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Abstract Nature finds ways to realize multi-functional surfaces by modulating nano-scale patterns on their surfaces, enjoying transparent, bactericidal, and/or anti-fogging features. Therein height distributions of nanopatterns play a key role. Recent advancements in nanotechnologies can reach that ability via chemical, mechanical, or optical fabrications. However, they require laborious complex procedures, prohibiting fast mass manufacturing. This paper presents a computational framework to help design multi-functional nano patterns by light. The framework behaves as a surrogate model for the inverse design of nano distributions. The framework’s hybrid (i.e., human and artificial) intelligence-based approach helps learn plausible rules of multi-physics processes behind the UV-controlled nano patterning and enriches training data sets. Then the framework’s inverse machine learning (ML) model can describe the required UV doses for the target heights of liquid in nano templates. Thereby, the framework can realize multiple functionalities including the desired nano-scale color, frictions, and bactericidal properties. Feasibility test results demonstrate the promising capability of the framework to realize the desired height distributions that can potentially enable multi-functional nano-scale surface properties. This computational framework will serve as a multi-physics surrogate model to help accelerate fast fabrications of nanopatterns with light and ML. 

Abstract Predicting individual large earthquakes (EQs)’ locations, magnitudes, and timing remains unreachable. The author’s prior study shows that individual large EQs have unique signatures obtained from multi-layered data transformations. Via spatio-temporal convolutions, decades-long EQ catalog data are transformed into pseudo-physics quantities (e.g., energy, power, vorticity, and Laplacian), which turn into surface-like information via Gauss curvatures. Using these new features, a rule-learning machine learning approach unravels promising prediction rules. This paper suggests further data transformation via Fourier transformation (FT). Results show that FT-based new feature can help sharpen the prediction rules. Feasibility tests of large EQs ($$M\ge$$ $M\ge$6.5) over the past 40 years in the western U.S. show promise, shedding light on data-driven prediction of individual large EQs. The handshake among ML methods, Fourier, and Gauss may help answer the long-standing enigma of seismogenesis. 

The multipole interference (MPI) effect plays pivotal roles in the formation of electromagnetic responses in various settings. In the optics regime, it has been realized typically through the Mie resonance that necessitates high‐index, deep‐subwavelength‐scale dielectric resonators that are challenging to fabricate. Herein, a new, diffraction‐based MPI scheme that can be realized with low‐index, mesoscale dielectric structures is demonstrated. It is verified that this “diffractive MPI” concept by realizing various MPI states using micrometric polymeric cuboids fabricated by soft‐lithography. Subsequent analyses reveal that the MPI states with a distinct near‐zero forward scattering (NZFS) characteristic played crucial roles in shaping the cuboid's transmission spectrum. A hitherto unreported NZFS state, which exhibits a unique, “trifolium” radiation pattern, is also identified. The spectral position of such NZFS states turns out to be strongly dependent on the cuboid's geometry. By combining these results, the diffractive NZFS formation is related to the important phenomena of induced transparency and structural color generation. 

Multifunctional nanosurfaces receive growing attention due to their versatile properties. Capillary force lithography (CFL) has emerged as a simple and economical method for fabricating these surfaces. In recent works, the authors proposed to leverage the evolution strategies (ES) to modify nanosurface characteristics with CFL to achieve specific functionalities such as frictional, optical, and bactericidal properties. For artificial intelligence (AI)-driven inverse design, earlier research integrates basic multiphysics principles such as dynamic viscosity, air diffusivity, surface tension, and electric potential with backward deep learning (DL) on the framework of ES. As a successful alternative to reinforcement learning, ES performed well for the AI-driven inverse design. However, the computational limitations of ES pose a critical technical challenge to achieving fast and efficient design. This paper addresses the challenges by proposing a parallel-computing-based ES (named parallel ES). The parallel ES demonstrated the desired speed and scalability, accelerating the AI-driven inverse design of multifunctional nanopatterned surfaces. Detailed parallel ES algorithms and cost models are presented, showing its potential as a promising tool for advancing AI-driven nanomanufacturing. 

Understanding the dynamic behavior of photopolymers in nanoscale environment is essential to improving MEMS/NEMS device fabrication technologies. Here, we unveil the highly nonlinear behaviors of photopolymers exhibited during the process of light-controlled, low-pressure nanoimprinting. Such peculiarities can complicate the relation between the UV-dose and the height of the nanoimprinted feature, degrading the accuracy of the height control. To address the issue, we establish a theoretical process model and used the control of the nanoimprinting height for structural coloring applications. Our findings will broadly benefit nanotechnology and nanoscience. 

