Electron donor–acceptor co-crystals are receiving increasing interest because of their many useful optoelectronic properties. While the steady-state properties of many different co-crystals have been characterized, very few studies have addressed how crystal morphology affects the dynamics of charge transfer (CT) exciton formation, migration, and decay, which are often critical to their performance in device structures. Here we show that co-crystallization of a pyrene (Pyr) electron donor with either N , N ′-bis(2,6-diisopropylphenyl)- or N , N ′-bis(3′-pentyl)-perylene-3,4:9,10-bis(dicarboximide) (diisoPDI or C 5 PDI) electron acceptors, respectively, yields mixed π-stacked Pyr–diisoPDI or Pyr–C 5 PDI donor–acceptor co-crystals. Femtosecond transient absorption microscopy is used to determine the CT exciton dynamics in these single crystals. Fitting the data to a one-dimensional charge transfer CT exciton diffusion model reveals a diffusion constant that is two orders of magnitude higher in the Pyr–diisoPDI co-crystal compared to the Pyr–C 5 PDI co-crystal. By correlating the co-crystal structures to their distinct excited-state dynamics, the effects of each mixed stacked structure on the exciton dynamics and the mechanisms of CT exciton diffusion are elucidated.
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A simple technique to classify diffraction data from dynamic proteins according to individual polymorphs
One often observes small but measurable differences in the diffraction data measured from different crystals of a single protein. These differences might reflect structural differences in the protein and may reveal the natural dynamism of the molecule in solution. Partitioning these mixed-state data into single-state clusters is a critical step that could extract information about the dynamic behavior of proteins from hundreds or thousands of single-crystal data sets. Mixed-state data can be obtained deliberately (through intentional perturbation) or inadvertently (while attempting to measure highly redundant single-crystal data). To the extent that different states adopt different molecular structures, one expects to observe differences in the crystals; each of the polystates will create a polymorph of the crystals. After mixed-state diffraction data have been measured, deliberately or inadvertently, the challenge is to sort the data into clusters that may represent relevant biological polystates. Here, this problem is addressed using a simple multi-factor clustering approach that classifies each data set using independent observables, thereby assigning each data set to the correct location in conformational space. This procedure is illustrated using two independent observables, unit-cell parameters and intensities, to cluster mixed-state data from chymotrypsinogen (ChTg) crystals. It is observed that the data populate an arc of the reaction trajectory as ChTg is converted into chymotrypsin.
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- Award ID(s):
- 1816314
- NSF-PAR ID:
- 10410675
- Date Published:
- Journal Name:
- Acta Crystallographica Section D Structural Biology
- Volume:
- 78
- Issue:
- 3
- ISSN:
- 2059-7983
- Page Range / eLocation ID:
- 268 to 277
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1107/S2059798321013425
