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Communicating Science Course at UC Berkeley - Lawrence Hall of Science

On this website you will find detailed information about presenting Communicating Science, as a semester-long, college science education course, preparing participants to provide highly engaging science lessons for young students. The course syllabus is comprised of nine two-hour sessions on the following key educational topics

  1. Nature and Practices of Science
  2. Teaching and Learning
  3. Constructing Understanding
  4. Questioning Strategies
  5. Questions Lab
  6. Promoting Discussion
  7. Classroom Conversations
  8. Designing a Lesson
  9. Assessing for Learning

Educational Initiatives Award

In 2005, Communicating Science won UC Berkeley’s Educational Initiatives Award for its innovative design and is now widely recognized as a model for combining theory and practice to promote effective teaching strategies for improving science literacy. Communicating Science, and similar courses based on it, Communicating Ocean Sciences and Communicating Ocean Sciences to Informal Audiences are currently being taught at over 25 colleges and universities nationwide. Various combinations of sessions from the course have also been implemented in hundreds of teacher workshops and institutes, both nationally and internationally.

 

 

 

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nanoHUBs Outreach Group page on Communicating Science

This links to the page on Communicating Science from nanoHUB's Outreach Group.  You can find a course and video links here, as well as join the group to find like-minded nanoHUB members.

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Research Experience for Undergraduates: Science Communication Workshop

This NISENet Professional Development Guide is written by Carol Lynn Alpert,Museum of Science, Boston, and was produced with support by the National Science Foundation.

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This presentation gives an overview of the current functionality of NEMO5.

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Extinction, Scattering and Absorption efficiencies of single and multilayer nanoparticles

This tool calculates the extinction, scattering, and absorption efficiencies of single nanoparticle (1 layer),core-shell nanoparticle (2 layer) and nanomatryushka nanoparticle (3 layer) using MIE formulation.

Here is a demo video on YouTube.

 

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This simulation tools calculates the extintion, scattering and absorption efficiencies of single and multilayer nanoparticles (quantum dots.)  

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Introduction to Quantum Dots and Modeling Needs/ Requirements

This lecture provides a very high level overview of quantum dots. The main issues and questions that are addressed are:

  1. Length scale of quantum dots
  2. Definition of a quantum dot
  3. Quantum dot examples and Applications
  4. Single electronics
  5. Need for quantum dot modeling
  6. Model requirements – what are the physical effects that need to be included?
  7. Overview of some of the existing theories and models
  8. Tight binding approach

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Engineering at the nanometer scale: is it a new material or a new device?

At the nanometer scale the concepts of device and material meet and a new device is really a new material and vice versa. While atomistic device representation is novel to device physicists who typically deal in effective mass models, the concept of finite devices that are not infinitely periodic is novel in the semiconductor materials modeling community. NEMO 3-D bridges the gap and enables electronic structure simulations of quantum dots, quantum wells, nanowires, and impurities. Electronic structure simulations of systems 52 million atoms have been demonstrated.
 

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Quantum Dot Lab Demonstration: Pyramidal Qdots

This video shows the simulation and analysis of a pyramid-shaped quantum dot using Quantum Dot Lab. Several powerful analytic features of this tool are demonstrated, including the following:

  • visualization of specific 3D wavefunctions corresponding to discrete energy levels within the quantum dot
  • rotating the 3D volume of the quantum dot with wavefunction
  • applying cut planes along the x and y axes and moving the cut planes along those axes.
  • customizing the color scale used in volume rendering
  • scanning through the energy levels of the states inside the dot
  • viewing the transition and absorption curves
  • returning to the input section to change the size of the quantum dot and running a new simulation
  • comparing the absorption curves for different dot sizes
  • interactively zooming in on regions of the absorption plots

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Nano 101 Quantum Dots

Quantum Dots are man-made artificial atoms that confine electrons to a small space. As such they have atomic-like behavior and enable the study of quantum mechanical effects on a length scale that is around 100 times larger than the pure atomic scale. Quantum dots offer application opportunities in optical sensors, lasers, and advanced electronic devices for memory and logic.

This seminar starts with an overview of wavelike and particle like properties and motivates the existence of quantum mechanics. It closes the quantum mechanics point of view with these new fascinating artificial atoms.

 

 

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QDot Learning Materials

By completing the Quantum Dot Lab, users will be able to:

- Understand the concept of 3D confinement of charge carriers in a Q Dot.

- Understand the concept of light absorption in a Q Dot.

