Deep Speckle Holography Redefines Label-free Nanoparticle Phenotyping

Nanoparticle metrology has long been constrained by the assumption that, in mixed and unprocessed fluids, particle size, morphology, composition, and species-specific abundance cannot be resolved simultaneously from a single label-free measurement. Here, we revisit this long-standing limitation by showing that complex forward speckle-holographic fields define an information-rich optical space for multidimensional particle signatures. We report deep speckle holography, a physics-informed generative framework that profiles particle identity, size, morphology, and species-resolved abundance from a single non-contact optical measurement. Across purified suspensions, mixed particle populations, environmental waters, human urine, and other unprocessed native fluids, the method enables direct nanoparticle inference without purification, labeling, or destructive preprocessing, delivering concurrent multidimensional readouts in 0.9 s over a dynamic range spanning 10 orders of magnitude. Deep speckle holography establishes a route toward direct label-free nanoparticle phenotyping in real-world fluids, moving nanoscale measurement beyond isolated-particle characterization toward multidimensional inference in complex mixtures, and expanding the scope of questions nanoscale measurement can address, from real-time tracking of nanoparticle transformations in living and environmental systems to non-invasive quality control of nanomedicine formulations, and beyond.

Universal Spectral Transfer with Physical Prior-Informed Deep Generative Learning

Spectroscopy is a powerful analytical technique for characterizing matter across physical and biological realms1-5. However, its fundamental principle necessitates specialized instrumentation per physical phenomena probed, limiting broad adoption and use in all relevant research. In this study, we introduce SpectroGen, a novel physical prior-informed deep generative model for generating relevant spectral signatures across modalities using experimentally collected spectral input only from a single modality. We achieve this by reimagining the representation of spectral data as mathematical constructs of distributions instead of their traditional physical and molecular state representations. The results from 319 standard mineral samples tested demonstrate generating with 99% correlation and 0.01 root mean square error with superior resolution than experimentally acquired ground truth spectra. We showed transferring capability across Raman, Infrared, and X-ray Diffraction modalities with Gaussian, Lorentzian, and Voigt distribution priors respectively6-10. This approach however is globally generalizable for any spectral input that can be represented by a distribution prior, making it universally applicable. We believe our work revolutionizes the application sphere of spectroscopy, which has traditionally been limited by access to the required sophisticated and often expensive equipment towards accelerating material, pharmaceutical, and biological discoveries.