arXiv subjects

Jiarong Hong

Publications and source records attributed to Jiarong Hong.

At least 19 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↗

arXiv2025

Seeing new depths: Three-dimensional flow of a free-swimming alga

A swimming microorganism stirs the surrounding fluid, creating a flow field that governs not only its locomotion and nutrient uptake, but also its interactions with other microorganisms and the environment. Despite its fundamental importance, capturing this flow field and unraveling its biological implications remains a challenge. Here, we report the first direct, time-resolved measurements of the three-dimensional (3D) flow field generated by a single, free-swimming microalga, Chlamydomonas reinhardtii, a model organism for microbial locomotion and flagellar dynamics. Supported by hydrodynamic modeling and simulations, our measurements resolve how established two-dimensional (2D) flow features such as in-plane vortices and the stagnation point emerge from and shape the full algal flow in 3D. Moreover, we reveal unexpected low-Reynolds-number flow phenomena including micron-sized vortex rings and periodically recurring translating vortices and uncover topological changes in the underlying flow structure associated with the puller-to-pusher transition of an alga. Biologically, access to the 3D flow field enables rigorous quantification of the alga's energy expenditure, as well as its swimming and feeding efficiency, improving the precision of these physiological metrics. Taken together, our study demonstrates rich vortex dynamics in inertialess flows and shows their influence on microbial motility. The work also introduces a new experimental method for mapping the fluid environment sculpted by beating flagella.

physics.flu-dyn↗

arXiv2025

Circulating tumor cell detection in cancer patients using in-flow deep learning holography

Circulating tumor cells (CTCs) are cancer cells found in the bloodstream that serve as biomarkers for early cancer detection, prognostication, and disease monitoring. However, CTC detection remains challenging due to low cell abundance and heterogeneity. Digital holographic microscopy (DHM) offers a promising, label-free method for high-throughput CTC identification by capturing superior morphological information compared to traditional imaging methods, while remaining compatible with in-flow data acquisition. We present a streamlined DHM-based system that integrates microfluidic enrichment with deep learning-driven image analysis, supplemented by immunofluorescent profiling, to improve the sensitivity and specificity of CTC enumeration. Specifically, our platform combines inertial microfluidic preprocessing with dual-modality imaging, integrating holography with fluorescence sensing of up to two markers. A deep learning model, trained on a diverse set of healthy blood samples and cancer cell lines, and executed in real-time, provides a morphological confidence on a cell-by-cell basis that may then be combined with immunofluorescence criteria for enumeration. In a pilot study, we demonstrate significantly higher CTC counts in patients with late-stage prostate cancer (n=13) compared to healthy controls (n=8), with a patient-level false positive rate of 1 cell/mL. Notably, nearly two-thirds of identified CTCs were EpCAM-negative but PSMA positive (a prostate specific epithelial marker), suggesting that traditional use of EpCAM as an epithelial marker for CTCs may lead to false negatives. These findings highlight the potential of DHM for applications including but not limited to screening, diagnostics, and precision oncology.

physics.optics↗

arXiv2025

3D Characterization of Smoke Plume Dispersion Using Multi-View Drone Swarm

This study presents an advanced multi-view drone swarm imaging system for the three-dimensional characterization of smoke plume dispersion dynamics. The system comprises a manager drone and four worker drones, each equipped with high-resolution cameras and precise GPS modules. The manager drone uses image feedback to autonomously detect and position itself above the plume, then commands the worker drones to orbit the area in a synchronized circular flight pattern, capturing multi-angle images. The camera poses of these images are first estimated, then the images are grouped in batches and processed using Neural Radiance Fields (NeRF) to generate high-resolution 3D reconstructions of plume dynamics over time. Field tests demonstrated the ability of the system to capture critical plume characteristics including volume dynamics, wind-driven directional shifts, and lofting behavior at a temporal resolution of about 1 s. The 3D reconstructions generated by this system provide unique field data for enhancing the predictive models of smoke plume dispersion and fire spread. Broadly, the drone swarm system offers a versatile platform for high resolution measurements of pollutant emissions and transport in wildfires, volcanic eruptions, prescribed burns, and industrial processes, ultimately supporting more effective fire control decisions and mitigating wildfire risks.

