Tags: material properties

All Categories (1-20 of 160)

  1. Linear Regression Young's modulus

    24 Sep 2020 | | Contributor(s):: Michael N Sakano, Saaketh Desai, Alejandro Strachan

    Use linear regression to extract Young's modulus and yield stress from stress-strain data

  2. The Eötvös Paradox: The Enduring Significance of Eötvös' Most Famous Experiment

    23 Aug 2020 | | Contributor(s):: Ephraim Fischbach

    Here we summarize the key elements of this "Eötvös paradox," and suggest some possible paths to a resolution. Along the way we also discuss the close relationship between Eötvös and Einstein, and consider how their respective contributions may have been influenced...

  3. Aerogel (Aerogel)

    02 Jul 2020 | | Contributor(s):: Nano-Link Center for Nanotechnology Education, Carol E. Bouvier, James J Marti, Rodfal Alberto Rodriguez (editor), María Teresa Rivera (editor)

    Los aerogeles son las sustancias semisólidas conocidas más livianas que existen. Los aerogeles de sílice o dióxido de silicio (SiO2) tienen la misma estructura química que la arena de sílice o el vidrio de las ventanas, pero tienen diferencias...

  4. Mark Knackstedt

    Mark Knackstedtis Professor at the Department of Applied Mathematics at the Australian National University (ANU). He received his BSc degree at Columbia University and PhD in Chemical Engineering...


  5. Mystery Molecules: Identifying Materials with Nanoscale Characterization Tools

    18 Mar 2020 | | Contributor(s):: Maude Cuchiara, NNCI Nano

     In this lesson plan, students will be given several similar looking materials and asked to identify them by observing them at the macro and micro-scale. They will then be exposed to different analytical tools and describe how they can be used to explore materials at the nanoscale. ...

  6. Aerogels

    05 Feb 2020 | | Contributor(s):: Carol Bouvier, James J Marti, Deb Newberry, Nano-Link Center for Nanotechnology Education

    This module introduces aerogels, nicknamed “blue smoke.” Using exploratory activities this module introduces the nano structure of aerogels and the resulting extreme physical properties exhibited such as: thermal conductivity, transparency, and impact resistance. These properties...

  7. Introduction to Machine Learning in MSE: Predicting Bulk Modulus

    29 Jan 2020 | | Contributor(s):: Adrian Nat Gentry, Peilin Liao

    In this module, you will learn how to predict bulk modulus using machine learning.

  8. MSEML: Machine Learning for Materials Science Tool on nanoHUB

    27 Jan 2020 | | Contributor(s):: Saaketh Desai

    This talk is a hands-on demonstration using the nanoHUB tool Machine Learning for Materials Science: Part 1.

  9. Data Science and Machine Learning for Materials Science

    22 Jan 2020 | | Contributor(s):: Saaketh Desai

    This talk covers the fundamentals of machine learning and data science, focusing on material science applications. The talk is for a general audience, attempting to introduce basic concepts such as linear regression, supervised learning with neural networks including forward and back...

  10. Quantum Dots: Real-world Particles in a Box

    15 Jan 2020 | | Contributor(s):: Joyce Allen, NNCI Nano

    The purpose of this activity is to show that nanosize particles of a given substance often exhibit different properties and behavior than macro or micro size particles of the same material. The property studied in this activity is the absorption and reflection of light which is based on energy...

  11. 3 min Research Talk: Web-based Machine Learning Tool for Material Discovery and Property Prediction

    26 Sep 2019 | | Contributor(s):: Bryan Arciniega

    This model allows the end-user to increase their knowledge on a scarce data set by using a data-rich property set. We also investigate the effect of chemical representation and autoencoder type on property prediction and compound generation.

  12. 3 min Research Talk: Using Machine Learning for Materials Discovery and Property Prediction

    26 Sep 2019 | | Contributor(s):: Mackinzie S Farnell

    Machine Learning models present a transformative method of optimization and prediction in science and engineering research. In the chemical sciences, unsupervised deep learning models such as autoencoders have shown to be useful for property prediction and material...

  13. Chemical Autoencoder for Latent Space Enrichment

    19 Sep 2019 | | Contributor(s):: Bryan Arciniega, Mackinzie S Farnell, Nicolae C Iovanac, Brett Matthew Savoie

    Chemical Autencoder uses machine learning for property prediction

  14. In-situ Mechanical Tests in X-Ray Microscope on Addictive Manufacture Anisotropic Rock Sample

    14 Aug 2019 | | Contributor(s):: Liyang Jiang

    This work explores the effects of layers and mineral texture on fracture evolution. Three-point bending and unconfined compressive strength tests were performed on the 3D printed gypsum samples with a Deben CT5000 in-situ stage inside a Zeiss Xradia 510 X-ray microscope....

  15. ABACUS—Introduction to Semiconductor Devices

    When we hear the term semiconductor device, we may think first of the transistors in PCs or video game consoles, but transistors are the basic component in all of the electronic devices we use in...


  16. Aug 12 2019

    ICANM2019:7th International Conference & Exhibition on Advanced & Nano Materials

    The 7th International Conference & Exhibition on Advanced & Nano Materials (ICANM2019) will take place from August 12 to 14 in Montreal, Canada. This conference offers the unique...


  17. 2D material reflectance spectra

    10 Jul 2019 | | Contributor(s):: Vu Dang Nguyen, Yunsu Park, Darren K Adams (editor), Hayden Taylor

    Simulation of the reflectance spectra of 2D materials and image analysis for thickness identification

  18. Nanoscale NMR Studies of Topological Insulators, Crystalline Insulators and Dirac Semimetals

    22 May 2019 | | Contributor(s):: Louis Bouchard

    In this talk, we will review recent advances in experimental techniques to study the electronic and magnetic properties of such topological materials.  Among the novel techniques, we shall discuss radioactive ion beam spectroscopy and nuclear magnetic resonance.   Our group has...

  19. Effective Integration of NIST Reference Data, Reference Materials, and Informatics in Support of Science and Technology

    15 May 2019 | | Contributor(s):: Carlos A. Gonzalez

    In this talk, a general description of NIST’s SRM program will be provided, highlighting some examples related to environmental science, clinical diagnoses and petroleum chemistry. In addition, issues related to the effective integration of reference data with reference materials and...

  20. Mechanically-Driven Nano-Manufacturing of Atomically-Thin Origami and Kirigami Structures

    22 Apr 2019 | | Contributor(s):: Sungwoo Nam