As
the twenty-first century unfolds, nanotechnology is no longer
just a buzzword in the field of materials science, but rather a tangible
reality. This is evident from the surging number of commercial nanoproducts
and their corresponding revenue generated in different industry sectors.
However, it is important to recognize that sustainable growth of nanotechnology
is heavily dependent on government funding and relevant national incentive
programs. Consequently, proper analyses on publicly available nanotechnology
data sets comprising information on the past two decades can be illuminating,
facilitate development, and amend previous strategies as we move forward.
Along these lines, classical statistics and machine learning (ML)
allow processing large data sets to scrutinize patterns in materials
science and nanotechnology research. Herein, we provide an analysis
on nanotechnology progress and investment from an unbiased, computational
vantage point and using orthogonal approaches. Our data reveal both
well-established and surprising correlations in the nanotechnology
field and its actors, including the interplay between the number of
research institutes–industry, publications–patents,
collaborative research, and top contributors to nanoproducts. Overall,
data suggest that, supported by incentive programs set out by stakeholders
(researchers, funding agencies, policy makers, and industry), nanotechnology
could experience an exponential growth and become a centerpiece for
economical welfare. Indeed, the recent success of COVID-19 vaccines
is also likely to boost public trust in nanotechnology and its global
impact over the coming years.