Direct Top-Down Fabrication of Large-Area Graphene Arrays by an In Situ Etching Method

2015 ◽  
Vol 27 (28) ◽  
pp. 4195-4199 ◽  
Author(s):  
Dechao Geng ◽  
Huaping Wang ◽  
Yu Wan ◽  
Zhiping Xu ◽  
Birong Luo ◽  
...  
Keyword(s):  
Large Area ◽  
Top Down ◽  
Etching Method

scholarly journals Graphene Arrays: Direct Top-Down Fabrication of Large-Area Graphene Arrays by an In Situ Etching Method (Adv. Mater. 28/2015)

2015 ◽  
Vol 27 (28) ◽  
pp. 4194-4194
Author(s):  
Dechao Geng ◽  
Huaping Wang ◽  
Yu Wan ◽  
Zhiping Xu ◽  
Birong Luo ◽  
...  
Keyword(s):  
Large Area ◽  
Top Down ◽  
Etching Method

scholarly journals Chlorosulfuric acid-assisted production of functional 2D materials

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Mohsen Moazzami Gudarzi ◽  
Maryana Asaad ◽  
Boyang Mao ◽  
Gergo Pinter ◽  
Jianqiang Guo ◽  
...  
Keyword(s):  
Hexagonal Boron Nitride ◽  
2D Materials ◽  
Real Life ◽  
Large Area ◽  
Polar Solvents ◽  
Aspect Ratios ◽  
Two Dimensional Materials ◽  
Liquid Phase Exfoliation

AbstractThe use of two-dimensional materials in bulk functional applications requires the ability to fabricate defect-free 2D sheets with large aspect ratios. Despite huge research efforts, current bulk exfoliation methods require a compromise between the quality of the final flakes and their lateral size, restricting the effectiveness of the product. In this work, we describe an intercalation-assisted exfoliation route, which allows the production of high-quality graphene, hexagonal boron nitride, and molybdenum disulfide 2D sheets with average aspect ratios 30 times larger than that obtained via conventional liquid-phase exfoliation. The combination of chlorosulfuric acid intercalation with in situ pyrene sulfonate functionalisation produces a suspension of thin large-area flakes, which are stable in various polar solvents. The described method is simple and requires no special laboratory conditions. We demonstrate that these suspensions can be used for fabrication of laminates and coatings with electrical properties suitable for a number of real-life applications.

Large area multi-material-multi-photon 3D printing with fast in-situ material replacement

2021 ◽  
Author(s):  
Yuan Gao ◽  
Souha Toukabri ◽  
Ye Yu ◽  
Andreas Richter ◽  
Robert Kirchner
Keyword(s):  
3D Printing ◽  
Large Area

A Facile In Situ Hydrothermal Etching Method to CaTiO3/TiO2 Heterostructure for Efficient Photocatalytic N2 Reduction

2021 ◽  
Author(s):  
Xinchun Gao ◽  
Muyao Song ◽  
Dewu Sun ◽  
Renquan Guan ◽  
Hongju Zhai ◽  
...  
Keyword(s):  
Etching Method

The Formation of an Epitaxial-Graphene Cap Layer for Post-Implantation High Temperature Annealing of SiC and its In Situ Removal by Si-Vapor Etching

2011 ◽  
Vol 679-680 ◽  
pp. 777-780 ◽  
Author(s):  
Shoji Ushio ◽  
Ayumu Adachi ◽  
Kazuhiro Matsuda ◽  
Noboru Ohtani ◽  
Tadaaki Kaneko
Keyword(s):  
High Temperature ◽  
Graphene Layer ◽  
Treatment Method ◽  
Etch Depth ◽  
High Temperature Annealing ◽  
Large Area ◽  
Vapor Flux ◽  
Precise Control ◽  
Post Implantation

As a new graphene functionality applicable to post-implantation high temperature annealing of SiC, a method of in situ formation and removal of large area epitaxial few-layer graphene on 4H-SiC(0001) Si-face is proposed. It is demonstrated that the homogeneous graphene layer formed by Si sublimation can be preserved without the decomposition of the underlying SiC substrate even in the excess of 2000 oC in ultrahigh vacuum. It is due to the existence of the stable (6√3×6√3) buffer layer at the interface. To ensure this cap function, the homogeneity of the interface must be guaranteed. In order to do that, precise control of the initial SiC surface flatness is required. Si-vapor etching is a simple and versatile SiC surface pre/post- treatment method, where thermally decomposed SiC surface is compensated by a Si-vapor flux from Si solid source in the same semi-closed TaC container. While this Si-vapor etching allows precise control of SiC etch depth and surface step-terrace structures, it also provides a “decap” function to remove of the graphene layer. The surface properties after the each process were characterized by AFM and Raman spectroscopy.

scholarly journals Comparison of in situ and columnar aerosol spectral measurements during TexAQS-GoMACCS 2006: testing parameterizations for estimating aerosol fine mode properties

