Laser-Induced Trapping of Microbubbles within the Bulk Solution.

The generation and manipulation of microbubbles on surfaces have been much more ubiquitous than those in the bulk liquid. In this paper, we report the generation and trapping of microbubbles at the laser boundary in the bulk of water. In a plasmonic suspension, the light intensity-induced temperature gradient leads to a Marangoni flow that can trap the microbubble.

Fluorinated Self-Assembled Monolayer Ion Receptors for Retentive Analog Synaptic Behavior.

Numerous studies have highlighted ionic transistors as a promising candidate for retentive artificial synapses in analog neuromorphic computing, owing to their exceptional characteristics of analog signal modulation and low energy consumption. However, the self-discharge of ions at the channel-electrolyte interface limits their retention characteristics. Therefore, it is crucial to control the ion discharge kinetics by engineering interfacial electrostatic interactions.

W-band frequency selective digital metasurface using active learning-based binary optimization.

The W-band is essential for applications like high-resolution imaging and advanced monitoring systems, but high-frequency signal attenuation leads to poor signal-to-noise ratios, posing challenges for compact and multi-channel systems. This necessitates distinct frequency selective surfaces (FSS) on a single substrate, a complex task due to inherent substrate resonance modes. In this study, we use a digital metasurface platform to design W-band FSS on a glass substrate, optimized through binary optimization assisted by active learning.

Visible transparency modulated cooling windows using pseudorandom dielectric multilayers.

The increasing global temperatures have escalated the demand for indoor cooling, thus requiring energy-saving solutions. Traditional approaches often integrate metal layers in cooling windows to block near-infrared (NIR) sunlight, which, albeit effective, lack the broad modulation of visible transmission and lead to heat accumulation due to sunlight absorption. Here, we address these limitations by developing cooling windows using ZnS/MgF multilayers, optimized through a binary optimization-based active learning process.

Inverse binary optimization of convolutional neural network in active learning efficiently designs nanophotonic structures.

Binary optimization using active learning schemes has gained attention for automating the discovery of optimal designs in nanophotonic structures and material configurations. Recently, active learning has utilized factorization machines (FM), which usually are second-order models, as surrogates to approximate the hypervolume of the design space, benefiting from rapid optimization by Ising machines such as quantum annealing (QA). However, due to their second-order nature, FM-based surrogate functions struggle to fully capture the complexity of the hypervolume.

Simultaneous Improvement of Multiple Electrical Characteristics in Ultrathin a-IGZO TFTs Using Machine Learning Optimization.

While IGZO is emerging as a promising channel material to address the scaling limitations of conventional silicon-based DRAM, its application in next-generation 3D DRAM requires further advancements in achieving ultrathin structures and excellent performance tailored to DRAM characteristics. Specifically, optimizing process variables is essential for enhancing mobility in ultrathin structures, where mobility tends to degrade significantly, while maintaining a constant threshold voltage, a task that is both experimentally intensive and resource-demanding. This study employed multi-objective Bayesian optimization (MOBO) machine learning (ML) to simultaneously optimize multiple electrical objectives, aiming to achieve high mobility and a near-zero threshold voltage for ultrathin IGZO thin-film transistors (TFTs) under complex sputtering conditions, involving a wide range of possible combinations of Ar gas flow, sputtering power, and working pressure.

Machine Learning Strategy for Optimizing Multiple Electrical Characteristics in Dual-Layer Oxide Thin Film Transistors.

A machine learning (ML) strategy is suggested to optimize dual-layer oxide thin film transistor (TFTs) performance. In this study, Bayesian optimization (BO), an algorithm recognized for its efficiency in optimizing material design, is applied to guide the design of a channel layer composed of IZO and IGZO. The sputtering fabrication process, which has attracted attention as an oxide semiconductor channel layer deposition method, is fine-tuned using ML to enhance multiple electrical characteristics of transistors: field-effect mobility, threshold voltage, and subthreshold swing.

Quantum annealing-aided design of an ultrathin-metamaterial optical diode.

