Single-Step, Rapidly Reconfigurable Grayscale Nanoprinting by Light-Controlled Nanocapillary Effect

Iowa State University, Ames IA

Investigators

Abstract

Meta-surfaces are material surfaces decorated with nanoscale features, which often acquire a variety of useful characteristics. Some of them generate vibrant colors without using dyes. Others can turn the moisture on them into tiny beads and clean themselves, a very useful feature for solar panels. Some others can even kill germs on them without using harmful chemicals. Meta-surfaces with such useful properties benefit society by promoting health, safety and sustainability. Recently, scientists discovered that spatially varying the height of the nanoscale surface features, or nanopixels, can make the meta-surfaces multi-functional. The key challenge is manufacturing the nanopixels with variable heights in a controllable manner. This award supports research to find a simple method to realize such variable height nanopixels. It is analogous to printing grayscale pixels. The grayscale nanoprinting method is based on the phenomenon of capillary action and its nanoscale control by light. To expedite the optimal design of the variable height meta-surfaces, the research approach integrates data science and machine learning. Accordingly, the research not only enriches nanomanufacturing but also advances fundamental nanotechnology and computational sciences through interdisciplinary endeavors. Plans for making science and technology more attractive to the general public and more accessible to women and underrepresented groups are included. This research explores light-controlled capillary effect for grayscale nanoprinting and nanotexturing. Conventional nanoprinting produces constant height nanopixels because the capillary rise of the photopolymer into the nanocavity cannot be stopped at an arbitrarily chosen height, nor can modulating the height as a function of position be possible. This research seeks to achieve both by exploiting the discovery that the capillary rise of certain polymers can be accurately controlled by light. The key enabling mechanism is the light-induced changes in the polymer’s liquid volume and viscosity which, in turn, govern the level of the polymer’s capillarity. Optical control is highly advantageous because light patterns can be rapidly updated by a spatial light modulator and easily shrunk down to the microscale by reduction optics, enabling rapidly reconfigurable, ultrahigh-resolution grayscale nanoprinting. This research establishes a novel nanomanufacturing paradigm and provides a useful tool for studying the physical process of polymeric capillarity. The collected data is leveraged to train a combination of advanced statistical and machine learning models to enable data-driven optimal design of meta-surfaces and their manufacturing. To combat the issues of data paucity and complex underlying physics, a synergistic combination of generalized models and deep learning is adopted to represent multi-physical performance targets, such as hydrophobicity and transparency. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.