Philipp Hönicke

An operando, chemistry-resolved approach is presented for the quantitative analysis of sodium transport and speciation in hard carbon anodes during electrochemical cycling. By combining Na K-edge X-ray absorption spectroscopy with reference-free X-ray fluorescence analysis, absolute, SI-traceable sodium inventories and species-resolved sodium concentrations are determined directly under operando conditions without calibration standards. Reference spectra of relevant sodium compounds were acquired and used for spectral deconvolution, enabling quantitative determination of Na-containing interphase and conversion products. Focusing on the first galvanostatic cycle, the methodology resolves distinct sodiation and desodiation regimes characterized by different sodium uptake and release rates. The operando measurements reveal the evolution of individual sodium species and distinguish reversible sodium storage from irreversible sodium immobilization within the electrode. By directly linking electrochemical performance to localized sodium inventories and chemical speciation, the approach establishes a quantitative operando framework for mechanistic studies, material optimization, and future sodium-ion battery development.

This study presents a comprehensive computational framework for reproducing the full X‐ray absorption fine structure (XAFS) through quantum‐chemical simulations. The near‐edge region is accurately captured using an efficient implementation of time‐dependent density‐functional perturbation theory applied to core excitations, while ab initio molecular dynamics provides essential sampling of core‐excitation energies and interatomic distance distributions for interpreting extended X‐ray absorption fine structure (EXAFS) features. Owing to the efficiency of the approach, the total spectrum can be decomposed into contributions from bulk, defective, and surface environments, which commonly coexist in experimental systems. The methodology is demonstrated for sodium at the Na K‐edge in NaCl, where the predicted spectra show good agreement with experimental measurements on thin‐film samples. This strategy offers a practical route to generating chemically specific XAFS cross‐section data for elements and species that remain challenging to characterize experimentally, thereby enabling deeper insights into materials of technological importance.

The grazing incidence – and grazing exit X-ray fluorescence techniques and their theoretical background are described with examples and a review of recent developments and applications is provided in this review.

The characterization of electrochemical processes occurring in a battery during cycling contributes to a better understanding of the mechanisms involved. Furthermore, the combination of different operando characterization techniques can provide more complete information for their interpretation. This multimodal characterization includes the combination of electrochemical measurements like Electrochemical Impedance Spectroscopy (EIS) and other spectroscopy and imaging techniques. In operando EIS, the continuous change of the battery state during charge or discharge induces drifted EIS signals and requires adequate processing for the validity of the measurement data. In our strategy, this treatment of operando EIS data consisted of drift corrections, adapted regularization of the Distribution of Relaxation Times (DRT) and validation analysis. Thus, the processed EIS data could be considered as valid and equivalent to the instantaneous data without drift.

As nanostructures in the semiconductor industry become smaller and more complex, non-destructive characterization methods capable of measuring buried domains become crucial. Grazing emission x-ray fluorescence (GEXRF) spectroscopy is a measurement technique capable of resolving nanometer-sized features of buried nanostructures while providing information about the sample’s elemental distribution. In this work, a study was conducted to realistically assess the uncertainties of this method, considering correlations between geometric parameters. Furthermore, we showed strategies to effectively reduce the measurement time in GEXRF experiments by applying state-of-the-art single photon evaluation and machine learning denoising techniques for two-dimensional detectors. The study was performed on two different sample positions on a HfO₂/TiO₂ nanograting, where the GEXRF method was able to resolve geometric differences between them. Based on a finite element method model of the nanograting, the expected fluorescence intensities can be simulated, from which the nanostructure’s geometry can be reconstructed. The reconstructed geometric shapes show good agreement with atomic force microscope and transmission electron microscope measurements, highlighting the method’s capability for investigating samples within the nanometer regime.

With advancements in the semiconductor industry, the complexity of three‐dimensional (3D) nanostructures becomes higher with continuously decreasing feature sizes. In order to monitor the processing steps, it is crucial to accurately determine the critical dimensions and composition of these nanostructures. Early detection of production malfunctions is crucial for reasonable production yields, as even minor imperfections can heavily impede device performance. Grazing X‐ray fluorescence spectroscopy methods are non‐destructive and element‐specific methods with high sensitivity for nanostructured surfaces. They enable the quantification and localization of elements within the sample, with information depths sufficient also for buried features. Such measurements can be utilized to reconstruct the shape and size of nanostructure features, with simulated data based on finite element method (FEM) Maxwell calculations. However, the computation time of FEM calculations poses a challenge (especially in the X‐ray energy regime), prolonging the reconstruction process and making it impractical for the characterization of multiple samples. To address this issue, a neural network‐based approach is adopted to replace the time‐consuming FEM‐based forward calculations. In this study, the feasibility of utilizing neural networks for nanostructure reconstruction is demonstrated on various nanostructures. The discrimination limit for different model parameters is assessed and compared against conventional FEM calculation results.

Sulfur and its compounds are both very abundant in the environment and very important for technological applications such as batteries. X-ray spectroscopic characterizations of sulfur compounds for a quantitative determination of the present amount of sulfur often requires a good knowledge on the atomic fundamental parameters involved. These quantitatively describe the processes of photoionization and x-ray fluorescence emission, making them crucial for x-ray spectroscopy-based quantifications. By employing the recently demonstrated holistic approach, the atomic fundamental parameters of the sulfur K-shell were experimentally determined using the radiometrically calibrated instrumentation of the Physikalisch-Technische Bundesanstalt. The transition probabilities of the main K-shell fluorescence lines, the K-shell fluorescence yield, the K-shell Auger yield, the subshell photoionization cross section and fluorescence production cross section were determined by means of photon energy dependent x-ray fluorescence and transmission measurements on a thin InS coated silicon nitride membrane. The results are also downloadable from Zenodo as plain text.