Towards Intelligent Multimodal Holography: Enabling Real-time, Selective Chemical Imaging for Dynamic Ion Separation

Molecular-level understanding and visualizing ion transport and separation dynamics in complex environments is central to advancing energy systems, water purification, and critical materials recovery. Achieving this requires imaging platforms capable of resolving both structural evolution and chemical specificity, non-invasively and in real time. Digital off-axis holography (DOAH) has emerged as a powerful, label-free technique for high-resolution quantitative phase imaging for monitoring chemical changes in solution and at solid–liquid–gas interfaces. However, DOAH inherently lacks chemical specificity, limiting their applicability to distinguish ionic species or map compositional gradients. Recent developments in multimodal holography combining DOAH with spectroscopic modalities such as fluorescence or Raman offer chemically sensitive imaging capabilities, but introduce challenges in data fusion, temporal synchronization, and computational throughput. In parallel, artificial intelligence and machine learning are reshaping DOAH with state-of-the-art models combining physics-based modules with data-driven neural networks to deliver fast and interpretable reconstructions. Here, we present a conceptual framework for intelligent multimodal holography that leverages physics-informed machine learning to enable sparse, adaptive acquisition and real-time chemical-structural inference. Collectively, these developments open new possibilities for employing DOAH in situ imaging across chemical separations and interfacial science.