Aknur Karabay | Searcharxiv

arXiv2026

Free-form diamond refractive optics enable efficient high-energy X-ray nano-imaging

Full-field transmission X-ray microscopy (TXM) enables nondestructive three-dimensional imaging of thick and strongly absorbing materials with high spatial resolution. Such capabilities are essential for understanding structure-function relationships in hierarchical materials, with broad applications in biology, energy conversion, and energy storage. At high photon energies, however, TXM performance is limited by the reduced efficiency of diffractive optics and by the challenge of matching the numerical aperture (NA) of the illumination to that of the objective optics. Here we harness freeform diamond refractive optics to overcome a key illumination-efficiency bottleneck in high-energy TXM, demonstrating full-field nano-imaging at 20 keV with a half-period resolution of 72 nm. The optical system combines a custom-designed diamond refractive beam shaper that produces a uniform near-flat-top illumination at the sample with a 94% efficiency, a moving diffuser placed near the sample to increase the effective illumination NA and improve image quality and resolution, and high-precision aberration-corrected diamond compound refractive lenses as the objective optics. These results establish free-form diamond optics as a powerful route to efficient high-energy TXM, expanding full-field nano-imaging to complex, evolving materials systems and enabling in situ, operando, and tomographic studies under experimentally realistic conditions. Furthermore, it opens new avenues for innovation of joint X-ray optical-digital design for a new generation of high-energy X-ray nano-imaging.

physics.optics↗

arXiv2023

A Central Asian Food Dataset for Personalized Dietary Interventions, Extended Abstract

Nowadays, it is common for people to take photographs of every beverage, snack, or meal they eat and then post these photographs on social media platforms. Leveraging these social trends, real-time food recognition and reliable classification of these captured food images can potentially help replace some of the tedious recording and coding of food diaries to enable personalized dietary interventions. Although Central Asian cuisine is culturally and historically distinct, there has been little published data on the food and dietary habits of people in this region. To fill this gap, we aim to create a reliable dataset of regional foods that is easily accessible to both public consumers and researchers. To the best of our knowledge, this is the first work on creating a Central Asian Food Dataset (CAFD). The final dataset contains 42 food categories and over 16,000 images of national dishes unique to this region. We achieved a classification accuracy of 88.70\% (42 classes) on the CAFD using the ResNet152 neural network model. The food recognition models trained on the CAFD demonstrate computer vision's effectiveness and high accuracy for dietary assessment.

cs.CV↗