News
Saskia joins the group for her MSc internship, working on our photoelectron-photoion coincidence spectrometer. Welcome!
Jantijn joins the group for his BSc internship, working on imaging photoelectron circular dichroism. Welcome!
Grite is off to the UK for beamtime at the HHG Artmis facility @CLF_STFC - good luck!
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Recent Publications
Awel, Salah; Lavin-Varela, Sebastian; Roth, Nils; Horke, Daniel A.; Rode, Andrei V.; Kirian, Richard A.; Küpper, Jochen; Chapman, Henry N.
Optical Funnel to Guide and Focus Virus Particles for X-Ray Diffractive Imaging Journal Article
In: Phys. Rev. Applied, vol. 17, no. 4, pp. 044044, 2022.
@article{PhysRevApplied.17.044044,
title = {Optical Funnel to Guide and Focus Virus Particles for X-Ray Diffractive Imaging},
author = {Salah Awel and Sebastian Lavin-Varela and Nils Roth and Daniel A. Horke and Andrei V. Rode and Richard A. Kirian and Jochen K\"{u}pper and Henry N. Chapman},
url = {https://link.aps.org/doi/10.1103/PhysRevApplied.17.044044},
doi = {10.1103/PhysRevApplied.17.044044},
year = {2022},
date = {2022-04-01},
journal = {Phys. Rev. Applied},
volume = {17},
number = {4},
pages = {044044},
publisher = {American Physical Society},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Zhuang, Yulong; Awel, Salah; Barty, Anton; Bean, Richard; Bielecki, Johan; Bergemann, Martin; Daurer, Benedikt J.; Ekeberg, Tomas; Estillore, Armando D.; Fangohr, Hans; Giewekemeyer, Klaus; Hunter, Mark S.; Karnevskiy, Mikhail; Kirian, Richard A.; Kirkwood, Henry; Kim, Yoonhee; Koliyadu, Jayanath; Lange, Holger; Letrun, Romain; Lübke, Jannik; Mall, Abhishek; Michelat, Thomas; Morgan, Andrew J.; Roth, Nils; Samanta, Amit K.; Sato, Tokushi; Shen, Zhou; Sikorski, Marcin; Schulz, Florian; Spence, John C. H.; Vagovic, Patrik; Wollweber, Tamme; Worbs, Lena; Xavier, P. Lourdu; Yefanov, Oleksandr; Maia, Filipe R. N. C.; Horke, Daniel A.; Küpper, Jochen; Loh, N. Duane; Mancuso, Adrian P.; Chapman, Henry N.; Ayyer, Kartik
Unsupervised Learning Approaches to Characterizing Heterogeneous Samples Using X-ray Single-Particle Imaging Journal Article
In: IUCrJ, vol. 9, no. 2, pp. 204–214, 2022, ISSN: 2052-2525.
@article{Zhuang:IUCrJ9:204,
title = {Unsupervised Learning Approaches to Characterizing Heterogeneous Samples Using X-ray Single-Particle Imaging},
author = {Yulong Zhuang and Salah Awel and Anton Barty and Richard Bean and Johan Bielecki and Martin Bergemann and Benedikt J. Daurer and Tomas Ekeberg and Armando D. Estillore and Hans Fangohr and Klaus Giewekemeyer and Mark S. Hunter and Mikhail Karnevskiy and Richard A. Kirian and Henry Kirkwood and Yoonhee Kim and Jayanath Koliyadu and Holger Lange and Romain Letrun and Jannik L\"{u}bke and Abhishek Mall and Thomas Michelat and Andrew J. Morgan and Nils Roth and Amit K. Samanta and Tokushi Sato and Zhou Shen and Marcin Sikorski and Florian Schulz and John C. H. Spence and Patrik Vagovic and Tamme Wollweber and Lena Worbs and P. Lourdu Xavier and Oleksandr Yefanov and Filipe R. N. C. Maia and Daniel A. Horke and Jochen K\"{u}pper and N. Duane Loh and Adrian P. Mancuso and Henry N. Chapman and Kartik Ayyer},
url = {https://scripts.iucr.org/cgi-bin/paper?S2052252521012707},
doi = {10.1107/S2052252521012707},
issn = {2052-2525},
year = {2022},
date = {2022-03-01},
urldate = {2022-03-14},
journal = {IUCrJ},
volume = {9},
number = {2},
pages = {204--214},
abstract = {One of the outstanding analytical problems in X-ray single-particle imaging (SPI) is the classification of structural heterogeneity, which is especially difficult given the low signal-to-noise ratios of individual patterns and the fact that even identical objects can yield patterns that vary greatly when orientation is taken into consideration. Proposed here are two methods which explicitly account for this orientation-induced variation and can robustly determine the structural landscape of a sample ensemble. The first, termed common-line principal component analysis (PCA), provides a rough classification which is essentially parameter free and can be run automatically on any SPI dataset. The second method, utilizing variation auto-encoders (VAEs), can generate 3D structures of the objects at any point in the structural landscape. Both these methods are implemented in combination with the noise-tolerant expand\textendash maximize\textendash compress ( EMC ) algorithm and its utility is demonstrated by applying it to an experimental dataset from gold nanoparticles with only a few thousand photons per pattern. Both discrete structural classes and continuous deformations are recovered. These developments diverge from previous approaches of extracting reproducible subsets of patterns from a dataset and open up the possibility of moving beyond the study of homogeneous sample sets to addressing open questions on topics such as nanocrystal growth and dynamics, as well as phase transitions which have not been externally triggered.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