cs.RO↗

arXiv2025

Autonomous Drone for Dynamic Smoke Plume Tracking

This paper presents a novel autonomous drone-based smoke plume tracking system capable of navigating and tracking plumes in highly unsteady atmospheric conditions. The system integrates advanced hardware and software and a comprehensive simulation environment to ensure robust performance in controlled and real-world settings. The quadrotor, equipped with a high-resolution imaging system and an advanced onboard computing unit, performs precise maneuvers while accurately detecting and tracking dynamic smoke plumes under fluctuating conditions. Our software implements a two-phase flight operation, i.e., descending into the smoke plume upon detection and continuously monitoring the smoke movement during in-plume tracking. Leveraging Proportional Integral-Derivative (PID) control and a Proximal Policy Optimization based Deep Reinforcement Learning (DRL) controller enables adaptation to plume dynamics. Unreal Engine simulation evaluates performance under various smoke-wind scenarios, from steady flow to complex, unsteady fluctuations, showing that while the PID controller performs adequately in simpler scenarios, the DRL-based controller excels in more challenging environments. Field tests corroborate these findings. This system opens new possibilities for drone-based monitoring in areas like wildfire management and air quality assessment. The successful integration of DRL for real-time decision-making advances autonomous drone control for dynamic environments.

cs.RO↗

arXiv2024

A Review of 3D Particle Tracking and Flow Diagnostics Using Digital Holography

Advanced three-dimensional (3D) tracking methods are essential for studying particle dynamics across a wide range of complex systems, including multiphase flows, environmental and atmospheric sciences, colloidal science, biological and medical research, and industrial manufacturing processes. This review provides a comprehensive summary of 3D particle tracking and flow diagnostics using Digital Holography (DH). We begin by introducing the principles of DH, accompanied by a detailed discussion on numerical reconstruction. The review then explores various hardware setups used in DH, including inline, off-axis, and dual or multiple-view configurations, outlining their advantages and limitations. We also delve into different hologram processing methods, categorized into traditional multi-step, inverse, and machine learning-based approaches, providing in-depth insights into their applications for 3D particle tracking and flow diagnostics across multiple studies. The review concludes with a discussion on future prospects, emphasizing the significant role of machine learning in enabling accurate DH-based particle tracking and flow diagnostic techniques across diverse fields, such as manufacturing, environmental monitoring, and biological sciences.

physics.flu-dyn↗

arXiv2024

Generalizable Deep Learning Approach for 3D Particle Imaging using Holographic Microscopy

Despite its potential for label-free particle diagnostics, holographic microscopy is limited by specialized processing methods that struggle to generalize across diverse settings. We introduce a deep learning architecture leveraging human perception of longitudinal variation of diffracted patterns of particles, which enables highly generalizable analysis of 3D particle information with orders of magnitude improvement in processing speed. Trained with minimal synthetic and real holograms of simple particles, our method demonstrates exceptional performance on various challenging cases including those with high particle concentrations and noises and a wide range of particle sizes, complex shapes, and optical properties exceeding the diversity of the training datasets.

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↗

arXiv2024

Field investigation of 3D snow settling dynamics under weak atmospheric turbulence

