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
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Recent Publications
Abma, Grite L.; Kleuskens, Dries; Wang, Siwen; Balster, Michiel; Roij, Andre; Janssen, Niek; Horke, Daniel A.
Single-Color Isomer-Resolved Spectroscopy Journal Article
In: The Journal of Physical Chemistry A, vol. 126, pp. 3811–3815, 2022.
@article{abmaSinglecolorIsomerresolvedSpectroscopy2022,
title = {Single-Color Isomer-Resolved Spectroscopy},
author = {Grite L. Abma and Dries Kleuskens and Siwen Wang and Michiel Balster and Andre Roij and Niek Janssen and Daniel A. Horke},
url = {https://doi.org/10.1021/acs.jpca.2c02277},
doi = {10.1021/acs.jpca.2c02277},
year = {2022},
date = {2022-06-01},
urldate = {2022-06-01},
journal = {The Journal of Physical Chemistry A},
volume = {126},
pages = {3811\textendash3815},
abstract = {Structural isomers, such as conformers or tautomers, are
of significant importance across chemistry and biology, as they can have
different functionalities. In gas-phase experiments using molecular
beams, formation of many different isomers cannot be prevented, and
their presence significantly complicates the assignment of spectral lines.
Current isomer-resolved spectroscopic techniques heavily rely on
theoretical calculations or make use of elaborate double-resonance
schemes. We show here that isomer-resolved spectroscopy can also be
performed using a single tunable laser. In particular, we demonstrate
single-color isomer-resolved spectroscopy by utilizing electrostatic
deflection to spatially separate the isomers. We show that for 3-
aminophenol we can spatially separate the syn and anti conformers and
use these pure samples to perform high-resolution REMPI spectroscopy, making the assignment of transitions to a particular isomer trivial, without any additional a priori information. This approach allows one to add isomer specificity to any molecular-beam-based experiment.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
of significant importance across chemistry and biology, as they can have
different functionalities. In gas-phase experiments using molecular
beams, formation of many different isomers cannot be prevented, and
their presence significantly complicates the assignment of spectral lines.
Current isomer-resolved spectroscopic techniques heavily rely on
theoretical calculations or make use of elaborate double-resonance
schemes. We show here that isomer-resolved spectroscopy can also be
performed using a single tunable laser. In particular, we demonstrate
single-color isomer-resolved spectroscopy by utilizing electrostatic
deflection to spatially separate the isomers. We show that for 3-
aminophenol we can spatially separate the syn and anti conformers and
use these pure samples to perform high-resolution REMPI spectroscopy, making the assignment of transitions to a particular isomer trivial, without any additional a priori information. This approach allows one to add isomer specificity to any molecular-beam-based experiment.
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}
}