Donggyu Kim

Nanoscale control of phonon excitations in graphene

Phonons, which are collective excitations in a lattice of atoms or molecules, play a major role in determining various physical properties of condensed matter, such as thermal and electrical conductivities. In particular, phonons in graphene interact strongly with electrons; however, unlike in usual metals, these interactions between phonons and massless Dirac fermions appear to mirror the rather complicated physics of those between light and relativistic electrons. Therefore, a fundamental understanding of the underlying physics through systematic studies of phonon interactions and excitations in graphene is crucial for realising graphene-based devices. In this study, we demonstrate that the local phonon properties of graphene can be controlled at the nanoscale by tuning the interaction strength between graphene and an underlying Pt substrate. Using scanning probe methods, we determine that the reduced interaction due to embedded Ar atoms facilitates electron–phonon excitations, further influencing phonon-assisted inelastic electron tunnelling.

Solving the Controversy on the Wetting Transparency of Graphene

Since its discovery, the wetting transparency of graphene, the transmission of the substrate wetting property over graphene coating, has gained significant attention due to its versatility for potential applications. Yet, there have been debates on the interpretation and validity of the wetting transparency. Here, we present a theory taking two previously disregarded factors into account and elucidate the origin of the partial wetting transparency. We show that the liquid bulk modulus is crucial to accurately calculate the van der Waals interactions between the liquid and the surface, and that various wetting states on rough surfaces must be considered to understand a wide range of contact angle measurements that cannot be fitted with a theory considering the flat surface. In addition, we reveal that the wetting characteristic of the substrate almost vanishes when covered by any coating as thick as graphene double layers. Our findings reveal a more complete picture of the wetting transparency of graphene as well as other atomically thin coatings, and can be applied to study various surface engineering problems requiring wettability-tuning.

Electronic and geometric structure of C60 molecules on Au(001)

The electronic and geometric structures of C60 molecular layers on the Au(001) surface were studied by scanning tunneling microscopy (STM) and spectroscopy (STS) and ultraviolet photoemission spectroscopy (UPS). Due to the lattice mismatch between the overlayer C60 and the substrate Au(100)‐(5×20) surface, a uniaxial stress is applied to the overlayer along the 〈110〉 direction, resulting in several types of oblique lattices. The electron charge density around a C60 molecule appears to be an ellipsoid due to the modified electron charge density by the uniaxial stress. The molecules can be considered to be chemisorbed on the Au substrate by the UPS and STS data. Charge transfer from the substrate to the molecules and intermolecular bonding under stress were observed in STM and STS data.

Optimal Large-Scale Quantum State Tomography With Pauli Measurements

Quantum state tomography aims to determine the state of a quantum system as represented by a density matrix. It is a fundamental task in modern scientific studies involving quantum systems. In this paper, we study estimation of high-dimensional density matrices based on Pauli measurements. In particular, under appropriate notion of sparsity, we establish the minimax optimal rates of convergence for estimation of the density matrix under both the spectral and Frobenius norm losses; and show how these rates can be achieved by a common thresholding approach. Numerical performance of the proposed estimator is also investigated.

The wear properties of carbon/carbon composites prepared by chemical vapour deposition

In this study the wear behaviour of C/C composites was investigated under conditions under which the interfacial temperature does not increase significantly (i.e. low sliding speed and load) in order to obtain information concerning only the frictional wear of C/C composites.

Graphene-coated meshes for electroactive flow control devices utilizing two antagonistic functions of repellency and permeability.

The wettability of graphene on various substrates has been intensively investigated for practical applications including surgical and medical tools, textiles, water harvesting, self-cleaning, oil spill removal and microfluidic devices. However, most previous studies have been limited to investigating the intrinsic and passive wettability of graphene and graphene hybrid composites. Here, we report the electrowetting of graphene-coated metal meshes for use as electroactive flow control devices, utilizing two antagonistic functions, hydrophobic repellency versus liquid permeability. Graphene coating was able to prevent the thermal oxidation and corrosion problems that plague unprotected metal meshes, while also maintaining its hydrophobicity. The shapes of liquid droplets and the degree of water penetration through the graphene-coated meshes were controlled by electrical stimuli based on the functional control of hydrophobic repellency and liquid permeability. Furthermore, using the graphene-coated metal meshes, we developed two active flow devices demonstrating the dynamic locomotion of water droplets and electroactive flow switching.

