TWO-DIMENSIONAL CRYSTALS ON SUBSTRATES: MORPHOLOGY AND CHEMICAL REACTIVITY

Two-dimensional crystals such as graphene and transition metal dichalcogenides have emerged as a new class of materials. They serve as rich playgrounds for two-dimensional physics but also have great potential for a wide range of applications due to their exceptional tunability via external influences such as electric fields, light, chemical adsorbates, defects, and stress. This dissertation aims to understand, as a fundamental step toward their application, the response of two-dimensional crystals to such external perturbations imposed by supporting substrates. First, the mechanical response of graphene supported on corrugated substrates is studied. I find that the structural evolution of graphene depends on the roughness of the substrate and the graphene thickness. On SiO2 substrates decorated with a low-density of SiO2 nanoparticles, adhesion dominates graphene elasticity and, hence, graphene conforms to the substrate. With increasing nanoparticle density, however, the elastic stretching energy is reduced by the formation of wrinkles. As the graphene membrane is made thicker, graphene becomes stiffer and delaminates from the substrate. Second, the effect of substrates on chemical reactivity of graphene is probed. Single-layer graphene on low charge-trap density boron nitride is not etched and shows little doping after oxygen treatment, in sharp contrast with oxidation under similar conditions of graphene on high charge-trap density SiO2 and mica. Furthermore, bilayer graphene shows reduced reactivity compared to single-layer graphene regardless of its substrate-induced roughness. Together the observations indicate that graphene's reactivity is predominantly controlled by charge- inhomogeneity-induced potential fluctuations rather than by surface roughness. Lastly, the oxidative reactivity of atomically thin molybdenum disulfide (MoS2) on SiO2 is studied. MoS2 is etched by oxygen treatment. However, unlike graphene on SiO2, the density of etch pits barely depends on MoS2 thickness, oxidation time, oxidation temperature, but varies significantly from sample to sample. The observations suggest that the oxidative etching of atomically thin MoS2 is initiated at native defect sites on the basal-plane surface rather than activated by substrate effects such as charged impurities and surface roughness. The findings provide insight into the mechanical and chemical properties of two-dimensional crystals and may have important implications for their applications

Semi-supervised learning on closed set lattices

We propose a new approach for semi-supervised learning using closed set lattices, which have been recently used for frequent pattern mining within the framework of the data analysis technique of Formal Concept Analysis (FCA). We present a learning algorithm, called SELF (SEmi-supervised Learning via FCA), which performs as a multiclass classifier and a label ranker for mixed-type data containing both discrete and continuous variables, while only few learning algorithms such as the decision tree-based classifier can directly handle mixed-type data. From both labeled and unlabeled data, SELF constructs a closed set lattice, which is a partially ordered set of data clusters with respect to subset inclusion, via FCA together with discretizing continuous variables, followed by learning classification rules through finding maximal clusters on the lattice. Moreover, it can weight each classification rule using the lattice, which gives a partial order of preference over class labels. We illustrate experimentally the competitive performance of SELF in classification and ranking compared to other learning algorithms using UCI datasets

Princess and the Pea at the nanoscale: Wrinkling and delamination of graphene on nanoparticles

Thin membranes exhibit complex responses to external forces or geometrical constraints. A familiar example is the wrinkling, exhibited by human skin, plant leaves, and fabrics, resulting from the relative ease of bending versus stretching. Here, we study the wrinkling of graphene, the thinnest and stiffest known membrane, deposited on a silica substrate decorated with silica nanoparticles. At small nanoparticle density monolayer graphene adheres to the substrate, detached only in small regions around the nanoparticles. With increasing nanoparticle density, we observe the formation of wrinkles which connect nanoparticles. Above a critical nanoparticle density, the wrinkles form a percolating network through the sample. As the graphene membrane is made thicker, global delamination from the substrate is observed. The observations can be well understood within a continuum elastic model and have important implications for strain-engineering the electronic properties of graphene.Comment: 11 pages, 8 figures. Accepted for publication in Physical Review

High-fidelity conformation of graphene to SiO2 topographic features

Strain engineering of graphene through interaction with a patterned substrate offers the possibility of tailoring its electronic properties, but will require detailed understanding of how graphene's morphology is determined by the underlying substrate. However, previous experimental reports have drawn conflicting conclusions about the structure of graphene on SiO2. Here we show that high-resolution non-contact atomic force microscopy of SiO2 reveals roughness at the few-nm length scale unresolved in previous measurements, and scanning tunneling microscopy of graphene on SiO2 shows it to be slightly smoother than the supporting SiO2 substrate. Quantitative analysis of the competition between bending rigidity of the graphene and adhesion to the substrate explains the observed roughness of monolayer graphene on SiO2 as extrinsic, and provides a natural, intuitive description in terms of highly conformal adhesion. The analysis indicates that graphene adopts the conformation of the underlying substrate down to the smallest features with nearly 99% fidelity.Comment: 13 pages, 3 figures plus supplemental informatio

