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Fenng, Y.

Publications and source records attributed to Fenng, Y..

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Spatial-filtering nanoscopy for 40-nm label-free Raman imaging

Super-resolution fluorescence microscopy overcomes the optical diffraction limit and has significantly advanced our understanding of biological complexity within the framework of fluorescence labelling (1). Fluorescence labelling underpins this capability, enabling high photon budgets, superior signal contrast, and tuneable photophysical properties essential for diverse super-resolution modalities (2,3). In contrast, label-free Raman imaging offers intrinsic chemical specificity (4), supporting applications ranging from biomolecular fingerprinting to cell metabolic mapping (5-8) and histopathological tissue characterization (9,10). Despite its label-free advantage and chemical specificity, Raman imaging remains fundamentally limited in both spatial resolution and imaging contrast due to inherently low signal throughput and weak intrinsic Raman contrast (11-13). Here, we introduce spatial-filtering nanoscopy (SFN), a physics-driven super-resolution strategy that achieves resolution enhancement through targeted signal purification instead of signal amplification, offering a conceptually distinct pathway beyond the diffraction limit. SFN synergistically integrates a sub-millimetre microsphere lens (SMML) with a standard confocal Raman microscope, harnessing two complementary physical effects: (i) the photonic redistribution effect (PRE), which narrows the lateral excitation profile, and (ii) the three-dimensional spatial filtering (3D-SFE), which effectively suppresses both lateral and axial background. We demonstrate SFN-enabled super-resolution Raman imaging of silicon nanostructures, intact cells, and tissue sections, achieving an effective lateral resolution of approximately 40 nm, and chemically resolving subcellular features including organelles and pseudopodia without exogenous labels. These findings establish SFN as a broadly generalizable hardware-based super-resolution strategy, readily deployable on standard confocal platforms and extensible across diverse optical imaging modalities.

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