Carbyne as a promising material for E-nose applications with machine learning

Abstract

There has been a lot of study and advancement in the area of carbon allotropes in the last several decades, driven by the exceptional and diverse physical and chemical characteristics of carbon nanomaterials. For example, nanostructured forms such as carbon nanotubes, graphene, and carbon quantum dots have the potential to revolutionize various industries [1–3]. The global scientific community continues to research in the field of creating new materials, particularly low-dimensional carbon allotropes such as carbon nanotubes (CNTs) and carbyne. Carbyne is a one-dimensional carbon allotrope with a large surface area, chemical reactivity, and gas molecule adsorption potential that makes it extremely sensitive to gases and electronic nose (E-nose) applications due to its linear sp-hybridized atomic chain structure. The primary objective of this work is to increase the sensitivity, selectivity, and overall efficiency of E-nose systems using a synergistic combination of carbyne-based sensing components with cutting-edge machine learning techniques. The exceptional electronic properties of carbyne, such as its high electron mobility and adjustable bandgap, enable rapid and specific adsorption of various gas molecules. Additionally, its significant surface area-to-volume ratio enhances the detection of trace concentrations. Our suggested advanced hybrid system utilises support vector machines (SVMs) and convolutional neural networks (CNNs) as sophisticated machine learning approaches to analyse data provided by carbyne sensors. These algorithms enhance the precision and durability of gas detection by effectively recognising intricate patterns and correlations in the sensor data. Empirical evidence suggests that E-nose systems based on carbyne have superior performance in terms of reaction time, sensitivity, and specificity compared to conventional materials. This research emphasises the revolutionary potential of carbyne in the advancement of next-generation gas sensing systems, which has significant implications for applications in environmental monitoring, medical diagnostics, and industrial process control.