An Educational Simulation for Understanding Atomic Force Microscopy Image Artifacts
- Conference
- Location
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Portland, Oregon
- Publication Date
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June 23, 2024
- Start Date
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June 23, 2024
- End Date
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July 12, 2024
- Conference Session
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MECH - Technical Session 9: Advanced Mechanical Engineering Topics
- Tagged Division
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Mechanical Engineering Division (MECH)
- Permanent URL
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https://peer.asee.org/46548
Paper Authors
Rachel Mok Massachusetts Institute of Technology
Rachel Mok is an instructor in the Department of Mechanical Engineering at MIT. She received her Ph.D. in Mechanical Engineering from MIT, specializing in the theory and simulation of bacterial dynamics. As a graduate student, she was a teaching assistant for 2.005, an undergraduate course on thermal-fluid engineering, for many semesters. Through this experience, she realized her passion for teaching. She currently develops online courses and education tools that demonstrate engineering principles.
Cong Li Massachusetts Institute of Technology
I am currently working as a systems engineer in the aerospace industry, I contributed to this project as an undergraduate researcher and helped create early versions of the simulation using Matlab
Benita Comeau Massachusetts Institute of Technology
Benita Comeau teaches a laboratory course on micro/nano engineering, in the Department of Mechanical Engineering at the Massachusetts Institute of Technology. She is a Chemical Engineer by degree, and received her BSE from the Univerisity of Michigan and PhD from the Georgia Institute of Technology.
Emily Welsh Massachusetts Institute of Technology
Ms. Welsh works as an educational technologist at MIT. Her work includes the development and running of MOOCs and the development of digital education tools. Prior to joining MIT, she worked in industry at an original equipment manufacturer.
Nicholas Xuanlai Fang University of Hong Kong
Professor Nick Fang recently moved to HKU to continue his passion for optical and acoustic research after nearly two decades of academic career in US. As an example of his public outreach effort, A 3D printing module has been successfully developed through the partnership with the NSF Center for Nanoscale Chemical-Electrical-Mechanical Manufacturing Systems, and engaged students and teachers from more than 10 high schools, showcased at the Illinois State Capitol Educational Fair and the St Louis Science Center. These innovative educational modules developed have received nation-wide attention of general public. His recognitions also include the ASME Chao and Trigger Young Manufacturing Engineer Award (2013); the ICO prize from the International Commission of Optics (2011); an invited participant of the Frontiers of Engineering Conference by National Academies in 2010; the NSF CAREER Award (2009) and MIT Technology Review Magazine’s 35 Young Innovators Award (2008).
John Liu
Massachusetts Institute of Technology
orcid.org/0000-0002-6085-0926
Dr. John Liu is the Principal Investigator of the MIT Learning Engineering and Practice (LEAP) Group, which applies design principles to solving challenges to better meet the increasing demand for STEM skills in tomorrow’s workforce. He is a Lecturer in MIT's Mechanical Engineering department and MITx Digital Learning Lab Scientist. He leads education and workforce development efforts for MIT's new initiative: Manufacturing@MIT. He was the Director of the Principles of Manufacturing MicroMasters program, an online certificate program that has now enrolled over 180,000 learners across the globe. Dr. Liu's work includes engineering education, mixed reality and haptic experiences, workforce solutions to address the nation-wide manufacturing skills need, open-ended assessments for scalable education settings, and instructional design theory for massively open online courses. He received Best Paper Awards at the American Society Engineering Education (ASEE) in 2020. Dr. Liu earned his B.S. in Applied Physics from Caltech and S.M. and Ph.D. in Mechanical Engineering from MIT, under an MIT-SUTD fellowship and NSF Graduate Research Fellowship.
Abstract
The atomic force microscope (AFM) is a fundamental imaging tool used to visualize minute features, often on the scale of fractions of a nanometer. This is achieved by scanning a tip over a surface and monitoring the motions of the tip in response to forces between the tip and surface. However, the AFM-generated image is not an exact replica of the real surface because tips are not infinitely thin and perfectly sharp. This can be confusing for students new to using the AFM, especially since the interaction between the AFM tip and the surface is imperceptible to the naked eye. It also underscores the risk of students perceiving the AFM as a black box, potentially impeding critical thinking about its fundamental principles and processes, which may lead to misinterpretation of AFM data. To address this learning gap and provide students with a more thorough understanding of AFM working principles, measurement inaccuracies, and the origins of image artifacts, we created and implemented an educational simulation. Students can use this simulation to explore diverse tip geometries on various surface topologies and observe the resulting images generated by the AFM. We have created activities and assessments that guide students to use the simulation to grasp key concepts related to tip-surface interaction and measurement accuracy. This module has now been released in both the undergraduate-level and graduate-level micro/nano-laboratory course (“Micro/Nano Engineering Laboratory”) at the Massachusetts Institute of Technology (MIT). We developed pre- and post-assessments to compare cognitive outcomes and learning experiences between simulation-based learning and traditional paper-based learning. Our user test with 36 students showed a strong preference for the simulation format over the paper format for learning about AFM image artifacts, with students valuing the simulation's interactive nature.
Mok, R., & Li, C., & Comeau, B., & Welsh, E., & Fang, N. X., & Liu, J. (2024, June), An Educational Simulation for Understanding Atomic Force Microscopy Image Artifacts Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/46548
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