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DTSTART:20220727T090000Z
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TRANSP:OPAQUE
LOCATION:Auditorium and Online (Teams)
SUMMARY:ICFO | XINYAO LIU
CLASS:PUBLIC
DESCRIPTION:One of the significant challenges of modern science is to track
  and image chemical reactions as they occur. The molecular movies\, the pr
 ecise spatiotemporal tracking of changes in their molecular dynamics\, wil
 l provide a wealth of actionable insights into how nature works. Experimen
 tal techniques need to resolve the relevant molecular motions in atomic re
 solution\, which includes (10^(-10) m) spatial dimensions and few- to hund
 reds of femtoseconds (10^(-15) s) temporal resolution.\nLaser-induced elec
 tron diffraction (LIED)\, a laser-based electron diffraction technique\, i
 mages even singular molecular structures with combined sub-atomic picometr
 e and femto-to attosecond spatiotemporal resolution. Here\, a laser-driven
  attosecond electron wave packet scatters the parent&rsquo\;s ion after ph
 otoionization. The measured diffraction pattern of the electrons provides 
 a unique fingerprint of molecular structure. Taking snapshots of molecular
  dynamics via the LIED technique is proved to be a potent tool to understa
 nd the intertwining of molecules and how they react\, change\, break\, ben
 d\, etc.\nThis thesis is especially interested in exploiting advanced LIED
  imaging techniques to retrieve large complex molecular structures. So far
 \, LIED has successfully retrieved molecular information from small gas-ph
 ase molecules like oxygen (O2)\, nitrogen (N2)\, acetylene (C2H2)\, carbon
  disulfide (CS2)\, ammonia (NH3) and carbonyl sulfide (OCS). Nevertheless\
 , most biology interesting organic molecules typically exist as liquid or 
 solid at room temperature. In order to accomplish the final goal to extrac
 t these larger complex molecular structural information\, we need to overc
 ome two main challenges: delivering the liquid or solid samples as a gas-p
 hase jet with sufficient gas density in the experiment and developing a ne
 w retrieval algorithm to extract the geometrical information from the diff
 raction pattern. We tested one of the most simple liquid molecules - water
  H2O in the reaction chamber as a primary step. We traced the variation of
  H2O+ cation structure under the different electric fields. To solve the p
 roblem of unsatisfactory gas density\, we present a novel delivery system 
 utilizing Tesla valves that generates more than an order-of-magnitude dens
 er gaseous beam. Machine learning is well qualified to solve difficulties 
 with manifold degrees of freedom. We use convolutional neural networks (CN
 Ns) combined with LIED techniques to enable atomic-resolution imaging of t
 he complex chiral molecule Fenchone (C10H16O).\n&nbsp\;\nThesis Director: 
 Prof Dr. Jens Biegert
DTSTAMP:20260407T034648Z
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