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.
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.
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.
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.
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.