Research on settling dynamics of snow particles, considering their complex morphologies and real atmospheric conditions, remains scarce despite extensive simulations and laboratory studies. Our study bridges the gap through a comprehensive field investigation into the three-dimensional (3D) snow settling dynamics under weak atmospheric turbulence, enabled by a 3D particle tracking velocimetry (PTV) system to record > a million trajectories, coupled with a snow particle analyzer for simultaneous aerodynamic property characterization of four distinct snow types (aggregates, graupels, dendrites, needles). Our findings indicate that while the terminal velocity predicted by the aerodynamic model aligns well with PTV-measured settling velocity for graupels, significant discrepancies arise for non-spherical particles, particularly dendrites, which exhibit higher drag coefficients than predicted. Qualitative observations of 3D settling trajectories highlight pronounced meandering in aggregates and dendrites, in contrast to the subtler meandering observed in needles and graupels, attributable to their smaller frontal areas. This meandering in aggregates and dendrites occurs at lower frequencies compared to that of graupels. Further quantification of trajectory acceleration and curvature suggests that the meandering frequencies in aggregates and dendrites are smaller than that of morphology-induced vortex shedding of disks, likely due to their rotational inertia, and those of graupels align with the small-scale atmospheric turbulence. Moreover, our analysis of vertical acceleration along trajectories elucidates that the orientation changes in dendrites and aggregates enhance their settling velocity. Such insights into settling dynamics refine models of snow settling velocity under weak atmospheric turbulence, with broader implications for more accurately predicting ground snow accumulation.

physics.flu-dyn↗

arXiv2024

Stochastic modelling of the instantaneous velocity profile in rough-wall turbulent boundary layers

The statistical properties of Uniform Momentum Zones (UMZs) are extracted from laboratory and field measurements in rough wall turbulent boundary layers to formulate a set of stochastic models for the simulation of instantaneous velocity profiles. A spatio-temporally resolved velocity dataset, covering a field of view of $8 \times 9$ m$^2$, was obtained in the atmospheric surface layer using super-large-scale particle image velocimetry (SLPIV), as part of the Grand-scale Atmospheric Imaging Apparatus (GAIA). Wind tunnel data from a previous study are included for comparison \citep{heisel2020mixing}. The probability density function of UMZ attributes such as their thickness, modal velocity, and averaged vertical velocity are built at varying elevations and modeled using log-normal and Gaussian distributions. Inverse transform sampling of the distributions is used to generate synthetic step-like velocity profiles that are spatially and temporally uncorrelated. Results show that in the wide range of wall-normal distances and $Re_τ$ up to $ \sim O(10^6)$ investigated here, shear velocity scaling is manifested in the velocity jump across shear interfaces between adjacent UMZs, and attached eddy behavior is observed in the linear proportionality between UMZ thickness and their wall normal location. These very same characteristics are recovered in the generated instantaneous profiles, using both a fully stochastic and a data-driven hybrid stochastic models, which address, in different ways, the coupling between modal velocities and UMZ thickness. Our method provides a stochastic approach for generating an ensemble of instantaneous velocity profiles, consistent with the structural organization of UMZs, where the ensemble reproduces the logarithmic mean velocity profile and recovers significant portions of the Reynolds stresses and thus of the streamwise and vertical velocity variability.

physics.flu-dyn↗

arXiv2023

Three-dimensional internal flow evolution of an evaporating droplet and its role in particle deposition pattern

The internal flow within an evaporating sessile droplet is one of the driving mechanisms that lead to the variety of particle deposition patterns seen in applications such as inkjet printing, surface patterning, and blood stain analysis. Despite decades of research, the causal link between droplet internal flow and particle deposition patterns has not been fully established. In this study, we employ a 3D imaging technique based on digital inline holography to quantitatively assess the evolution of internal flow fields and particle migration in three distinct types of wetting droplets: water, sucrose aqueous solution, and SDS aqueous solution droplets, throughout their entire evaporation process. Our imaging reveals the three-stage evolution of the 3D internal flow regimes driven by changes in the relative importance of capillary flow, Marangoni flow, and droplet boundary movement during evaporation, each exhibiting unique dynamics. The migration of particles from their initial locations to deposition can be divided into five categories, with particles depositing either at the contact line or inside the droplet. We observe the changing migration directions of particles due to competing Marangoni and capillary flows during droplet evaporation. We further develop an analytical model that predicts the droplet internal flow and deposition patterns and determines the dependence of the deposition mechanisms of particles on their initial locations and the evolving internal flow field. The model, validated using different types of droplets from our experiment and the literature, can be further expanded to other Newtonian and non-Newtonian droplets, which can potentially serve as a real-time assessment tool for particle deposition in various applications.