Thin-film optical diodes are important elements for miniaturizing photonic systems. However, the design of optical diodes relies on empirical and heuristic approaches. This poses a significant challenge for identifying optimal structural models of optical diodes at given wavelengths.

Electrochemical Analysis of Ion Effects on Electrolyte-Gated Synaptic Transistor Characteristics.

Electrolyte-gated transistors (EGTs) are promising candidates as artificial synapses owing to their precise conductance controllability, quick response times, and especially their low operating voltages resulting from ion-assisted signal transmission. However, it is still vague how ion-related physiochemical elements and working mechanisms impact synaptic performance. Here, to address the unclear correlations, we suggest a methodical approach based on electrochemical analysis using poly(ethylene oxide) EGTs with three alkali ions: Li, Na, and K.

Enhanced Thermal Boundary Conductance across GaN/SiC Interfaces with AlN Transition Layers.

Heat dissipation plays a crucial role in the performance and reliability of high-power GaN-based electronics. While AlN transition layers are commonly employed in the heteroepitaxial growth of GaN-on-SiC substrates, concerns have been raised about their impact on thermal transport across GaN/SiC interfaces. In this study, we present experimental measurements of the thermal boundary conductance (TBC) across GaN/SiC interfaces with varying thicknesses of the AlN transition layer (ranging from 0 to 73 nm) at different temperatures.

Bubble nucleation and growth on microstructured surfaces under microgravity.

Understanding the dynamics of surface bubble formation and growth on heated surfaces holds significant implications for diverse modern technologies. While such investigations are traditionally confined to terrestrial conditions, the expansion of space exploration and economy necessitates insights into thermal bubble phenomena in microgravity. In this work, we conduct experiments in the International Space Station to study surface bubble nucleation and growth in a microgravity environment and compare the results to those on Earth.

Machine Learning Driven Channel Thickness Optimization in Dual-Layer Oxide Thin-Film Transistors for Advanced Electrical Performance.

Machine learning (ML) provides temporal advantage and performance improvement in practical electronic device design by adaptive learning. Herein, Bayesian optimization (BO) is successfully applied to the design of optimal dual-layer oxide semiconductor thin film transistors (OS TFTs). This approach effectively manages the complex correlation and interdependency between two oxide semiconductor layers, resulting in the efficient design of experiment (DoE) and reducing the trial-and-error.

Analytical model of optical force on supercavitating plasmonic nanoparticles.

Optical manipulation of nanoparticles (NPs) in liquid has garnered increasing interest for various applications, ranging from biological systems to nanofabrication. A plane wave as an optical source has recently been shown to be capable of pushing or pulling an NP when the NP is encapsulated by a nanobubble (NB) in water. However, the lack of an accurate model to describe the optical force on NP-in-NB systems hinders a comprehensive understanding of NP motion mechanisms.

Wide-angle deep ultraviolet antireflective multilayers via discrete-to-continuous optimization.

To date, various optimization algorithms have been used to design non-intuitive photonic structures with unconventional optical performance. Good training datasets facilitate the optimization process, particularly when an objective function has a non-convex shape containing multiple local optima in a continuous parametric space. Herein, we developed a discrete-to-continuous optimization algorithm and confirmed its validity by designing and fabricating deep-ultraviolet antireflective MgF/LaF multilayers.

Experimental observation of localized interfacial phonon modes.

Interfaces impede heat flow in micro/nanostructured systems. Conventional theories for interfacial thermal transport were derived based on bulk phonon properties of the materials making up the interface without explicitly considering the atomistic interfacial details, which are found critical to correctly describing thermal boundary conductance. Recent theoretical studies predicted the existence of localized phonon modes at the interface which can play an important role in understanding interfacial thermal transport.

Negative optical force field on supercavitating titanium nitride nanoparticles by a single plane wave.

