Jaeyoun Kim

Achieving strongly saturated structural coloring in pixels made entirely of low‐index dielectrics, such as polymers or glass, is a challenging task as they lack the index contrast for narrowband resonances. Forming gratings or photonic crystals is an alternative, but their large footprints often degrade the printing resolution. Recently, standalone low‐index pixels have been demonstrated in the form of microscale ridges (MRs). Although they are compact and easy to fabricate, their color purity trails behind that of resonator‐ or periodic pattern‐based counterparts. In this work, we spectrally condense the MR‐generated color by “cascading” the MR's coloring mechanism, i.e., spectrally selective self‐similar beam formation. To that end, we optimize the multi‐stage cascaded MR design computationally, fabricate the optimal designs through 3D‐nanoprinting of polymer, and characterize the spectra and profiles of the self‐similar beams. The results show that a 3‐stage cascading can narrow the transmission's spectral bandwidth from >300 to ∼100 nm and boost its peak‐to‐valley ratio from <2.0 to 3.7–6.2, while maintaining the cost‐effectiveness and manufacturability of polymer. With a wide color tuning range and sub‐10‐micrometer pixels, this scheme provides a viable pathway for high‐resolution, high‐saturation structural coloring.

Microscale optical beams can be especially useful when generated by micrometric platforms. Here, we achieve one such combination by exploiting the concept of the self-similar beam. Fresnel diffraction-based reformulation of the self-similar beam leads to a compact platform design realizable with 3D-printing of low-index photopolymers. The final design, refined through numerical simulations, is only 1050 nm high and 5500 nm wide and generates bright-core microbeams out of unstructured plane wave excitation. Experimentally, we realized the design by 3D-nanoprinting and produced self-similar bright beams that maintained ±15 µm beam width over the millimetric propagation range. Their characteristics are close to those of quasi-non-diffracting microbeams. We also confirmed the self-healing characteristic of the microbeams.

Quasi‐nondiffracting (QND) optical beams are well known for their long‐range propagation and self‐healing capabilities. Shrinking their beam width down to the mesoscale regime can open up many new applications in microtechnology, but the complexity of the beam‐forming platform remains the major hindrance to its realization. In contrast, the photonic nanojets (PNJs) boast simple platforms for their generation and inherently small beam size, but their utilization has been limited mainly to near‐field applications. This work aims to hybridize the QND and PNJ beam concepts, eventually realizing microscale, self‐healing QND beams using a simple, PNJ‐style platform. To that end, the microscale cuboids made of low‐index dielectric materials are dually utilized, first to generate two self‐decelerating corner‐diffraction beams and then to superpose them into a long‐range (10 λ –100 λ ) microbeam. The QND–PNJ hybridization concept and model will contribute synergistically to the study and application of the microscale optical beams.

We demonstrate neuromorphic behavior in silicon nanosheet (NS) assemblies. We utilized Cl- and H-passivated Si-NSs that were synthesized through topotactic deintercalation of the Zintl phase of CaSi₂ with concentrated HCl at -35 °C. We utilize a silicon-based platform in which we fabricated Au source-drain (S-D) electrodes on SiO₂/Si wafers by photolithography. We fabricated encapsulated assemblies of Si-NS between gold S-D electrodes at mesoscopic channel lengths (30-80 μm) using solution processing methods. The Si-NS assemblies exhibit novel neuromorphic characteristics in which spiking voltage inputs lead to decaying spiking current outputs, analogous to plasticity known for neuron-synapse systems. Decaying spiking current outputs follow a power-law decay (∼t^-β) with β ∼ 0.7-1.1. DFT and electron-trapping simulations suggest that such neuroplastic characteristics may arise from the charging and release of charge from Si-dangling bonds at the NS surfaces. Higher-frequency measurements also reveal an initial learning phase, where the spiking voltage outputs gradually build up to a maximum value, analogous to the potentiation process in neuron-synapse systems. These observed neuromorphic characteristics indicate that Si-NSs are a promising material for next-generation neuromorphic networks with immense potential for energy-efficient, brain-inspired computing.

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.

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.

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.

Nanolenses are gaining importance in nanotechnology, but their challenging fabrication is thwarting their wider adoption. Of particular challenge is facile control of the lens’ curvature. In this work, we demonstrate a new nanoimprinting technique capable of realizing polymeric nanolenses in which the nanolens’ curvature is optically controlled by the ultraviolet (UV) dose at the pre-curing step. Our results reveal a regime in which the nanolens’ height changes linearly with the UV dose. Computational modeling further uncovers that the polymer undergoes highly nonlinear dynamics during the UV-controlled nanoimprinting process. Both the technique and the process model will greatly advance nanoscale science and manufacturing technology.

Replica molding-based triboelectrification has emerged as a new and facile technique to generate nanopatterned tribocharge on elastomer surfaces. The “mechano-triboelectric charging model” has been developed to explain the mechanism of the charge formation and patterning process. However, this model has not been validated to cover the full variety of nanotexture shapes. Moreover, the experimental estimation of the tribocharge’s surface density is still challenging due to the thick and insulating nature of the elastomeric substrate. In this work, we perform experiments in combination with numerical analysis to complete the mechano-triboelectrification charging model. By utilizing Kelvin probe force microscopy (KPFM) and finite element analysis, we reveal that the mechano-triboelectric charging model works for replica molding of both recessed and protruding nanotextures. In addition, by combining KPFM with numerical electrostatic modeling, we improve the accuracy of the surface charge density estimation and cross-calibrate the result against that of electrostatic force microscopy. Overall, the regions which underwent strong interfacial friction during the replica molding exhibited high surface potential and charge density, while those suffering from weak interfacial friction exhibited low values on both. These multi-physical approaches provide useful and important tools for comprehensive analysis of triboelectrification and generation of nanopatterned tribocharge. The results will widen our fundamental understanding of nanoscale triboelectricity and advance the nanopatterned charge generation process for future applications.

Attempts to use machine learning to discover hidden physical rules are in their infancy, and such attempts confront more challenges when experiments involve multifaceted measurements over three-dimensional objects. Here we propose a framework that can infuse scientists’ basic knowledge into a glass-box rule learner to extract hidden physical rules behind complex physics phenomena. A “convolved information index” is proposed to handle physical measurements over three-dimensional nano-scale specimens, and the multi-layered convolutions are “externalized” over multiple depths at the information level, not in the opaque networks. A transparent, flexible link function is proposed as a mathematical expression generator, thereby pursuing “glass-box” prediction. Consistent evolution is realized by integrating a Bayesian update and evolutionary algorithms. The framework is applied to nano-scale contact electrification phenomena, and results show promising performances in unraveling transparent expressions of a hidden physical rule. The proposed approach will catalyze a synergistic machine learning-physics partnership.