Asymptotic theory for estimating the singular vectors and values of a partially-observed low rank matrix with noise

Matrix completion algorithms recover a low rank matrix from a small fraction of the entries, each entry contaminated with additive errors. In practice, the singular vectors and singular values of the low rank matrix play a pivotal role for statistical analyses and inferences. This paper proposes estimators of these quantities and studies their asymptotic behavior. Under the setting where the dimensions of the matrix increase to infinity and the probability of observing each entry is identical, Theorem 4.1 gives the rate of convergence for the estimated singular vectors; Theorem 4.3 gives a multivariate central limit theorem for the estimated singular values. Even though the estimators use only a partially observed matrix, they achieve the same rates of convergence as the fully observed case. These estimators combine to form a consistent estimator of the full low rank matrix that is computed with a non-iterative algorithm. In the cases studied in this paper, this estimator achieves the minimax lower bound in Koltchinskii et al. (2011). The numerical experiments corroborate our theoretical results.

Large Volatility Matrix Estimation with Factor-Based Diffusion Model for High-Frequency Financial data

Large volatility matrices are involved in many finance practices, and estimating large volatility matrices based on high-frequency financial data encounters the “curse of dimensionality”. It is a common approach to impose a sparsity assumption on the large volatility matrices to produce consistent volatility matrix estimators. However, due to the existence of common factors, assets are highly correlated with each other, and it is not reasonable to assume the volatility matrices are sparse in financial applications. This paper incorporates factor influence in the asset pricing model and investigates large volatility matrix estimation under the factor price model together with some sparsity assumption. We propose to model asset prices by assuming that asset prices are governed by common factors and that the assets with similar characteristics share the same association with the factors. We then impose some reasonable sparsity condition on the part of the volatility matrices after accounting for the factor contribution. Under the proposed factorbased model and sparsity assumption, we develop an estimation scheme called “blocking and regularizing”. Asymptotic properties of the proposed estimator are studied, and its finite sample performance is tested via extensive numerical studies to support theoretical results.

Surface Modification of Anisotropic Dielectric Elastomer Actuators with Uni- and Bi-axially Wrinkled Carbon Electrodes for Wettability Control

Interest in soft actuators for next-generation electronic devices, such as wearable electronics, haptic feedback systems, rollable flexible displays, and soft robotics, is rapidly growing. However, for more practical applications in diverse electronic devices, soft actuators require multiple functionalities including anisotropic actuation in three-dimensional space, active tactile feedback, and controllable wettability. Herein, we report anisotropic dielectric elastomer actuators with uni- and bi-axially wrinkled carbon black electrodes that are formed through pre-streching and relaxation processes. The wrinkled dielectric elastomer actuator (WDEA) that shows directional actuation under electric fields is used to control the anisotropic wettability. The morphology changes of the electrode surfaces under various electric stimuli are investigated by measuring the contact angles of water droplets, and the results show that the controllable wettability has a broad range from 141° to 161° along the wrinkle direction. The present study successfully demonstrates that the WDEA under electrically controlled inputs can be used to modulate the uni- or bi-axially wrinkled electrode surfaces with continous roughness levels. The controllable wrinkled structures can play an important role in creating adaptable water repellency and tunable anisotropic wettability.

Sparse PCA-based on high-dimensional Itô processes with measurement errors

This paper investigates the eigenspace estimation problem for the large integrated volatility matrix based on non-synchronized and noisy observations from a high-dimensional Ito process. We establish a minimax lower bound for the eigenspace estimation problem and propose sparse principal subspace estimation methods by using the multi-scale realized volatility matrix estimator or the pre-averaging realized volatility matrix estimator. We derive convergence rates of the proposed eigenspace estimators and show that the estimators can achieve the minimax lower bound, and thus are rate-optimal. The minimax lower bound can be established by Fano’s lemma with an appropriately constructed subclass that has independent but not identically distributed normal random variables with zero mean and heterogeneous variances.