Accelerated settling velocity of airborne particulate matter on hairy plant leaves.

Phytoremediation has emerged as an ecofriendly technique to reduce hazardous particulate matter (PM) in the air. Although previous studies have conducted statistical analyses to reveal PM removal capabilities of various plant species according to their leaf characteristics, the underlying physical mechanism of PM adsorption of plants remains unclear. Conventional methodologies for measuring PM accumulation usually require long-term field tests and provide limited understanding on PM removal effects of individual leaf traits of various plants. In this study, we propose a novel methodology which can compare the electrostatic interactions between PMs and plant leaves according to their trichome structures by using digital in-line holographic microscopy (DIHM). Surface characteristics of Perilla frutescens and Capsicum annuum leaves are measured to examine electrostatic effects according to the morphological features of trichomes. 3D settling motions of PMs near the microstructures of trichomes of the two plant species are compared in detail. To validate the PM removal effect of the hairy microstructures, a polydimethylsiloxane (PDMS) replica model of a P. frutescens leaf is fabricated to demonstrate accelerated settling velocities of PMs near trichome-like microstructures. The size and electric charge distributions of PMs with irregular shapes are analyzed using DIHM. Numerical simulation of the PM deposition near a trichome-like structure is conducted to verify the empirical results. As a result, the settling velocities of PMs on P. frutescens leaves and a PDMS replica sample are 12.11 ± 1.88% and 34.06 ± 4.19% faster than those on C. annuum leaves and a flat PDMS sample, respectively. These findings indicate that the curved microstructures of hairy trichomes of plant leaves increase the ability to capture PMs by enhancing the electric field intensity just near trichomes. Compared with conventional methods, the proposed methodology can quantitatively evaluate the settling velocity of PMs on various plant leaves according to the morphological structure and density of trichomes within a short period of time. The present research findings would be widely utilized in the selection of suitable air-purifying plants for sustainable removal of harmful air pollutants in urban and indoor environments.

Volumetric monitoring of airborne particulate matter concentration using smartphone-based digital holographic microscopy and deep learning.

Airborne particulate matter (PM) has become a global environmental issue. This PM has harmful effects on public health and precision industries. Conventional air-quality monitoring methods usually utilize expensive equipment, and they are cumbersome to handle for accurate and high throughput measurements. In addition, commercial particle counters have technical limitations in high-concentration measurement, and data fluctuations are induced during air sampling. In this study, a novel smartphone-based technique for monitoring airborne PM concentrations was developed using smartphone-based digital holographic microscopy (S-DHM) and deep learning network called Holo-SpeckleNet. Holographic speckle images of various PM concentrations were recorded by the S-DHM system. The recorded speckle images and the corresponding ground truth PM concentrations were used to train deep learning algorithms consisting of a deep autoencoder and regression layers. The performance of the proposed smartphone-based PM monitoring technique was validated through hyperparameter optimization. The developed S-DHM integrated with Holo-SpeckleNet can be smartly and effectively utilized for portable PM monitoring and safety alarm provision under perilous environmental conditions.

Accurate real-time monitoring of high particulate matter concentration based on holographic speckles and deep learning.

Accurate real-time monitoring of particulate matter (PM) has emerged as a global issue due to the hazardous effects of PM on public health and industry. However, conventional PM monitoring techniques are usually cumbersome and require expensive equipments. In this study, Holo-SpeckleNet is proposed as a fast and accurate PM concentration measurement technique with high throughput using a deep learning based holographic speckle pattern analysis. Speckle pattern datasets of PMs for a wide range of PM concentrations were acquired by using a digital in-line holography microscopy system. Deep autoencoder and regression algorithms were trained with the captured speckle pattern datasets to directly measure PM concentration from speckle pattern images without any air intake device and time-consuming post image processing. The proposed technique was applied to predict various PM concentrations using the test datasets, optimize hyperparameters, and compare its performance with a convolutional neural network (CNN) algorithm. As a result, high PM concentration values can be measured over air quality index of 150, above which human exposure is unhealthy. In addition, the proposed technique exhibits higher measurement accuracy and less overfitting than the CNN with a relative error of 7.46 ± 3.92%. It can be applied to design a compact air quality monitoring device for highly accurate and real-time measurement of PM concentrations under hazardous environment, such as factories or construction sites.