physics.flu-dyn↗

arXiv2023

Visualization and Characterization of Agricultural Sprays Using Machine Learning based Digital Inline Holography

Accurate characterization of agricultural sprays is crucial to predict in field performance of liquid applied crop protection products. Here we introduce a robust and efficient machine learning (ML) based Digital In-line Holography (DIH) to accurately characterize the droplet field for a wide range of agricultural spray nozzles. Compared to non-ML methods, our method enhances accuracy, generalizability, and processing speed. Our approach employs two neural networks: a modified U-Net to obtain the 3D droplet field from the numerically reconstructed optical field, followed by a VGG16 classifier to reduce false positives from the U-Net prediction. The modified U-Net is trained using holograms generated using a single spray nozzle at three spray locations; center, half-span, and the spray edge to create training data with various number densities and droplet size ranges. VGG16 is trained via the minimum intensity projection of the droplet 3D point spread function. Data augmentation is used to increase the efficiency of classification and make the algorithm generalizable for different measurement settings. The model is validated via NIST traceable glass beads and six agricultural spray nozzles representing various spray characteristics. The results demonstrate a high accuracy rate, with over 90% droplet extraction and less than 5% false positives. Compared to traditional spray measurement techniques, our method offers a significant leap forward in spatial resolution and generalizability. In particular, our method can extract the real cumulative volume distribution of the NIST beads, where the laser diffraction is biased towards droplets moving at slower speeds. Additionally, the ML-based DIH enables the estimation of mass and momentum flux at different locations and the calculation of relative velocities of droplet pairs, which are difficult to obtain via conventional techniques.

physics.flu-dyn↗

arXiv2023

Probing dynamics of elliptical vortex rings via direct vorticity measurements with digital inline holography

Investigating vorticity dynamics provides an effective way for understanding the fundamental mechanisms of fluid flows across diverse scales. However, experimental vorticity measurements often suffer from limited spatial and temporal resolution, hindering our capability to probe into small-scale dynamics in various flows, particularly turbulence. In Li et al. (EXIF, 2022, vol. 63, 161), we introduced a novel holographic vorticimetry technique for direct vorticity measurements by tracking the three-dimensional rotations of tracers with internal markers. This study further extends it to investigate the intricate vorticity dynamics during the evolution of elliptical vortex rings with different aspect ratios. Based on the shadowgraph imaging quantifying the axis-switching cycles and vortex ring deformation, holographic vorticimetry is applied to measure the vorticity distribution within the millimeter-size core of elliptical vortex rings during their evolution. Specifically, our method resolves an even vorticity spread near the core center that rapidly decays within a few hundred microns. Additionally, our results reveal the intricate vorticity fluctuations associated with the folding-unfolding behaviors during the vortex ring evolution. These subtle vorticity changes informed by simulations have not been captured by previous experiments due to limited resolution. Furthermore, we find that higher aspect ratios yield larger initial vorticity and vorticity fluctuations but also prompt earlier inception of instabilities, causing vortex core distortion. These opposing effects result in a non-monotonic vorticity evolution trend. Overall, our measurements demonstrate the efficacy of holographic vorticimetry by measuring the intricate vorticity variations in unsteady vortex flows, paving the way for capturing the vorticity dynamics of small-scale turbulence structures.

physics.flu-dyn↗

arXiv2023

Numerical Investigation of Airborne Infection Risk in an Elevator Cabin under Different Ventilation Designs

Airborne transmission of SARS-CoV-2 via virus-laden aerosols in enclosed spaces poses a significant concern. Elevators, commonly utilized enclosed spaces in modern tall buildings, present a challenge as the impact of varying heating, ventilation, and air conditioning (HVAC) systems on virus transmission within these cabins remains unclear. In this study, we employ computational modeling to examine aerosol transmission within an elevator cabin outfitted with diverse HVAC systems. Using a transport equation, we model aerosol concentration and assess infection risk distribution across passengers' breathing zones. We calculate particle removal efficiency for each HVAC design and introduce a suppression effect criterion to evaluate the effectiveness of the HVAC systems. Our findings reveal that mixing ventilation, featuring both inlet and outlet at the ceiling, proves most efficient in reducing particle spread, achieving a maximum removal efficiency of 79.40% during the exposure time. Conversely, the stratum ventilation model attains a mere removal efficiency of 3.97%. These results underscore the importance of careful HVAC system selection in mitigating the risk of SARS-CoV-2 transmission within elevator cabins.

