Nanotechnology is necessary for computers to help us parse data (not to mention the sensors, cables, networks, and displays that connect computers to the rest of the world,) and data-driven investigation will be a mainstay of nanotechnology.
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Naturally, nanotechnology – the creation, manipulation, and application of parts and particles measured on a nanoscale – has developed alongside computer-driven data science. Advances in either field are soon met with applications in the other, and the progress of each has benefited as a result.
Converging Fields
Recently, scientists have noted how varying fields of endeavor, including nanotechnology and data-based sciences, appear to be converging. That is, advances in discrete fields are informed by – and are applicable – to cutting-edge research in separate fields.
For some scientists, convergence refers to a predicted increase in synergies like this between fields. For others, it is the idea that sectors are beginning to merge, blurring traditional boundary lines between disciplines, and calls for funding and development to focus on areas where previously discrete areas of research overlap. As well as nanotechnology and data-focused fields like computer science, network theory, and artificial intelligence (AI,) convergence has also been noted in biology, neurology, and robotics.
Advances in physics, chemistry, and engineering have led to nanotechnology developments, and some researchers have even demonstrated nanotechnology techniques inspired by biology. Data technologies like AI also rely on biological inspiration by approximating the structure of neurons in a brain in so-called “neural networks.”
For many scientists, nanotechnology and data technologies are converging in a similar way. Interdisciplinary efforts between these fields are already bearing fruitful results with applications in medicine, microscopy, chemical modeling, material analysis, and even agricultural research.
More Precision in Cancer Detection
In medicine, AI and nanotechnology are being combined to achieve treatments that can be precisely tailored to meet the needs of individual cancer patients. Patient data acquisition is improving with the development of low-cost, passive, smart sensing devices based on nanotechnology. At the same time, AI is being used to design nanomaterials, such as precisely combining different nanoparticles in specific nanostructures, that can more effectively detect cancer in the body.
The higher selectivity that nanotechnology brings enables caregivers to establish a patient-specific disease profile that can be targeted with a bespoke set of therapeutic nanotechnologies to increase positive treatment outcomes. But AI must also be used to effectively process the extra information acquired by advanced nanotechnology-based devices and output useful information.
In a symbiotic relationship, nanotechnology-based therapies also benefit from data-driven investigations. AI is used to model thousands of reiterations of drug compounds and nanostructured delivery systems against biological data to find the best possible treatments for cancer. This data-driven research predicts how treatments interact with biological fluids, the immune system, cell membranes, and vasculature in the patient’s body.
The Next Generation of Microscopy
Atomic force microscopy (AFM) has advanced significantly in recent years, and electronic methods can now be used to break the refractory limit of optical microscopes and image samples near the scale of individual atoms. However, it remains challenging to acquire usable, high-quality data from these devices.
Data sciences process AFM data and output usable information gathered from each data point, representing a discrete atomic force operating in the tiny space between the AFM’s probe tip and the sample material’s surface.
Another image data processing approach in AI referred to as functional recognition imaging (FR-SPM) uses artificial neural networks (ANNs) alongside principal component analysis (PCA) to identify local actions from measured spectroscopic reactions in spectrometric microscopy techniques.
Meanwhile, researchers recently developed a microfluidic channel that had a removable nanotextured surface to bind with breast cancer cells before being extracted and parsed for image data.
The image data is segmented and combined in an AI algorithm that determines if a cell is cancerous based on historical cell data that has already been input. This new imaging system compares the historical samples with cells currently being surveyed in real-time, enabling rapid diagnosis of breast cancer through imaging surveys.
Cutting Edge Technologies Bringing Agriculture into the Twenty-First Century
The scientific field of agriculture – which to the uninitiated may seem rural and unsophisticated – has also been impacted by the convergence of research frontiers between nanotechnology and data-based sciences.
In particular, precision agriculture has developed to maximize crop yields in the face of multiple challenges like climate change, increasing populations, declining soil quality, and globalization. Precision agriculture helps farmers respond immediately to any changes in their crops, reducing waste and increasing yields.
Nanotechnology is used to enhance the nutritional value of composts and improve the action of pesticides. At the same time, data-led investigations coupled with advanced nanomaterial sensor systems based on unmanned drones and satellite imagery can help farmers to precisely target composts and pesticides where they are most needed.
With robotics, data-led agriculture could see a revolution in productivity in the next few years. Further, new agriculture systems such as urban greenhouses using automated hydroponics mechanisms, advanced nanotechnology-based growing additives, and network-connected production to minimize food waste are being made possible by the convergence of data-based science and nanotechnology in the field.
Continue reading: Developing and Testing New Nanomaterials with Anton Paar and EZD.
References and Further Reading
Adir, O., M. Poley, G. Chen, et al (2020). Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine. Advanced Materials. Available at: https://doi.org/10.1002/adma.201901989.
Berger, M. (2013). Artificial intelligence (AI) in nanotechnology research. Nanowerk.com. [Online] Available at: https://www.nanowerk.com/spotlight/spotid=32741.php.
Nanowerk (2014). Nanotechnology and big data - the next industrial revolution? Nanowerk.com. [Online] Available at: https://www.nanowerk.com/nanotechnology-news/newsid=37154.php.
Sacha, G.M., and P. Varona (2013). Artificial intelligence in nanotechnology. Nanotechnology. Available at: https://doi.org/10.1088/0957-4484/24/45/452002.
Smith, B. (2019). AI and Nanotechnology - How do They Work Together? AZoNano.com. [Online] Available at: https://www.azonano.com/article.aspx?ArticleID=5116.
Zhang, P., Z. Guo, S. Ullah, S., et al (2021). Nanotechnology and artificial intelligence to enable sustainable and precision agriculture. Nature Plants. Available at: https://doi.org/10.1038/s41477-021-00946-6.
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