- Apply numerical techniques to calculate:

  1. The 3D wave function in a Q Dot
  2. The energy states in a Q Dot
  3. The optical absorption strength in a Q Dot.

- Design and simulate their own Q Dot structures.
 

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Photovoltaic Education Network

A collection of resources for the photovoltaic educator.

As solar cell manufacturing continues to grow at a record-setting pace, increasing demands are placed on universities to educate students on both the practical and theoretical aspects of photovoltaics. As a truly interdisciplinary field, young professionals must be fluent with the science, engineering, policy, and market dimensions of this technology, in the context of a growing renewable energy economy.

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Bayesian Calibration Tool

Given a model, input data for some paramaters and output data, calibrate unknown input parameters.

The result is not just a single value, but a probability distribution of the most likely values for the unknown parameters.

This tool should find great use in any laboratory course, especially those that require students to do some sort of statistical error analysis.

 

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Bayesian Calibration http://nanohub.org/tools/bayes  is a tool that allows you to include a range of inputs and outputs of an equation, and then calculate unknown parameters, not merely as a single-value but as a probability distribution.

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General Chemistry courses are taught at every college and university, and usually the goal is to cover the field of chemistry broadly over the course of an academic year. The topics included in such courses have evolved with time, but generally the emphasis is on the structural and…

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Study the transfer of energy between the vibrational modes of a carbon nanotube.

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The main goal of this learning module is to introduce students to the atomic-level processes responsible for plastic deformation in crystalline metals and help them develop a more intuitive understanding of how materials work at molecular scales. Image to the right shows plastic deformation of…

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DFT on Wikipedia

This is a link to Wikipedia's page on Density Functional Theory.

  1. DFT

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Life Beyond DFT

Computational Nanoscience, Lecture 26: Life Beyond DFT -- Computational Methods for Electron Correlations, Excitations, and Tunneling Transport

By Jeffrey B. Neaton

Lawrence Berkeley National Laboratory

This lecture provides a brief introduction to "beyond DFT" methods for studying excited state properties, optical properties, and transport properties, how the GW approximation to the self-energy corrects the quasiparticle excitations energies predicted by Kohn-Sham DFT;  the Bethe-Salpeter Equation for optical properties; and an example demonstrating the use of the Landauer formalism for exploring transport properties.

  1. DFT

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Ale Strachan EAFIT presentation

Published on Apr 4, 2014

En una colaboración entre la Escuela de Ingeniería de EAFIT, el Centro de Computación Científica Apolo y Proyecto 50, recibimos a Alejandro Strachan, profesor asociado de la Universidad de Purdue, quien explicó la utilidad de las simulaciones en procesos de enseñanza y aprendizaje, así como su impacto en la ingeniería.

Esta metodología explora los múltiples recursos que brinda "nanoHUB", una página web orientada a la creación de simulaciones para la investigación.

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Tanya Faltens onto Materials in Spanish

The Effect of Doping on Semiconductors

In this simulation, users can select the temperature and the concentration of dopant, both donors and acceptors, that can be added to silicon. Two diagrams are generated. One is a schematic of an energy band diagram that shows the Fermi energy as well as a representation of the concentrations of electrons and holes in the material using red and blue circles. The other shows the concentrations of electrons and holes as a function of temperature as a line plot, in a classic Arrhenius plot representation. The intrinsic carrier concentration and Fermi energy are also shown, for reference.

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Tanya Faltens onto MNT nanoHUB Gems

DDSCAT: Discrete Dipole Approximation

DDSCAT allows for the computation of scattering properties with arbitrary shapes and geometries, and provide graphs of light extinction, absorption, and scattering properties. Using software like DDSCAT, scientists can determine properties of everything from space dust to red blood cells (RBCs).

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S4: Stanford Stratified Structure Solver

This simulation tool calculates reflection and transmission spectra for a range of materials and geometries, from single layers to thin films on a substrate to multi-layered materials and even photonic structures made of patterned layers. S4 can be used to predict the reflections of an SiO2 layer on a silicon wafer, a soap bubble, an anti-reflective coating, and more. S4 is a powerful and versatile tool.

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S4: Stanford Stratified Structure Solver

This simulation tool calculates reflection and transmission spectra for a range of materials and geometries, from single layers to thin films on a substrate to multi-layered materials and even photonic structures made of patterned layers. S4 can be used to predict the reflections of an SiO2 layer on a silicon wafer, a soap bubble, an anti-reflective coating, and more. S4 is a powerful and versatile tool.

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