Synthesis technique and electron beam damage study of nanometer-thin single-crystalline thymine.

Samples suitable for electron diffraction studies must satisfy certain characteristics such as having a thickness in the range of 10-100 nm. We report, to our knowledge, the first successful synthesis technique of nanometer-thin sheets of single-crystalline thymine suitable for electron diffraction and spectroscopy studies. This development provides a well-defined system to explore issues related to UV photochemistry of DNA and high intrinsic stability essential to maintaining integrity of genetic information. The crystals are grown using the evaporation technique, and the nanometer-thin sheets are obtained via microtoming. The sample is characterized via x-ray diffraction and is subsequently studied using electron diffraction via a transmission electron microscope. Thymine is found to be more radiation resistant than similar molecular moieties (e.g., carbamazepine) by a factor of 5. This raises interesting questions about the role of the fast relaxation processes of electron scattering-induced excited states, extending the concept of radiation hardening beyond photoexcited states. The high stability of thymine in particular opens the door for further studies of these ultrafast relaxation processes giving rise to the high stability of DNA to UV radiation.

Novel applications of generative adversarial networks (GANs) in the analysis of ultrafast electron diffraction (UED) images.

Inferring transient molecular structural dynamics from diffraction data is an ambiguous task that often requires different approximation methods. In this paper, we present an attempt to tackle this problem using machine learning. Although most recent applications of machine learning for the analysis of diffraction images apply only a single neural network to an experimental dataset and train it on the task of prediction, our approach utilizes an additional generator network trained on both synthetic and experimental data. Our network converts experimental data into idealized diffraction patterns from which information is extracted via a convolutional neural network trained on synthetic data only. We validate this approach on ultrafast electron diffraction data of bismuth samples undergoing thermalization upon excitation via 800 nm laser pulses. The network was able to predict transient temperatures with a deviation of less than 6% from analytically estimated values. Notably, this performance was achieved on a dataset of 408 images only. We believe that employing this network in experimental settings where high volumes of visual data are collected, such as beam lines, could provide insights into the structural dynamics of different samples.

Utilizing relativistic time dilation for time-resolved studies.

Time-resolved studies have so far relied on rapidly triggering a photo-induced dynamic in chemical or biological ions or molecules and subsequently probing them with a beam of fast moving photons or electrons that crosses the studied samples in a short period of time. Hence, the time resolution of the signal is mainly set by the pulse duration of the pump and probe pulses. In this paper, we propose a different approach to this problem that has the potential to consistently achieve orders of magnitude higher time resolutions than what is possible with laser technology or electron beam compression methods. Our proposed approach relies on accelerating the sample to a high speed to achieve relativistic time dilation. Probing the time-dilated sample would open up previously inaccessible time resolution domains.

Fixed-target serial oscillation crystallography at room temperature.

A fixed-target approach to high-throughput room-temperature serial synchrotron crystallography with oscillation is described. Patterned silicon chips with microwells provide high crystal-loading density with an extremely high hit rate. The microfocus, undulator-fed beamline at CHESS, which has compound refractive optics and a fast-framing detector, was built and optimized for this experiment. The high-throughput oscillation method described here collects 1-5° of data per crystal at room temperature with fast (10°â€…s-1) oscillation rates and translation times, giving a crystal-data collection rate of 2.5 Hz. Partial datasets collected by the oscillation method at a storage-ring source provide more complete data per crystal than still images, dramatically lowering the total number of crystals needed for a complete dataset suitable for structure solution and refinement - up to two orders of magnitude fewer being required. Thus, this method is particularly well suited to instances where crystal quantities are low. It is demonstrated, through comparison of first and last oscillation images of two systems, that dose and the effects of radiation damage can be minimized through fast rotation and low angular sweeps for each crystal.

Compression of high-density 0.16 pC electron bunches through high field gradients for ultrafast single shot electron diffraction: The Compact RF Gun.

We present an RF gun design for single shot ultrafast electron diffraction experiments that can produce sub-100 fs high-charge electron bunches in the 130 keV energy range. Our simulations show that our proposed half-cell RF cavity is capable of producing 137 keV, 27 fs rms (60 fs FWHM), 106 electron bunches with an rms spot size of 276 µm and a transverse coherence length of 2.0 nm. The required operation power is 9.2 kW, significantly lower than conventional rf cavity designs and a key design feature. This electron source further relies on high electric field gradients at the cathode to simultaneously accelerate and compress the electron bunch to open up new space-time resolution domains for atomically resolved dynamics.