main problem globally. Pollution damages the environmental ecosystem. The prevalence of many diseases is rising due in large part to pollution, which affects human health in many ways. The gas sensing devices are very important for monitoring the environmental ecosystem and reducing harmful gases to save our lives. Developing an ethylene gas sensor is very important to solve the spoilage of fruits and save the economic losses, estimated at billions of dollars annually in the agriculture sector and food industry. The development of an ethylene gas sensor and the impact of doping on its characteristics are the main objectives of this project. We have used in-situ doping into WO3 to modify the Li-ion concentration via solid state ionics. The sensor's effectiveness has been demonstrated through tests using ethylene gas. My second objective, decision-making is frequently performed in the areas of computation to get better results in a wide variety of current intelligence activities. The decision-maker device is able to solve different multi-armed bandit problems (MBPs). The ionic decision-maker device also mimics the decision-making process of the human brain. A neural network is developed in our human brain by billions of neurons. Our decision-maker device can predict the image of this neural network data and make decisions. By changing Li ion concentration via solid state Ionics by in-situ doping into WO3, the decision maker device will be developed. Brain-inspired neuromorphic computing is a revolutionary technology to create an intelligent and energy-efficient computing system. Here, we propose a three terminal artificial synaptic ionic decision-maker device based on ion movement. The WO3 based artificial synapse shows synaptic behaviors including excitatory postsynaptic current and pairedpulse facilitation as short-term plasticity. The artificial synapse emulates the transition characteristics of a biological synapse from short-term to longterm plasticity depending on the amplitudes of the applied bias pulses.