arXiv subjects

Nicholas Bravo-Frank

Publications and source records attributed to Nicholas Bravo-Frank.

3 recordsLinked to original sources

arXiv2026

HoloCMA: A Holographic Eye on Coarse-Mode Aerosols

Coarse-mode aerosols (CMAs), including pollen, spores, and dust, remain difficult to characterize in situ because conventional instruments rarely resolve particle geometry and number concentration together. We present HoloCMA, which combines digital inline holography, numerical reconstruction, and deep learning-assisted analysis for particle-resolved measurement. Its optical system targets particles from approximately 5.0 um to the millimeter scale. HoloCMA supports active sampling at up to 30.0 L/min and converts to open-path operation by removing the sampling module. With an NVIDIA GeForce RTX 5070 Laptop GPU and 8 GB VRAM, HoloCMA reports equivalent circular diameter (ECD) and number concentration in real time without sustained queue buildup up to 11.4 particles/cm3. Additional computing could extend real-time analysis to the workflow limit of 100.0 particles/cm3. We evaluated HoloCMA using polystyrene latex spheres, sodium chloride and ammonium sulfate crystals, and oleic acid droplets with nominal diameters of 4.9-11.8 um, with an Aerodynamic Particle Sizer (APS) for comparison. Across 12 comparisons, absolute differences between HoloCMA and corrected APS geometric-mean diameters ranged from less than 0.1 to 1.0 um, with a mean absolute difference of 0.5 um and a mean absolute relative difference of 8.4%. HoloCMA measures geometric size directly from reconstructed contours, whereas converting APS aerodynamic diameter requires material-specific assumptions. The APS-to-HoloCMA concentration ratio generally decreased with particle size, consistent with size-dependent transport and counting losses in the APS, although the cause could not be determined conclusively. HoloCMA thus enables continuous CMA measurements that combine geometric size, number concentration, and retained particle images, supporting classification and long-term atmospheric, environmental, and indoor-air monitoring.

physics.optics↗

arXiv2024

Realtime Particulate Matter and Bacteria Analysis of Peritoneal Dialysis Fluid using Digital Inline Holography

We developed a digital inline holography (DIH) system integrated with deep learning algorithms for real-time detection of particulate matter (PM) and bacterial contamination in peritoneal dialysis (PD) fluids. The system comprises a microfluidic sample delivery module and a DIH imaging module that captures holograms using a pulsed laser and a digital camera with a 40x objective. Our data processing pipeline enhances holograms, reconstructs images, and employs a YOLOv8n-based deep learning model for particle identification and classification, trained on labeled holograms of generic PD particles, Escherichia coli (E. coli), and Pseudomonas aeruginosa (P. aeruginosa). The system effectively detected and classified generic particles in sterile PD fluids, revealing diverse morphologies predominantly sized 1-5 um with an average concentration of 61 particles per microliter. In PD fluid samples spiked with high concentrations of E. coli and P. aeruginosa, our system achieved high sensitivity in detecting and classifying these bacteria at clinically relevant low false positive rates. Further validation against standard colony-forming unit (CFU) methods using PD fluid spiked with bacterial concentrations from approximately 100 to 10,000 bacteria per milliliter demonstrated a clear one-to-one correspondence between our measurements and CFU counts. Our DIH system provides a rapid, accurate alternative to traditional culture-based methods for assessing bacterial contamination in PD fluids. By enabling real-time sterility monitoring, it can significantly improve patient outcomes in PD treatment, facilitate point-of-care fluid production, reduce logistical challenges, and be extended to quality control in pharmaceutical production.

physics.optics↗

arXiv2024

Holographic Air-quality Monitor (HAM)

We introduce the holographic air-quality monitor (HAM) system, uniquely tailored for monitoring large particulate matter (PM) over 10 um in diameter, i.e., particles critical for disease transmission and public health but overlooked by most commercial PM sensors. The HAM system utilizes a lensless digital inline holography (DIH) sensor combined with a deep learning model, enabling real-time detection of PMs, with greater than 97% true positive rate at less than 0.6% false positive rate, and analysis of PMs by size and morphology at a sampling rate of 26 liters per minute (LPM), for a wide range of particle concentrations up to 4000 particles/L. Such throughput not only significantly outperforms traditional imaging-based sensors but also rivals some lower-fidelity, non-imaging sensors. Additionally, the HAM system is equipped with additional sensors for smaller PMs and various air quality conditions, ensuring a comprehensive assessment of indoor air quality. The performance of the DIH sensor within the HAM system was evaluated through comparison with brightfield microscopy, showing high concordance in size measurements. The efficacy of the DIH sensor was also demonstrated in two two-hour experiments under different environments simulating practical conditions with one involving distinct PM-generating events. These tests highlighted the HAM system's advanced capability to differentiate PM events from background noise and its exceptional sensitivity to irregular, large-sized PMs of low concentration.

physics.ins-det↗