Artificial Intelligence (AI) technology has been developing for many years now; not only as an area of technology in and of itself, but also in the various spaces and industries where it can now be found.
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Technology that operates on the nanometre scale often involves intricate systems that are not always suited to the various facets of AI. However, there are some growing areas where AI converges with nanotechnology. In addition to merging the two technologies, combined work in nanotechnology and AI can also boost study in each discipline, possibly leading to all kinds of new tools for gaining insights and communication technologies.
Consider the following areas where AI and nanotechnology are working together.
Microscopy
Although atomic force microscopy (AFM) has seen many significant advances in recent years, it can still be a challenge to get high-quality signals from these imaging devices. The predominant problem is that many of the tip-sample interactions these microscopes rely on are complex, varied and therefore not easy to decipher. AI can be very useful in dealing with these kinds of signal-related issues.
An AI approach known as functional recognition imaging (FR-SPM) looks to address this issue through the direct identification of local actions from measured spectroscopic reactions. This process brings together the use of artificial neural networks (ANNs) with principal component analysis (PCA), utilized to streamline the input data to the neural network.
In another imaging development, researchers from The University of Texas Rio Grande recently announced the creation of a microfluidic channel with a removable nanotextured surface that binds specifically to breast cancer cells. Once linked, it can be extracted and imaged. The imaging is segmented and combined with an AI algorithm that automatically determines if a cell is cancerous based on historical existing cell data. The novel imaging system can contrast historical samples to the cells being evaluated in real-time.
Chemical Modelling
Algorithms are already being used to illustrate the frameworks of a molecules and materials in order to figure out various qualities and how they may interact in various environments. A natural progression has led to the incorporation of AI and the use of complex machine learning algorithms.
From a modelling point-of-view, there are numerous types of parameters that must be correlated to generate either an image or a dynamic depiction of a chemical system. As with some imaging techniques, AI can better analyse information and learn from the past to create a more precise representation of the system under study. For instance, AI can minimise the degree of error related to the geometry or size of a system or particle. This is especially useful for nanomaterials as the several effects and phenomena seen with materials like graphene can often be difficult to recreate.
Portrayal of the structural qualities of nanomaterials has also been resolved by the usage of ANNs. For instance, these algorithms have been used to figure out the configuration of carbon nanotube structures by quantifying structural qualities like alignment and curvature. Furthermore, the portrayal of several qualities of thin films is an issue that has been broadly addressed by using AI, machine learning and neural networks.
Nano-computing
Unsurprisingly, AI is also extremely useful with respect to the future of nanocomputing, which is computing conducted through nanoscale mechanisms. Currently, there are a lot of ways nanocomputing devices can execute a function, and these can cover anything from the physical operations to computational methods. Due to a great deal of these devices depending on intricate physical systems to allow for intricate computational algorithms, machine learning procedures can be used to generate novel information representations for a broad range of uses.
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