Recently, there have been notable advances in nanophotonic structural color generation which enabled various applications in display, anti-counterfeiting, sensors and detectors. However, most advances in this domain have been achieved through the use of high-index materials which require expensive and complex fabrication. In this work, we enable low-index polymer nanostructures to generate structural colors using the multipolar decomposition technique which allows a better understanding and design of the scattering process by identifying the dominant multipole modes from the scattered fields. We set a polymeric (n~1.56) cuboid as the structural color generation platform, examined the contributions of various multipoles from the wave scattered by it, and synthesized the desired color spectrum by adjusting only the height of the cuboid. To validate our findings, we fabricated the designed structural color pixels via light-controlled, low-pressure nanoimprinting and measured the color and spectrum from them. Our experimental results agreed well with the simulation results, providing insights for bringing further advances to structural coloring. 

Nanopatterned tribocharge can be generated on the surface of elastomers through their replica molding with nanotextured molds. Despite its vast application potential, the physical conditions enabling the phenomenon have not been clarified in the framework of analytical mechanics. Here, we explain the final tribocharge pattern by separately applying two models, namely cohesive zone failure and cumulative fracture energy, as a function of the mold nanotexture’s aspect ratio. These models deepen our understanding of the triboelectrification phenomenon. 

Abstract Nature finds a way to leverage nanotextures to achieve desired functions. Recent advances in nanotechnologies endow fascinating multi-functionalities to nanotextures by modulating the nanopixel’s height. But nanoscale height control is a daunting task involving chemical and/or physical processes. As a facile, cost-effective, and potentially scalable remedy, the nanoscale capillary force lithography (CFL) receives notable attention. The key enabler is optical pre-modification of photopolymer’s characteristics via ultraviolet (UV) exposure. Still, the underlying physics of the nanoscale CFL is not well understood, and unexplained phenomena such as the “forbidden gap” in the nano capillary rise (unreachable height) abound. Due to the lack of large data, small length scales, and the absence of first principles, direct adoptions of machine learning or analytical approaches have been difficult. This paper proposes a hybrid intelligence approach in which both artificial and human intelligence coherently work together to unravel the hidden rules with small data. Our results show promising performance in identifying transparent, physics-retained rules of air diffusivity, dynamic viscosity, and surface tension, which collectively appear to explain the forbidden gap in the nanoscale CFL. This paper promotes synergistic collaborations of humans and AI for advancing nanotechnology and beyond. 

The scientific community has been looking for novel approaches to develop nanostructures inspired by nature. However, due to the complicated processes involved, controlling the height of these nanostructures is challenging. Nanoscale capillary force lithography (CFL) is one way to use a photopolymer and alter its properties by exposing it to ultraviolet radiation. Nonetheless, the working mechanism of CFL is not fully understood due to a lack of enough information and first principles. One of these obscure behaviors is the sudden jump phenomenon—the sudden change in the height of the photopolymer depending on the UV exposure time and height of nano-grating (based on experimental data). This paper uses known physical principles alongside artificial intelligence to uncover the unknown physical principles responsible for the sudden jump phenomenon. The results showed promising results in identifying air diffusivity, dynamic viscosity, surface tension, and electric potential as the previously unknown physical principles that collectively explain the sudden jump phenomenon. 

Diffraction gratings are ubiquitous in many optical applications such as sensors, filters, and optical security devices. Capillary force lithography, which utilizes the capillary rise of photopolymer into nanoscale cavities, is a simple and rapid method to construct diffraction gratings without necessitating expensive instruments or complex steps. With the help of spatial light modulators, such as the digital micromirror device, the height of the grating can also be spatially modulated, printing spatially height-modulated gratings. When white light normally impinges on the grating, the light propagates into the grating interferes with light that propagates into air. By varying the height of the grating, the optical path lengths of two lights can be varied, leading to different interference effects and structural coloring. Judicious design of the grating’s parameters and patterning process will even allow encoding of multiple images. In this work, by tuning the height of the grating through the light-controlled capillary force lithography, we demonstrate grating-based structural color printing. This technique is promising for producing the custom patterns for anti-counterfeiting, authentication, and cryptography.