arXiv subjects

Martina Russo

Publications and source records attributed to Martina Russo.

3 recordsLinked to original sources

arXiv2026

STEPIC: High-Speed Imaging via Spatio-Temporal Encoding in Photonic Integrated Circuits

High-speed imaging of cells in flow is essential for probing cellular heterogeneity in large populations. Existing imaging approaches based on single-pixel detection and spatio-temporal encoding provide exceptional speed, but typically rely on bulky free-space optics, long dispersive elements, and are prone to alignment instabilities. Here, we introduce STEPIC Microscopy, the first fully integrated on-chip system for high-speed imaging via spatio-temporal encoding in photonic integrated circuits. Our platform leverages waveguides, splitters, fiber delay-lines, and 3D optical remappers to encode spatial information into the temporal domain, enabling robust image reconstruction of cells flowing through microchannels. The monolithic architecture provides a compact and robust platform for high-throughput bioimaging, enabling scalable and practical implementations of ultrafast imaging systems.

physics.optics↗

arXiv2026

Suppressing Plasmonic Heating in Aqueous Environments with Hexagonal Boron Nitride

Optical heating of plasmonic nanostructures is a critical challenge in nanoscale systems. Although plasmonic effects enable enhanced optical functionalities, the associated temperature rise can degrade performance in heat-sensitive applications such as biosensing, nanophotonics, and microelectronics. Conventional cooling strategies fail at these scales due to limited heat transport and high interfacial thermal resistance, motivating the integration of advanced materials for thermal management. Here, we investigate hexagonal boron nitride (hBN) thin flakes as heat spreaders to mitigate plasmonic heating of gold nanospheres immobilized on hBN deposited on glass and surrounded by water. Using finite-element simulations, we quantify the influence of hBN thickness, in-plane thermal conductivity, and interfacial thermal conductance on cooling efficiency. Complementary experiments employ cross-grating wavefront microscopy (CGM) for nanothermometry to map the temperature around optically heated gold nanoparticles and quantify the cooling effect of hBN. We extend the application of CGM for rapid, non-invasive, and all-optical characterization of non-absorbing 2D materials. Our results reveal a strong thickness dependence, where heat dissipation in thin flakes is limited by the heat capacity of hBN and in thick flakes by interfacial thermal conductance. Including hBN, we obtain a reduction in temperature rise by up to 60% compared to glass. In addition, the presence of two main heat dissipation pathways emerges: a direct one from the nanoparticle to the hBN and an indirect one from the particle via water to the hBN. This combined simulation-experiment framework offers a versatile approach to improve thermal management in plasmonic systems and beyond, establishing design guidelines for integrating 2D materials into thermally sensitive platforms such as biosensors and integrated circuits.

physics.optics↗

arXiv2025

Augmentation-Based Deep Learning for Identification of Circulating Tumor Cells

Circulating tumor cells (CTCs) are crucial biomarkers in liquid biopsy, offering a noninvasive tool for cancer patient management. However, their identification remains particularly challenging due to their limited number and heterogeneity. Labeling samples for contrast limits the generalization of fluorescence-based methods across different hospital datasets. Analyzing single-cell images enables detailed assessment of cell morphology, subcellular structures, and phenotypic variations, often hidden in clustered images. Developing a method based on bright-field single-cell analysis could overcome these limitations. CTCs can be isolated using an unbiased workflow combining Parsortix technology, which selects cells based on size and deformability, with DEPArray technology, enabling precise visualization and selection of single cells. Traditionally, DEPArray-acquired digital images are manually analyzed, making the process time-consuming and prone to variability. In this study, we present a Deep Learning-based classification pipeline designed to distinguish CTCs from leukocytes in blood samples, aimed to enhance diagnostic accuracy and optimize clinical workflows. Our approach employs images from the bright-field channel acquired through DEPArray technology leveraging a ResNet-based CNN. To improve model generalization, we applied three types of data augmentation techniques and incorporated fluorescence (DAPI) channel images into the training phase, allowing the network to learn additional CTC-specific features. Notably, only bright-field images have been used for testing, ensuring the model's ability to identify CTCs without relying on fluorescence markers. The proposed model achieved an F1-score of 0.798, demonstrating its capability to distinguish CTCs from leukocytes. These findings highlight the potential of DL in refining CTC analysis and advancing liquid biopsy applications.

eess.IV↗