arXiv subjects

Alexander Ditter

Publications and source records attributed to Alexander Ditter.

3 recordsLinked to original sources

arXiv2026

Operando spectro-ptychography reveals dynamical charge-storage and degradation pathways in redox-active electrodes

Electrochemical reactions at buried electrode-electrolyte interfaces govern how redox-active materials store and release energy. However, these reactions are difficult to visualize because chemical and morphological changes occur simultaneously over distinct length and time scales. Existing operando microscopies often require trade-offs among chemical sensitivity, spatial resolution and temporal resolution. Direct nanoscale tracking of such processes throughout extended timescale has therefore remained out of reach. Here, we develop a fast and robust operando soft X-ray spectro-ptychography platform that delivers chemical-state-resolved spatiotemporal movies of redox-active electrodes over the full battery lifetime. Applied to an alkaline Fe anode, the method reveals that reversible charge storage gives way to degradation through two competing processes: rapid hydroxide insertion that drives early reversible cycling, and slower dissolution-redeposition that redistributes Fe, enlarges FeOOH particles, and ultimately causes capacity loss. By separating fast charge-storage chemistry from slower degradation chemistry in operando and at both single-particle and particle ensemble level, this work establishes spectro-ptychography as a general approach for studying dynamic redox transformations in batteries, electrocatalysts, and other electrochemical materials.

cond-mat.mtrl-sci↗

arXiv2026

Machine Learning-Augmented Acceleration of Iterative Ptychographic Reconstruction

Iterative ptychographic reconstruction algorithms are widely used for coherent diffractive imaging but can exhibit slow convergence under realistic experimental conditions. We propose a machine learning-augmented approach that accelerates iterative ptychographic reconstruction by introducing a learned fast-forward operator applied during reconstruction. Following an initial warm-up using standard iterations, the fast-forward operator advances the reconstruction toward a more converged state, after which conventional iterative updates are resumed. This strategy preserves the physical consistency and flexibility of established ptychographic solvers while reducing the number of iterations required for convergence. The model is trained on diverse ptychographic datasets and evaluated on experimental data acquired in a different year, demonstrating robustness and temporal generalization. Compared with conventional iterative solvers, the machine learning-augmented method achieves comparable reconstruction quality while converging faster in terms of Poisson negative log-likelihood, yielding over a two-fold reduction in wall-clock time. The approach has been integrated into an existing reconstruction pipeline and deployed in production at a synchrotron beamline, demonstrating practicality for real-time experimental operation.

cs.LG↗

arXiv2015

OpenCL 2.0 for FPGAs using OCLAcc

Designing hardware is a time-consuming and complex process. Realization of both, embedded and high-performance applications can benefit from a design process on a higher level of abstraction. This helps to reduce development time and allows to iteratively test and optimize the hardware design during development, as common in software development. We present our tool, OCLAcc, which allows the generation of entire FPGA-based hardware accelerators from OpenCL and discuss the major novelties of OpenCL 2.0 and how they can be realized in hardware using OCLAcc.

cs.SE↗