Measuring the Complex Optical Conductivity of Graphene by Fabry-Pérot Reflectance Spectroscopy

Partial funding for Open Access provided by the UMD Libraries Open Access Publishing Fund.We have experimentally studied the dispersion of optical conductivity in few-layer graphene through reflection spectroscopy at visible wavelengths. A laser scanning microscope (LSM) with a supercontinuum laser source measured the frequency dependence of the reflectance of exfoliated graphene flakes, including monolayer, bilayer and trilayer graphene, loaded on a Si/SiO2 Fabry-Pérot resonator in the 545–700 nm range. The complex refractive index of few-layer graphene, n − ik, was extracted from the reflectance contrast to the bare substrate. It was found that each few-layer graphene possesses a unique dispersionless optical index. This feature indicates that the optical conductivity does not simply scale with the number of layers, and that inter-layer electrodynamics are significant at visible energies

Generation of three induced pluripotent stem cell lines from postmortem tissue derived following sudden death of a young patient with STXBP1 mutation

We established three iPSC lines from postmortem-cultured fibroblasts derived following the sudden unexpected death of an 8-year-old girl with Lennox-Gastaut syndrome, who turned out to have the R551H-mutant STXBP1 gene. These iPSC clones showed pluripotent characteristics while retaining the genotype and demonstrated trilineage differentiation capability, indicating their utility in disease-modeling studies, i.e., STXBP1-encephalopathy. This is the first report on the establishment of iPSCs from a sudden death child, suggesting the possible use of postmortem-iPSC technologies as an epoch-making approach for precise identification of the cause of sudden death

Cerebral hypoperfusion accelerates cerebral amyloid angiopathy and promotes cortical microinfarcts

Cortical microinfarcts (CMIs) observed in brains of patients with Alzheimer’s disease tend to be located close to vessels afflicted with cerebral amyloid angiopathy (CAA). CMIs in Alzheimer’s disease are preferentially distributed in the arterial borderzone, an area most vulnerable to hypoperfusion. However, the causal association between CAA and CMIs remains to be elucidated. This study consists of two parts: (1) an observational study using postmortem human brains (n = 31) to determine the association between CAA and CMIs, and (2) an experimental study to determine whether hypoperfusion worsens CAA and induces CMIs in a CAA mouse model. In postmortem human brains, the density of CMIs was 0.113/cm2 in mild, 0.584/cm2 in moderate, and 4.370/cm2 in severe CAA groups with a positive linear correlation (r = 0.6736, p < 0.0001). Multivariate analysis revealed that, among seven variables (age, disease, senile plaques, neurofibrillary tangles, CAA, atherosclerosis and white matter damage), only the severity of CAA was a significant multivariate predictor of CMIs (p = 0.0022). Consistent with the data from human brains, CAA model mice following chronic cerebral hypoperfusion due to bilateral common carotid artery stenosis induced with 0.18-mm diameter microcoils showed accelerated deposition of leptomeningeal amyloid β (Aβ) with a subset of them developing microinfarcts. In contrast, the CAA mice without hypoperfusion exhibited very few leptomeningeal Aβ depositions and no microinfarcts by 32 weeks of age. Following 12 weeks of hypoperfusion, cerebral blood flow decreased by 26% in CAA mice and by 15% in wild-type mice, suggesting impaired microvascular function due to perivascular Aβ accumulation after hypoperfusion. Our results suggest that cerebral hypoperfusion accelerates CAA, and thus promotes CMIs

Mining RNA Families with Structure Histograms

ABSTRACT In this paper we present the idea of mining RNA families with structure histograms. A structure histogram is a histogram employing structures of some type as attributes. As structures of RNA sequences we adopt their secondary structures which are not pseudo-knots. Unfortunately to obtain the histogram for an RNA sequence of its length l needs more than the (l/2)-th Catalan number time, but show that the value for every structure in the histogram is calculated in the time O(l 3 ). We also give some experimental results by applying structure histograms obtained from real RNA data to some mining methods and demonstrated the cases that structure histograms works effectively