Saleem A. Al Dajani | Searcharxiv

arXiv2026

Detecting the Onset and Progression of Spinodal Decomposition using Transient Grating Spectroscopy

Spinodal decomposition can degrade corrosion resistance and embrittle materials. The ability to quickly, conclusively, and non-destructively detect the onset of spinodal decomposition before catastrophic materials degradation would represent a significant advance in materials testing. We demonstrate that spinodal decomposition can be detected in binary Fe-Cr alloys via modulus stiffening using in situ and ex situ transient grating spectroscopy (TGS). The key mechanistic insight is the non-linearity in elastic moduli as function of Cr content renders a spinodally decomposed Fe-Cr alloy stiffer than an equivalent solid solution for a certain range of initial chromium compositions. We confirm the presence of spinodal decomposition in the 36 at.% chromium alloy using differential scanning calorimetry (DSC), linked to known spinodal decomposition energetics, and show via atomistic simulations that elastic modulus stiffening is expected after spinodal decomposition in the 36 at.% chromium alloy. The results of this study suggest the potential use of TGS as a practical tool for non-destructive evaluation of key materials susceptible to such degradation.

cond-mat.mtrl-sci↗

arXiv2025

DeepSeq: High-Throughput Single-Cell RNA Sequencing Data Labeling via Web Search-Augmented Agentic Generative AI Foundation Models

Generative AI foundation models offer transformative potential for processing structured biological data, particularly in single-cell RNA sequencing, where datasets are rapidly scaling toward billions of cells. We propose the use of agentic foundation models with real-time web search to automate the labeling of experimental data, achieving up to 82.5% accuracy. This addresses a key bottleneck in supervised learning for structured omics data by increasing annotation throughput without manual curation and human error. Our approach enables the development of virtual cell foundation models capable of downstream tasks such as cell-typing and perturbation prediction. As data volume grows, these models may surpass human performance in labeling, paving the way for reliable inference in large-scale perturbation screens. This application demonstrates domain-specific innovation in health monitoring and diagnostics, aligned with efforts like the Human Cell Atlas and Human Tumor Atlas Network.

q-bio.GN↗