physics.flu-dyn↗

arXiv2023

Atmospheric aerosol diagnostics with UAV-based holographic imaging and computer vision

Emissions of particulate matter into the atmosphere are essential to characterize, in terms of properties such as particle size, morphology, and composition, to better understand impacts on public health and the climate. However, there is no currently available technology capable of measuring individual particles with such high detail over the extensive domains associated with events such as wildfires or volcanic eruptions. To solve this problem, we present an autonomous measurement system involving an unmanned aerial vehicle (UAV) coupled with a digital inline holographic microscope for in situ particle diagnostics. The flight control uses computer vision to localize and then trace the movements of particle-laden flows while sampling particles to determine their properties as they are transported away from their source. We demonstrate this system applied to measuring particulate matter in smoke plumes and discuss broader implications for this type of system in similar applications.

physics.ins-det↗

arXiv2023

Numerical investigation of airborne transmission in low-ceiling rooms under displacement ventilation

This study employs computational fluid dynamics (CFD) simulations to evaluate the risk of airborne transmission of COVID-19 in low-ceiling rooms, such as elevator cabins, under mechanical displacement ventilation. The simulations take into account the effects of the human body's thermal environment and respiratory jet dynamics on the transmission of pathogens. The results of the study are used to propose a potential mitigation strategy based on ventilation thermal control to reduce the risk of airborne transmission in these types of enclosed indoor spaces. Our findings demonstrate that as the ventilation rate (Qv) increases, the efficiency of removing airborne particles (Ep) initially increases rapidly, reaches a plateau (Ep,c) at a critical ventilation rate (Qc), and subsequently increases at a slower rate beyond Qc. The Qc for low-ceiling rooms is lower compared to high-ceiling rooms due to the increased interaction between the thermal plume generated by the occupants or infectors and the ventilation. Further analysis of the flow and temperature fields reveals that Ep is closely linked to the thermal stratification fields, as characterized by the thermal interface height, the height of the temperature isosurface, and temperature gradient. The simulations also indicate that the location of infector relative to ventilation inlet/outlet affects Qc and Ep,c with higher Qc and lower Ep,c observed when infector is in a corner due to potential formation of a local hot spot of high infection risk when infector is near the ventilation inlet. In conclusion, based on the simulations, we propose a ventilation thermal control strategy, by increasing the ventilation temperature, to reduce the risk of airborne transmission in low-ceiling rooms. Our findings indicate that the thermal environment plays a critical role in the transmission of airborne diseases confined spaces.

physics.flu-dyn↗

arXiv2023

In situ Biological Particle Analyzer based on Digital Inline Holography

Obtaining in situ measurements of biological microparticles is crucial for both scientific research and numerous industrial applications (e.g., early detection of harmful algal blooms, monitoring yeast during fermentation). However, existing methods are limited to offer timely diagnostics of these particles with sufficient accuracy and information. Here, we introduce a novel method for real-time, in situ analysis using machine learning assisted digital inline holography (DIH). Our machine learning model uses a customized YOLO v5 architecture specialized for the detection and classification of small biological particles. We demonstrate the effectiveness of our method in the analysis of 10 plankton species with equivalent high accuracy and significantly reduced processing time compared to previous methods. We also applied our method to differentiate yeast cells under four metabolic states and from two strains. Our results show that the proposed method can accurately detect and differentiate cellular and subcellular features related to metabolic states and strains. This study demonstrates the potential of machine learning driven DIH approach as a sensitive and versatile diagnostic tool for real-time, in situ analysis of both biotic and abiotic particles. This method can be readily deployed in a distributive manner for scientific research and manufacturing on an industrial scale.

physics.bio-ph↗