arXiv subjects

Gozde Oney

Publications and source records attributed to Gozde Oney.

4 recordsLinked to original sources

arXiv2025

Nanoscale Mapping of Transition Metal Ordering in Individual LiNi0.5Mn1.5O4 Particles Using 4D-STEM ACOM Technique

The electrochemical performance of the spinel LiNi0.5Mn1.5O4, a high-voltage positive electrode material for Li-ion batteries, is influenced by the transition metal arrangement in the octahedral network, leading to disordered (Fd m S.G.) and ordered3 (P4332 S.G.) structures. However, widely used techniques lack the spatial resolution necessary to elucidate the ordering phenomenon at the particle scale. Using the 4D-STEM technique, we present the first direct observation of ordering distribution in individual LiNi0.5Mn1.5O4 particles with nanometric spatial resolution. We propose a quantification method for the local degree of ordering based on the ratio of ordered to disordered spinel lattices along the particle thickness extracted from electron diffraction spot intensities. In an ordered spinel LiNi0.5Mn1.5O4, the transition metal ordering is consistently observed throughout the primary particle. However, the extent of ordering in the spinel phase depends on its distribution at the particle scale, a factor influenced by the annealing conditions. The 4D-STEM analysis elucidates the boundary between highly-ordered and low-ordered LiNi0.5Mn1.5O4 particles.

cond-mat.mtrl-sci↗

arXiv2025

Unsupervised Multi-Clustering and Decision-Making Strategies for 4D-STEM Orientation Mapping

This study presents a novel integration of unsupervised learning and decision-making strategies for the advanced analysis of 4D-STEM datasets, with a focus on non-negative matrix factorization (NMF) as the primary clustering method. Our approach introduces a systematic framework to determine the optimal number of components (k) required for robust and interpretable orientation mapping. By leveraging the K-Component Loss method and Image Quality Assessment (IQA) metrics, we effectively balance reconstruction fidelity and model complexity. Additionally, we highlight the critical role of dataset preprocessing in improving clustering stability and accuracy. Furthermore, our spatial weight matrix analysis provides insights into overlapping regions within the dataset by employing threshold-based visualization, facilitating a detailed understanding of cluster interactions. The results demonstrate the potential of combining NMF with advanced IQA metrics and preprocessing techniques for reliable orientation mapping and structural analysis in 4D-STEM datasets, paving the way for future applications in multi-dimensional material characterization.

cs.LG↗

arXiv2025

Dead, Slow and Overworked Graphite: Operando X-ray Microdiffraction Mapping of Aged Electrodes

Aging limits lithium-ion battery lifetime and must be understood to improve durability and performance, requiring a detailed understanding of how aging alters the availability of cyclable lithium and the integrity of active particles. In this work, (de)lithiation mechanisms are examined and spatially-resolved at the microscale in aged graphite electrodes dismounted from a large format graphite/LiFePO4-Li(NiCoAl)O2 cell at 70% remaining capacity. A multi-technique workflow is employed, combining electrochemical methods with post-mortem structural and morphological analyses, and introducing synchrotron microX-ray 2D diffraction imaging as a technique to probe aged graphite, applied at C-rates from C/5 to C. In-plane and through-plane heterogeneities in graphite dynamics are evidenced, showing the presence of inactive regions localized in two dimensions. In these areas, particles are either disconnected (irreversibly lost) or kinetically limited (reactivated at a slow C-rate), with dead or slow particles exhibiting a wide range of compositions, from x = 0 to x = 1 in LixC6. These inactivated graphite particles are found to be heterogeneously distributed throughout the depth of the aged negative electrode. In particular, the most inactivated region localizes at the negative electrode-separator interface, correlating to overworking graphite near the separator.

cond-mat.mtrl-sci↗

arXiv2023

Improved ACOM pattern matching in 4D STEM through adaptive sub pixel peak detection and image reconstruction

The technique known as 4D-STEM has recently emerged as a powerful tool for the local characterization of crystalline structures in materials, such as cathode materials for Li-ion batteries or perovskite materials for photovoltaics. However, the use of new detectors optimized for electron diffraction patterns and other advanced techniques requires constant adaptation of methodologies to address the challenges associated with crystalline materials. In this study, we present a novel image processing method to improve pattern matching in the determination of crystalline orientations and phases. Our approach uses sub-pixelar adaptative image processing to register and reconstruct electron diffraction signals in large 4D-STEM datasets. By using adaptive prominence and linear filters such as mean and gaussian blur, we are able to improve the quality of the diffraction pattern registration. The resulting data compression rate of 103 is well-suited for the era of big data and provides a significant enhancement in the performance of the entire ACOM data processing method. Our approach is evaluated using dedicated metrics, which demonstrate a high improvement in phase recognition. Our results demonstrate that this data preparation method not only enhances the quality of the resulting image but also boosts the confidence level in the analysis of the outcomes related to determining crystal orientation and phase. Additionally, it mitigates the impact of user bias that may occur during the application of the method through the manipulation of parameters.

cond-mat.mtrl-sci↗