Engineering topics

Genc, Arda

Publications and source records attributed to Genc, Arda.

3 records

DOE OSTI2024

Coexistence of antiferrodistortive and polar order in a superconducting SrTi⁢O 3 film

Strontium titanate (SrTiO 3 ) can exhibit multiple orders, including superconductivity, an antiferrodistortive instability and ferroelectricity. The cooperation or competition between these orders in samples that undergo all three transitions is of great fundamental interest. Here we report scanning transmission electron microscopy imaging of the antiferrodistortive and ferroelectric structural distortions in a compressively strained SrTiO 3 film that was previously shown to become superconducting at ~ 410 mK. The experiments are complemented by first-principles simulations. Unlike the polar ferroelectric phase, which is suppressed by dopants, the antiferrodistortive order is insensitive to the presence of the free carriers. The single domain nature of the antiferrodistortive phase excludes any role of antiferrodistortive domain walls in the superconductivity. Furthermore, a previously reported low temperature resistance anomaly is associated with the ferroelectric transition, not the antiferrodistortive transition.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

A deep learning approach for semantic segmentation of unbalanced data in electron tomography of catalytic materials

In computed TEM tomography, image segmentation represents one of the most basic tasks with implications not only for 3D volume visualization, but more importantly for quantitative 3D analysis. In case of large and complex 3D data sets, segmentation can be an extremely difficult and laborious task, and thus has been one of the biggest hurdles for comprehensive 3D analysis. Heterogeneous catalysts have complex surface and bulk structures, and often sparse distribution of catalytic particles with relatively poor intrinsic contrast, which possess a unique challenge for image segmentation, including the current state-of-the-art deep learning methods. To tackle this problem, we apply a deep learning-based approach for the multi-class semantic segmentation of a γ-Alumina/Pt catalytic material in a class imbalance situation. Specifically, we used the weighted focal loss as a loss function and attached it to the U-Net’s fully convolutional network architecture. We assessed the accuracy of our results using Dice similarity coefficient (DSC), recall, precision, and Hausdorff distance (HD) metrics on the overlap between the ground-truth and predicted segmentations. Our adopted U-Net model with the weighted focal loss function achieved an average DSC score of 0.96 ± 0.003 in the γ-Alumina support material and 0.84 ± 0.03 in the Pt NPs segmentation tasks. We report an average boundary-overlap error of less than 2 nm at the 90th percentile of HD for γ-Alumina and Pt NPs segmentations. The complex surface morphology of γ-Alumina and its relation to the Pt NPs were visualized in 3D by the deep learning-assisted automatic segmentation of a large data set of high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) tomography reconstructions.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Progressive growth of the solid–electrolyte interphase towards the Si anode interior causes capacity fading

The solid–electrolyte interphase (SEI), a layer formed on the electrode surface, is essential for electrochemical reactions in batteries and critically governs the battery stability. Active materials, especially those with extremely high energy density, such as silicon (Si), often inevitably undergo a large volume swing upon ion insertion and extraction, raising a critical question as to how the SEI interactively responds to and evolves with the material and consequently controls the cycling stability of the battery. Here, by integrating sensitive elemental tomography, an advanced algorithm and cryogenic scanning transmission electron microscopy, we unveil, in three dimensions, a correlated structural and chemical evolution of Si and SEI. Furthermore, corroborated with a chemomechanical model, we demonstrate progressive electrolyte permeation and SEI growth along the percolation channel of the nanovoids due to vacancy injection and condensation during the delithiation process. Consequently, the Si–SEI spatial configuration evolves from the classic ‘core–shell’ structure in the first few cycles to a ‘plum-pudding’ structure following extended cycling, featuring the engulfing of Si domains by the SEI, which leads to the disruption of electron conduction pathways and formation of dead Si, contributing to capacity loss. The spatially coupled interactive evolution model of SEI and active materials, in principle, applies to a broad class of high-capacity electrode materials, leading to a critical insight for remedying the fading of high-capacity electrodes.

36 MATERIALS SCIENCE↗