In our group we study fundamental chemical processes at ultrafast timescales. In particular we are interested in how small structural changes, such as isomerism, influence chemical functionality. To do this we develop novel experimental methods based on ultrafast lasers, strong- field control of molecules and charged-particle velocity-map imaging. For current projects have a look at the Research page. Our group is part of the Spectroscopy of Cold Molecules department (headed by Prof. Bas van de Meerakker), and we are located in the Institute for Molecules and Materials at Radboud University in Nijmegen, the Netherlands
News
Our latest paper using coincidence double-VMI imaging to distinguish neutral and ionic multiphoton dissociation channels in molecular oxygen is out now in @JPhysChem A - well done Ana! https://doi.org/10.1021/acs.jpca.2c06707
Yerbolat joins the group as a PostDoc to work on your NWO-funded project on electron-driven reactions - Welcome Yerbolat!
Our latest paper comparing continuous and pulsed laser-based desorption (LIAD) methods is out now in Eur.Phys.J. D - well done Siwen! https://bit.ly/3cYM5tc
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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}
}
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}
}