Episode 89: Special Applications of Microscopy Technologies
Jun 10, 2024
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Professor Sergei Kalinin from the University of Tennessee-Knoxville talks about the new applications of electron microscopy, including atom-by-atom materials fabrication. They also discuss the integration of machine learning to enhance scientists' work with electron microscopy.
Electron microscopy evolves for atom-by-atom fabrication.
Machine learning enhances data analysis in material science.
Microscopy enables precise atom manipulation for material engineering.
Deep dives
Material Science and the Imperceptible Realm
Exploring a field that sheds light on hidden phenomena below perceptible levels, material scientists aim to understand and create imperceptible components crucial for future advancements. The quest is to uncover the mysteries of nature's hidden workings and delve into the recesses of material properties, potentially leading to groundbreaking discoveries.
Machine Learning in Material Science
The integration of machine learning tools in material science allows for advanced data analysis and automated decision-making. Scientists acknowledge the importance of sharing code and data for progress and emphasize the necessity of developing commercially viable products from research outcomes.
Advancements in Microscopy: Atom-by-Atom Assembly
Recent developments in microscopy, particularly in electron microscopy and machine learning, have enabled scientists to explore atom-by-atom assembly of matter. By deliberately manipulating atoms using electron beams, researchers can potentially engineer materials with unprecedented precision and control.
Challenges and Innovations in Atom Manipulation
Manipulating atoms at the atomic level using electron beams presents challenges such as beam precision and real-time control. Innovations like aberration correction and high-throughput imaging have enhanced the ability to observe and manipulate atoms with greater accuracy and efficiency.
Future Outlook: Machine Learning in Experimental Sciences
Machine learning applications in experimental sciences, especially in microscopy, offer promising avenues for accelerating research and discovery. By defining precise reward structures and integrating machine learning into experimental workflows, scientists can optimize data analysis, automate decision-making, and pave the way for impactful advancements in material science and beyond.
Electron microscopy is almost a century old, but it continues to play a role in exciting new developments that extend its use well beyond its original purpose. We sit down with Professor Sergei Kalinin from the University of Tennessee-Knoxville to discuss these exciting new applications of this older technology. Learn how electron microscopy, originally developed for imaging, is now used for atom-by-atom materials fabrication. We also explore how combining this technology with machine learning can streamline scientists' work.
This Materialism Podcast is sponsored by Materials Today, an Elsevier community dedicated to the creation and sharing of materials science knowledge and experience through their peer-reviewed journals, academic conferences, educational webinars, and more
This episode of the Materialism Podcast is sponsored by Cal Nano, leading experts in spark plasma sintering and cryomilling technologies. You can learn more about their work and services by visiting their website.
Thanks to Kolobyte and Alphabot for letting us use their music in the show!
If you have questions or feedback please send us emails at materialism.podcast@gmail.com or connect with us on social media: Instagram, Twitter.
Materialism Team: Taylor Sparks (co-host, co-creator), Andrew Falkowski (co-host, co-creator), Jared Duffy (production, marketing, and editing).
Keywords: Electron Microscopy
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