One of the outstanding analytical problems in X-ray single-particle imaging (SPI) is the classification of structural heterogeneity, which is especially difficult given the low signal-to-noise ratios of individual patterns and the fact that even identical objects can yield patterns that vary greatly when orientation is taken into consideration. Proposed here are two methods which explicitly account for this orientation-induced variation and can robustly determine the structural landscape of a sample ensemble. The first, termed common-line principal component analysis (PCA), provides a rough classification which is essentially parameter free and can be run automatically on any SPI dataset. The second method, utilizing variation auto-encoders (VAEs), can generate 3D structures of the objects at any point in the structural landscape. Both these methods are implemented in combination with the noise-tolerant expand– maximize– compress ( EMC ) algorithm and its utility is demonstrated by applying it to an experimental dataset from gold nanoparticles with only a few thousand photons per pattern. Both discrete structural classes and continuous deformations are recovered. These developments diverge from previous approaches of extracting reproducible subsets of patterns from a dataset and open up the possibility of moving beyond the study of homogeneous sample sets to addressing open questions on topics such as nanocrystal growth and dynamics, as well as phase transitions which have not been externally triggered.
Lübke, Jannik; Roth, Nils; Worbs, Lena; Horke, Daniel A.; Estillore, Armando D.; Samanta, Amit K.; Küpper, Jochen
Charge-State Distribution of Aerosolized Nanoparticles Journal Article
In: J. Phys. Chem. C, vol. 125, no. 46, pp. 25794–25798, 2021, ISSN: 1932-7447, 1932-7455.
@article{Lubke:J.Phys.Chem.C125:25794,
title = {Charge-State Distribution of Aerosolized Nanoparticles},
author = {Jannik L\"{u}bke and Nils Roth and Lena Worbs and Daniel A. Horke and Armando D. Estillore and Amit K. Samanta and Jochen K\"{u}pper},
url = {https://pubs.acs.org/doi/10.1021/acs.jpcc.1c06912},
doi = {10.1021/acs.jpcc.1c06912},
issn = {1932-7447, 1932-7455},
year = {2021},
date = {2021-11-01},
urldate = {2022-01-03},
journal = {J. Phys. Chem. C},
volume = {125},
number = {46},
pages = {25794--25798},
abstract = {In single-particle imaging experiments, beams of individual nanoparticles are exposed to intense pulses of X-rays from free-electron lasers to record diffraction patterns of single, isolated molecules. The reconstruction for structure determination relies on signals from many identical particles. Therefore, well-defined-sample delivery conditions are desired in order to achieve sample uniformity, including avoidance of charge polydispersity. We have observed charging of 220 nm polystyrene particles in an aerosol beam created by a gas-dynamic virtual nozzle focusing technique, without intentional charging of the nanoparticles. Here, we present a deflection method for detecting and characterizing the charge states of a beam of aerosolized nanoparticles. Our analysis of the observed charge-state distribution using optical light-sheet localization microscopy and quantitative particle trajectory simulations is consistent with previous descriptions of skewed charging probabilities of triboelectrically charged nanoparticles.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In single-particle imaging experiments, beams of individual nanoparticles are exposed to intense pulses of X-rays from free-electron lasers to record diffraction patterns of single, isolated molecules. The reconstruction for structure determination relies on signals from many identical particles. Therefore, well-defined-sample delivery conditions are desired in order to achieve sample uniformity, including avoidance of charge polydispersity. We have observed charging of 220 nm polystyrene particles in an aerosol beam created by a gas-dynamic virtual nozzle focusing technique, without intentional charging of the nanoparticles. Here, we present a deflection method for detecting and characterizing the charge states of a beam of aerosolized nanoparticles. Our analysis of the observed charge-state distribution using optical light-sheet localization microscopy and quantitative particle trajectory simulations is consistent with previous descriptions of skewed charging probabilities of triboelectrically charged nanoparticles.