Physicist Matthias Kling studies photons and the things science can do with ultrafast pulses of X-rays. These pulses last just attoseconds – a billionth of a billionth of a second, Kling says. He uses them to create slo-mo “movies” of electrons moving through materials like those used in batteries and solar cells. The gained knowledge could reshape fields like materials science, ultrafast and quantum computers, AI, and medical diagnostics, Kling tells host Russ Altman on this episode of Stanford Engineering’s The Future of Everything podcast.Have a question for Russ? Send it our way in writing or via voice memo, and it might be featured on an upcoming episode. Please introduce yourself, let us know where you're listening from, and share your quest. You can send questions to [email protected] Reference Links:SStanford Profile: Matthias KlingMatthias’ Lab: SLAC National Accelerator LaboratoryConnect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>> Twitter/X / Instagram / LinkedIn / FacebookChapters:(00:00:00) IntroductionRuss Altman introduces guest Matthias Kling, a professor of photon science and applied physics at Stanford University.(00:02:52) Ultrafast Electronics OverviewThe technologies enabling ultrafast photonics and electronic advancements.(00:05:32) Attosecond Science ApplicationsCapturing electron and molecular movements with attosecond pulses.(00:09:31) Photoelectric Effect InsightsAttosecond science’s impact on understanding the photoelectric effect and quantum mechanics.(00:13:27) Real-Time Molecular MeasurementsUsing light waves to capture images of molecules at room temperature.(00:19:32) Future of Ultrafast ElectronicsHow attosecond light pulses could revolutionize computing with petahertz speed.(00:23:28) Energy-Efficient Quantum ComputingPotential for room-temperature quantum computers using light wave electronics.(00:26:33) AI and Machine Learning in ScienceAI's role in optimizing research and data collection in ultrafast electronics.(00:28:51) Real-Time AI Data AnalysisMachine learning enables real-time analysis of massive experimental data.(00:32:15) Conclusion
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