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Description

Graphene is a one-atom-thick planar sheet of sp2-bonded carbon atoms that are densely packed in a honeycomb crystal lattice. The term Graphene was coined as a combination of graphite and the suffix -ene by Hanns-Peter Boehm, who described single-layer carbon foils in 1962. Graphene is most easily visualized as an atomic-scale chicken wire made of carbon atoms and their bonds. The crystalline or "flake" form of graphite consists of many graphene sheets stacked together.

Learn more about quantum dots from the many resources on this site, listed below. More information on Graphene can be found here.

All Categories (21-40 of 219)

  1. Mar 23 2021

    MNT-EC Spring Development Workshop: CVD Synthesis and Image Analysis

    This hands-on tutorial will introduce users to the Gr-ResQ ('graphene rescue') platform. Gr-ResQ is (i) an open, crowd-sourced database of recipes and characterization of graphene...

    https://nanohub.org/events/details/1965

  2. U-Net Convolutional Neural Networks for Image Segmentation: Application to Scanning Electron Microscopy Images of Graphene

    Online Presentations | 01 Feb 2021 | Contributor(s):: Aagam Rajeev Shah

    This tutorial introduces you to U-Net, a popular convolutional neural network commonly developed for image segmentation in biomedicine. Using an assembled data set, you will learn how to create and train a U-Net neural network, and apply it to segment scanning electron microscopy images of...

  3. Unsupervised Clustering Methods for Image Segmentation: Application to Scanning Electron Microscopy Images of Graphene

    Online Presentations | 27 Jan 2021 | Contributor(s):: Aagam Rajeev Shah

    This tutorial will introduce you to some basic image segmentation techniques driven by unsupervised machine learning techniques such as the Gaussian mixture model and k-means clustering. You will learn how to implement k-means clustering and template matching, and use these to segment a...

  4. SEM Image Segmentation Workshop

    Tools | 12 Jan 2021 | Contributor(s):: Aagam Rajeev Shah, Darren K Adams, Mitisha Surana, Ricardo Toro, Sameh H Tawfick, Elif Ertekin

    This tool introduces users to machine learning used to segment microscopy images

  5. "Turning Fruit Juice into Graphene Quantum Dots" Supplementary Lesson Plans: Going Atomic

    Series | 15 Nov 2020 | Contributor(s):: Rachel Altovar, Susan P Gentry

    Expanding on the pre-existing resource on nanoHUB: “Turning Fruit Juice into Graphene Quantum Dots” this resource expands on the concepts in the experimental guide to give a comprehensive overview of materials pertaining to concepts and ideas within the...

  6. MODULE 3 - Structures: "Turning Fruit Juice into Graphene Quantum Dots" Supplementary Lesson Plans: Going Atomic

    Teaching Materials | 15 Nov 2020 | Contributor(s):: Rachel Altovar, Susan P Gentry

    In MODULE 3- Structures in the "Turning Fruit Juice into Graphene Quantum Dots" Supplementary Lesson Plans, crystal structures and systems are investigated. This module relates back to graphene and how its structure relates back to its unique properties in comparison to other forms of...

  7. MODULE 1 - Graphene: "Turning Fruit Juice into Graphene Quantum Dots" Supplementary Lesson Plans: Going Atomic

    Teaching Materials | 13 Nov 2020 | Contributor(s):: Rachel Altovar, Susan P Gentry

    The first module in "Turning Fruit Juice into Graphene Quantum Dots" Supplementary Lesson Plans, explores the material, graphene, how it was discovered, and the unique properties that it has. The activity paired with this lesson plan re-creates the famous "sticky-tape"...

  8. MODULE 2 - Sizes: "Turning Fruit Juice into Graphene Quantum Dots" Supplementary Lesson Plans: Going Atomic

    Teaching Materials | 13 Nov 2020 | Contributor(s):: Rachel Altovar, Susan P Gentry

    The next installment of Turning Fruit Juice into Graphene Quantum Dots" Supplementary Lesson Plans delves into the concept of size and how materials and their properties may change at the macro-, micro-, and nanoscale. Activities include viewing images from a microscope to determine...