A pulling motion of supercavitating plasmonic nanoparticle (NP) by a single plane wave has received attention for the fundamental physics and potential applications in various fields (, bio-applications, nanofabrication, and nanorobotics). Here, the supercavitating NP depicts a state where a nanobubble encapsulates the NP, which can be formed via the photo-thermal heating process in a liquid. In this letter, we theoretically study the optical force on a supercavitating titanium nitride (TiN) NP by a single plane wave at near-infrared wavelengths to explore optical conditions that can potentially initiate the backward motion of the NP against the wave-propagating direction.

Ballistic Brownian motion of supercavitating nanoparticles.

We show that the Brownian motion of a nanoparticle (NP) can reach a ballistic limit when intensely heated to form supercavitation. As the NP temperature increases, its Brownian motion displays a sharp transition from normal to ballistic diffusion upon the formation of a vapor bubble to encapsulate the NP. Intense heating allows the NP to instantaneously extend the bubble boundary via evaporation, so the NP moves in a low-friction gaseous environment.

Optically Driven Gold Nanoparticles Seed Surface Bubble Nucleation in Plasmonic Suspension.

Photothermal surface bubbles play important roles in applications like microfluidics and biosensing, but their formation on transparent substrates immersed in a plasmonic nanoparticle (NP) suspension has an unknown origin. Here, we reveal NPs deposited on the transparent substrate by optical forces are responsible for the nucleation of such photothermal surface bubbles. We show the surface bubble formation is always preceded by the optically driven NPs moving toward and deposited to the surface.

Bulk-like Intrinsic Phonon Thermal Conductivity of Micrometer-Thick AlN Films.

Aluminum nitride (AlN) has garnered much attention due to its intrinsically high thermal conductivity. However, engineering thin films of AlN with these high thermal conductivities can be challenging due to vacancies and defects that can form during the synthesis. In this work, we report on the cross-plane thermal conductivity of ultra-high-purity single-crystal AlN films with different thicknesses (∼3-22 μm) via time-domain thermoreflectance (TDTR) and steady-state thermoreflectance (SSTR) from 80 to 500 K.

Ballistic supercavitating nanoparticles driven by single Gaussian beam optical pushing and pulling forces.

Directed high-speed motion of nanoscale objects in fluids can have a wide range of applications like molecular machinery, nano robotics, and material assembly. Here, we report ballistic plasmonic Au nanoparticle (NP) swimmers with unprecedented speeds (~336,000 μm s) realized by not only optical pushing but also pulling forces from a single Gaussian laser beam. Both the optical pulling and high speeds are made possible by a unique NP-laser interaction.

Surface Bubble Growth in Plasmonic Nanoparticle Suspension.

Understanding the growth dynamics of the microbubbles produced by plasmonic heating can benefit a wide range of applications like microfluidics, catalysis, micropatterning, and photothermal energy conversion. Usually, surface plasmonic bubbles are generated on plasmonic structures predeposited on the surface subject to laser heating. In this work, we investigate the growth dynamics of surface microbubbles generated in plasmonic nanoparticle (NP) suspension.

Light-Guided Surface Plasmonic Bubble Movement via Contact Line De-Pinning by In-Situ Deposited Plasmonic Nanoparticle Heating.

Precise spatiotemporal control of surface bubble movement can benefit a wide range of applications like high-throughput drug screening, combinatorial material development, microfluidic logic, colloidal and molecular assembly, and so forth. In this work, we demonstrate that surface bubbles on a solid surface are directed by a laser to move at high speeds (>1.8 mm/s), and we elucidate the mechanism to be the depinning of the three-phase contact line (TPCL) by rapid plasmonic heating of nanoparticles (NPs) deposited in situ during bubble movement.

Tuning Water Slip Behavior in Nanochannels Using Self-Assembled Monolayers.

Water slip at solid surfaces is important for a wide range of micro-/nanofluidic applications. While it is known that water slip behavior depends on surface functionalization, how it impacts the molecular level dynamics and mass transport at the interface is still not thoroughly understood. In this paper, we use nonequilibrium molecular dynamics simulations to investigate the slip behavior of water confined between gold surfaces functionalized by self-assembled monolayer (SAM) molecules with different polar functional groups.