2009 ◽  
Vol 9 (4) ◽  
pp. 17465-17494
Author(s):  
D. B. Atkinson ◽  
P. Massoli ◽  
N. T. O'Neill ◽  
P. K. Quinn ◽  
S. Brooks ◽  
...  
Keyword(s):  
Gulf Of Mexico ◽  
Atmospheric Aerosols ◽  
Regional Scale ◽  
Atmospheric Composition ◽  
Light Extinction ◽  
Large Area ◽  
Light Extinction Coefficient ◽  
Spectral Models ◽  
Fine Mode

Abstract. During the 2006 Texas Air Quality Study and Gulf of Mexico Atmospheric Composition and Climate Study (TexAQS-GoMACCS 2006), the optical, chemical and microphysical properties of atmospheric aerosols were measured on multiple mobile platforms and at ground based stations. In situ measurements of the aerosol light extinction coefficient (σep) were performed by two multi-wavelength cavity ring-down (CRD) instruments, one located on board the NOAA R/V Ronald H. Brown (RHB) and the other located at the University of Houston, Moody Tower (UHMT). An AERONET sunphotometer was also located at the UHMT to measure the columnar aerosol optical depth (AOD). The σep data were used to extract the extinction Ångström exponent (åep), a measure of the wavelength dependence of σep. There was general agreement between the åep (and to a lesser degree σep measurements by the two spatially separated CRD instruments during multi-day periods, suggesting a regional scale consistency of the sampled aerosols. Two spectral models are applied to the σep and AOD data to extract the fine mode fraction of extinction (η) and the fine mode effective radius (Reff f). These two parameters are robust measures of the fine mode contribution to total extinction and the fine mode size distribution respectively. The results of the analysis are compared to Reff f values extracted using AERONET V2 retrievals and calculated from in situ particle size measurements on the RHB and at UHMT. During a time period when fine mode aerosols dominated the extinction over a large area extending from Houston/Galveston Bay and out into the Gulf of Mexico, the various methods for obtaining Reff f agree qualitatively (showing the same temporal trend) and quantitatively (pooled standard deviation=28 nm).

Large Area Supersonic Jet Epitaxy of AlN, GaN, and SiC on Silicon

1996 ◽  
Vol 449 ◽  
Author(s):  
L.J. Lauhon ◽  
S. A. Ustin ◽  
W. Ho
Keyword(s):  
Supersonic Jet ◽  
X Ray Diffraction ◽  
Large Area ◽  
Free Jets ◽  
X Ray ◽  
Force Microscopy ◽  
Atomic Force ◽  
Supersonic Jet Epitaxy ◽  
Carrier Gases

ABSTRACTAlN, GaN, and SiC thin films were grown on 100 mm diameter Si(111) and Si(100) substrates using Supersonic Jet Epitaxy (SJE). Precursor gases were seeded in lighter mass carrier gases and free jets were formed using novel slit-jet apertures. The jet design, combined with substrate rotation, allowed for a uniform flux distribution over a large area of a 100 mm wafer at growth pressures of 1–20 mTorr. Triethylaluminum, triethylgailium, and ammonia were used for nitride growth, while disilane, acetylene, and methylsilane were used for SiC growth. The films were characterized by in situ optical reflectivity, x-ray diffraction (XRD), atomic force microscopy (AFM), and spectroscopic ellipsometry (SE).

Street-Level Imagery and Deep Learning for Characterization of Exposed Buildings

2021 ◽  
Author(s):  
Patrick Aravena Pelizari ◽  
Christian Geiß ◽  
Elisabeth Schoepfer ◽  
Torsten Riedlinger ◽  
Paula Aguirre ◽  
...  
Keyword(s):  
Deep Learning ◽  
Structural Characteristics ◽  
High Accuracy ◽  
Risk Modeling ◽  
Sources Of Information ◽  
Large Area ◽  
Image Domain ◽  
Street Level ◽  
Building Characteristics

<p>Knowledge on the key structural characteristics of exposed buildings is crucial for accurate risk modeling with regard to natural hazards. In risk assessment this information is used to interlink exposed buildings with specific representative vulnerability models and is thus a prerequisite to implement sound risk models. The acquisition of such data by conventional building surveys is usually highly expensive in terms of labor, time, and money. Institutional data bases such as census or tax assessor data provide alternative sources of information. Such data, however, are often inappropriate, out-of-date, or not available. Today, the large-area availability of systematically collected street-level data due to global initiatives such as Google Street View, among others, offers new possibilities for the collection of <em>in-situ</em> data. At the same time, developments in machine learning and computer vision – in deep learning in particular – show high accuracy in solving perceptual tasks in the image domain. Thereon, we explore the potential of an automatized and thus efficient collection of vulnerability related building characteristics. To this end, we elaborated a workflow where the inference of building characteristics (e.g., the seismic building structural type, the material of the lateral load resisting system or the building height) from geotagged street-level imagery is tasked to a custom-trained Deep Convolutional Neural Network. The approach is applied and evaluated for the earthquake-prone Chilean capital Santiago de Chile. Experimental results are presented and show high accuracy in the derivation of addressed target variables. This emphasizes the potential of the proposed methodology to contribute to large-area collection of <em>in-situ</em> information on exposed buildings.</p>

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