2021 User Meeting Agenda

X-ray spectroscopy provides a window into the behavior of molecules and materials. As an element-specific probe, it directly accesses local electronic or chemical information. However, the interpretation of these measurements can be challenging. Also, the specific advantages of soft X-ray spectroscopy in providing increased surface or interface sensitivity may lack any corresponding spectral standards to facilitate interpretation. This defines an excellent opportunity for theory and simulation to propose models with atomic structural and dynamic detail that make sense of existing experiments or that can inspire future measurements. With direct access to electronic or chemical detail at relevant time scales, we can uncover molecular-scale mechanisms that define a wide range of functionality in applied materials science and chemistry. This talk will sample research in the space of X-ray spectral simulation and interpretation that leverages Berkeley Lab’s user facilities: the Molecular Foundry, the Advanced Light Source, and the National Energy Research Scientific Computing Center.

Visualizing bone’s complex hierarchical structure at the microscale provides insight into the mechanism that allows bone to resist fracture. With short acquisition and high-resolution imaging, dynamic synchrotron radiation micro-computed tomography (SRμT) is well suited for in situ mechanical testing to monitor 3D micro-crack evolution and to determine the stress/strain fields via DVC based on osteocyte lacunae displacement in real-time loading. However, the high levels of radiation exposure associated with synchrotron imaging compromises the mechanical and fracture properties in bone.

To address this issue, we developed a novel method combining SRμT at Beamline 8.3.2 of the ALS and machine learning to image in situ tissue deformation and crack growth. This method consists of acquiring low-quality scans at a reduced radiation dose (and reduced signal-to-noise ratio) reconstructed with convolutional neural network to recover the signal level and reduce the noise. This technique will improve our ability to assess and prevent fracture risk in population with fragility diseases, and offers new possibilities to lower the radiation dose in medical imaging.

We have developed an artificial-intelligence-based methodology that can be utilized to reliably analyze experimental results from extended x-ray absorption fine structure (EXAFS) measurements. This development will help to address the reproducibility problems that slow research progress and inhibit effective tech transfer and manufacturing innovation in these scientific disciplines. A machine learning approach was applied to the analysis of EXAFS spectroscopy measurements collected using a synchrotron radiation facility. Specifically, a genetic algorithm was developed for fitting of the measured spectra to extract the relevant structural parameters.

The current approach relies on a human analyst to suggest a potential set of chemical compounds in the form of feff.inp input files that may be present. The algorithm then attempts to determine the best structural paths from these compounds that are present in the experimental measurement. The automated analysis looks for the primary EXAFS path contributors from the potential compounds. It calculates a goodness of fit value that can be used to identify the chemical moieties present. The analysis package is called EXAFS Neo and is open source and written in Python. It requires the use of Larch and Feff for calculating the initial EXAFS paths. We have recently extended the code to make use of Feff8.5lite so it can calculate the paths needed for populating the analysis from within the EXAFS Neo package. Initial efforts have also begun to extend the code to the analysis of core level photoemission. The publication describing the analysis package and where to obtain the software can be downloaded at https://doi.org/10.1016/j.apsusc.2021.149059 or by contacting the speaker.

The rechargeable lithium battery is approaching 50 years in age. The structure of the electrode materials is critical to the performance of intercalation reactions, which depend on fast ion transport. All of today’s battery electrodes depend on defects for their ionic motion, including the common layered oxides and sulfides. Disorder of the cations is also critical to their performance. The role of structure, non-stoichiometry and defects will be discussed.