  9. Atomistic Green’s Functions: The Beauty of Self-energies

    Online Presentations | 28 Oct 2020 | Contributor(s):: Tillmann Christoph Kubis

    This presentation gives an introduction to NEGF. It will be explained how self-energies cause NEGF to fundamentally differ from most other quantum methods. Atomistic examples of phonon and impurity scattering self-energies agree quantitatively with experiments.

  10. Florida Ferroelectric Tunnel Junction Device Model

    09 Sep 2020 | Compact Models | Contributor(s):

    By Tong Wu1, Jing Guo1

    University of Florida

    A compact model of the Ferroelectric Tunnel Junctions (FTJs) device is constructed, using the Wentzel–Kramers–Brillouin (WKB) approximation for tunneling current calculation.

    https://nanohub.org/publications/375/?v=1

  11. Bandgap Manipulation of Armchair Graphene nanoribbon

    Papers | 01 Sep 2020 | Contributor(s):: Lance Fernandes

    Bandgap Manipulation is very important for various applications. Optical Devices need smaller Bandgap where as Diode's need larger Bandgap. Armchair graphene Nanoribbon (AGNR) has a special property where if the numbers of atoms are multiple of three or multiple of three plus one, they are...

  12. Alexis V Miranda

    Masters student

    https://nanohub.org/members/295321

  13. Carbon nanotubes and graphene nanoribbons

    Wiki

    This page provides links to various nanoHUB resources related to carbon nanotubes (CNT) and graphene nanoribbons (GNR). The CNTbands tool simulates CNT and GNR. This tutorial introduces various...

    https://nanohub.org/wiki/cntgnr

  14. HIMADRI PANDEY, Ph.D.

    https://scholar.google.com/citations?user=dnFxtFAAAAAJ&hl=en

    https://nanohub.org/members/292409

  15. Mechanical Exfoliation as a Route to Nanomanufacturing of 2D van der Waals Bonded

    Online Presentations | 11 May 2020 | Contributor(s):: Daryl Chrzan

    In this talk I present a mechanical exfoliation method able to reliably produce large patterned monolayer samples and place them with upon a substrate in desired locations. The method relies on the epitaxial strain imposed upon the layer to be exfoliated by the deposition of a thin metallic film.

  16. Synthesis of Graphene by Chemical Vapor Deposition Part II: Data Science + Graphene Synthesis

    Online Presentations | 29 Apr 2020 | Contributor(s):: Sameh H Tawfick

    Overall, these two lectures are meant to be a general introduction on the opportunities and challenges related to graphene synthesis.

  17. Synthesis of Graphene by Chemical Vapor Deposition Part I

    Online Presentations | 29 Apr 2020 | Contributor(s):: Sameh H Tawfick

    Overall, these two lectures are meant to be a general introduction on the opportunities and challenges related to graphene synthesis.

  18. Image Segmentation for Graphene Images

    Online Presentations | 29 Apr 2020 | Contributor(s):: Joshua A Schiller

    This lecture outlines the need for a fast, automated means for identifying regions of images corresponding to graphene. Simple methods, like color masking and template matching, are discussed initially. Unsupervised clustering methods are then introduced as potential improvements...

  19. Images of Nanotubes, Graphene, Buckyballs, etc.

    Downloads | 24 Apr 2020 | Contributor(s):: Marco Curreli

    Free images of nanotubes, graphene, buckyballs, etc.  

  20. Andreas Hemmetter

    Andreas Hemmetter received his B.Sc. degree in physics from TU Dresden (Dresden, Germany) in 2016, and his M.Sc. degree in metamaterials and nanophotonics from ITMO University (St. Petersburg,...

    https://nanohub.org/members/280091