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The Learning Studio is a real-world marketplace for student-developed simulation tools and learning materials. In this vertically integrated model of student engagement, students begin to create and contribute simulation tools and associated learning content starting in their first year of engineering at Purdue. We actively engage students with a nanoHUB design project in their first year engineering course, giving them a real-life engineering problem that requires them to create a MATLAB-based simulation that will teach their peers about nanotechnology applications that have relevance to various fields of engineering, while teaching some of the big ideas in nano.
After this initial exposure to modeling and simulation with nanoHUB, selected students have the opportunity to serve in a year-long research apprenticeship with the nanoHUB Education Team to bring their tools and materials to production quality.
nanoHUB also engages students from around the country in a 10- week Summer Undergraduate Research Fellowships (SURF) program. Students work for an NCN Faculty member and are paired with an NCN graduate student mentor who guides them through the process, and facilitates their introduction to research.
Our goal through this effort is to increase awareness of models and simulations through the integrative area of nanotechnology, and create experiences that lead towards more advanced work and graduate level studies.
The NCN Education Research team has developed some materials for a course in Purdue’s First-Year Engineering Program. Course content can be seen at: First Year Engineering.
Summary of Categories
Impact on nanoHUB users
nanoHUB.org is a collaborative community for researchers, educators, and learners. This group is interested in sharing tools and educational materials created by undergraduate students for their peers, or for high school and middle school students. This group is also interested in creating a community for students interested in developing simulations, and faculty mentors who would like to start a similar program.
Tools Developed by NCN Undergraduate Students
By Chen Shang, Sankarsh Ramadas, Derrick Kearney, Tanya Faltens, and Krishna Madhavan.
This instructional simulation tool displays drain current as a function of source-drain voltage for different values of gate voltage, gate dimensions, substrate material, and oxide material in an n-type MOSFET.
By Ogaga Daniel Odele, Hanjun Xian, and Krishna Madhavan
This educational tool focuses on passive filter circuit concepts and consists of four basic types of filters: Low Pass filters (allow passage of low frequencies), High Pass filters (allow passage of high frequencies), Band Pass filters (allow passage only to a certain band of frequencies) and Band Reject filters(rejects a certain band of frequencies). The filter circuits are also sub divided into 1st order (consist of only one storage element-an inductor or capacitor), 2nd order (consist of 2 storage elements), 3rd order (consist of 3 storage elements). The user can choose between a capacitive or inductive model within the 1st order circuits. Graphical representations are provided for each type of filter. The magnitude and phase of the transfer function are provided along with a circuit diagram. The output consists of the Magnitude and Phase plots of the transfer function. As a result this user-friendly tool will allow students or other users to alter values of components within a circuit and visually see how the output changes. This tool will enhance their learning experience because of the presence of diagrams and simulations.
By Ogaga Daniel Odele, Hanjun Xian, Xue Yuan Wong, Francesca Polo, and Krishna Madhavan.
Function Discovery Tool allows users to learn how to identify mathematical functions from plots of data as well as understand linearization of functions. This tool provides users with a platform to generate plots for the following mathematical functions:
From plotting graphs of the above functions, users will be able to visualize the behaviors of these curves, grasp the differences between the plots of each function, and learn to linearize these functions.
By Ogaga Daniel Odele, Francesca Polo, Hanjun Xian, andKrishna Madhavan.
This tool will allow users to simulate the Normal Distribution Curve by changing the mean and standard deviation values. Users will see how these changes affect the height and width of the Normal Distribution Curve.
