Bomi Kim | Searcharxiv

arXiv2026

Polymer-Regulated Freezing of Water Droplets Revealed by Synchrotron X-ray Imaging and Raman Spectroscopy

Adding a polymer to a sessile water droplet not only lowers its freezing point but also suppresses the tip singularity that forms during its freezing on cold substrates. Here, we employ synchrotron X-ray and Raman imaging to elucidate the spatiotemporal mechanism underlying tip suppression in an aqueous polyvinyl alcohol (PVA) solution, a model polymer solution. As the polymer concentration increases, we observe slower propagation of the freezing front, reduced bubble entrapment, and a progressively more rounded apex across the volumes and molecular weights examined. X-ray tomography reveals that frozen PVA droplets retain low X-ray transmittance domains in their interiors and at the surface, and Raman spectral mapping confirms that these domains correspond to PVA-enriched regions, providing direct evidence of freeze-induced polymer segregation. These findings indicate that PVA is redistributed heterogeneously during water solidification rather than shifting bulk properties homogeneously, providing a spatially resolved framework for interpreting the observed tip blunting and the suppression of discrete bubble entrapment. Our work identifies freeze-induced polymer segregation as a pathway by which a dissolved polymer regulates both the external shape and the internal structure of a freezing droplet, and these findings shed light on potential applications in freezing-based processes such as freeze-casting and cryopreservation.

cond-mat.soft↗

arXiv2021

Unsupervised Image Denoising with Frequency Domain Knowledge

Supervised learning-based methods yield robust denoising results, yet they are inherently limited by the need for large-scale clean/noisy paired datasets. The use of unsupervised denoisers, on the other hand, necessitates a more detailed understanding of the underlying image statistics. In particular, it is well known that apparent differences between clean and noisy images are most prominent on high-frequency bands, justifying the use of low-pass filters as part of conventional image preprocessing steps. However, most learning-based denoising methods utilize only one-sided information from the spatial domain without considering frequency domain information. To address this limitation, in this study we propose a frequency-sensitive unsupervised denoising method. To this end, a generative adversarial network (GAN) is used as a base structure. Subsequently, we include spectral discriminator and frequency reconstruction loss to transfer frequency knowledge into the generator. Results using natural and synthetic datasets indicate that our unsupervised learning method augmented with frequency information achieves state-of-the-art denoising performance, suggesting that frequency domain information could be a viable factor in improving the overall performance of unsupervised learning-based methods.

eess.IV↗