Tools Developed by Teams that Include NCN Undergraduate Students
By Je-Hyeong Bahk, Robert Benjamin Post, Kevin Margatan, Zhixi Bian, and Ali Shakouri
This simulation tool allows users to calculate various thermoelectric properties such as Seebeck coefficient, electrical conductivity, and electronic thermal conductivity for any semiconductor materials with band structures modeled using the nonparabolic dispersion relation. The linearized Boltzmann transport equation under the relaxation time approximation is used for the calculations. Maximum two conduction bands and two valence bands can be included in the band structure, and temperature- and composition- dependent band parameters can be taken into account. Various scattering mechanism such as the acoustic phonon deformation scattering, ionized impurity scattering, polar optical phonon scatterings and others can be included for the calculation of realistic energy-dependent scattering time. Simpler scattering models with constant scattering time or constant mean free path are also possible as a scattering option.
By Kaz Yazawa, Kevin Margatan, Je-Hyeong Bahk, and Ali Shakouri Simulate cost and efficiency trade-off of a thermoelectric device as a function of material properties and heat transfer coefficients. In this simulation tool, a thermoelectric power generator is optimized together with the heat source and heat sinks for maximum power output and system cost. Full electrical and thermal co-optimization is performed with given material properties, and dimensions to find the optimal thickness of the thermoelectric elements.
By Je-Hyeong Bahk, Megan Youngs, Zach Schaffter, Kazuaki Yazawa, and Ali Shakouri
This tool simulates both micro-scale thin-film thermoelectric devices and large-scale multi-element thermoelectric modules for cooling and power generation.
By Samiran Ganguly, Deepanjan Datta, Chen Shang, Sankarsh Ramadas, Sayeef Salahuddin, and Supriyo Datta
Calculate Resistance, Tunneling Magneto Resistance, Spin Torques, and Switching characteristics of a Magnetic Tunnel Junction. The Magnetic Tunnel Junction (MTJ) is a spintronic device that is gaining attention in hybrid- and post-silicon memory and logic circuit designs. We present a Non-Equilibrium Green’s Function (NEGF) based quantum transport simulator that calculates critical transport properties of an MTJ viz. Parallel and Anti-Parallel Resistances, Tunneling Magneto-Resistance (TMR) and Spin Transfer Torque (STT, both in-plane and out of the plane), using material parameters and geometric dimensions of an MTJ device as inputs. The underlying model is the first model to quantitatively benchmark multiple experimental data and was published in a peer reviewed journal.
Posters and Presentations of NCN Undergraduate Research
Stanford Stratified Structure Solver (S4) Simulation tool
Contributors: Chang Liu, Ogaga Daniel Odele, Xufeng Wang, Peter Bermel
Crystal Viewer Simulation Tool
Contributors: Osiris Vincent Ntarugera, Zach Schaffter, Kevin Margatan, Tanya Faltens, James Fonseca, Michael Povolotskyi, Gerhard Klimeck
Electrical Thin Double Layer Simulation and Micro-Electrochemical Supercapacitor Cooling
Contributors: Kaitlyn Fisher, Guoping Xiong, Timothy S Fisher
NanoHub Submit Scheme
Contributors: members/82413 Alejandro Gomez, Alejandro Strachan, Benjamin Haley, Steven Clark
Designing Meaningful MD Simulations: The Lithiation of Silicon
Contributors: Maria C Rincon, Hojin Kim, David Guzman, Alejandro Strachan
Crystalline Cellulose – Atomistic Modeling Toolkit
Contributors: Mateo Gómez Zuluaga, Fernando Luis Dri, Robert J. Moon, Pablo Daniel Zavattieri
Thermophotovoltaic Efficiency Simulation
Contributors: Qingshuang Chen, Roman Shugayev, Peter Bermel
Kinetic Monte Carlo Simulations
Contributors: Jingyuan Liang, R. Edwin García, Ding-Wen Chung
Next Generation Crystal Viewer
Contributors: Zach Schaffter, Gerhard Klimeck, Osiris Vincent Ntarugera, Kevin Margatan
Assessing the MVS Model for Nanotransistors
Contributors: Siyang Liu, Xingshu Sun, Mark Lundstrom
Finite-Difference Time-Domain Simulation of Photovoltaic Structures using a Graphical User Interface for MEEP
Contributors: Xin Tze Tee, Haejun Chung, Peter Bermel