Three-dimensional volumetric monitoring of settling particulate matters on a leaf using digital in-line holographic microscopy.

Plants are considered as a possible modality to reduce particulate matter (PM) particles from ambient air in an ecofriendly manner. A new precise monitoring technique that can explore interactions between individual PM particles and a leaf surface is necessary to understand the underlying mechanisms of PM removal of plant leaves. In this study, a digital in-line holographic microscopy (DIHM) was employed to experimentally investigate the settling motions of PM particles over the leaf surface. The in-plane positions and sizes of opaque PMs with irregular shapes were obtained from the projection images of numerically reconstructed holographic images. The depth positions of PMs were determined by using proper selection of an autofocusing criterion with automatic segmentation method. The edge of a hairy Perilla frutescens leaf was detected by adopting several digital imaging processing techniques. The DIHM technique was applied in this study to accurately detect 3D settling trajectories of PMs with velocity information of PMs in the midair and near leaf surface, simultaneously.

Deep learning-based hologram generation using a white light source.

Digital holographic microscopy enables the recording of sample holograms which contain 3D volumetric information. However, additional optical elements, such as partially or fully coherent light source and a pinhole, are required to induce diffraction and interference. Here, we present a deep neural network based on generative adversarial network (GAN) to perform image transformation from a defocused bright-field (BF) image acquired from a general white light source to a holographic image. Training image pairs of 11,050 for image conversion were gathered by using a hybrid BF and hologram imaging technique. The performance of the trained network was evaluated by comparing generated and ground truth holograms of microspheres and erythrocytes distributed in 3D. Holograms generated from BF images through the trained GAN showed enhanced image contrast with 3-5 times increased signal-to-noise ratio compared to ground truth holograms and provided 3D positional information and light scattering patterns of the samples. The developed GAN-based method is a promising mean for dynamic analysis of microscale objects with providing detailed 3D positional information and monitoring biological samples precisely even though conventional BF microscopic setting is utilized.

Air spreading through wetted cellulose membranes: Implications for the safety function of hydraulic valves in plants.

Plants transport water against the risk of cavitation inside xylem vessels, called "embolism." As one of their hydraulic strategies, pit membranes composed of cellulose fibers have been known as safety valves that prevent the spreading of embolism towards adjacent xylem vessels. However, detailed observation of embolism spreading through a pit membrane is still lacking. Here, we hypothesized that the pit membranes normally remain to be wetted in xylem vessels and noticed in particular the hydraulic role of water film on air spreading that has been overlooked previously. For the hydrodynamic study of the embolism spreading through a wetted pit membrane, we investigated the penetration and spreading dynamics of air plugs through the wetted cellulose membrane in a channel flow. Air spreading exhibits two types of dynamics: continuous and discrete spreading. We elucidated the correlation of dynamic characteristics of air flow and pressure variations according to membrane thickness. Our study speculates that the thickness of pit membranes affects the behaviors of water film captured by cellulose fibers, and it is a crucial criterion for the reversible gating of further spreading of embolism throughout xylem networks.

Learning-based automatic sensing and size classification of microparticles using smartphone holographic microscopy.

The accurate and fast size classification of microparticles is important in environmental monitoring and biomedical applications. Conventional methods for sensing and classifying microparticles require bulky optical setups and generally show medium performance. Accordingly, the development of a portable and smart platform for accurate particle size classification is essential. In this study, we propose a new sensing platform for automatic identification of microparticle types through the synergistic integration of smartphone-based digital in-line holographic microscopy (DIHM) and machine-learning algorithms. The smartphone-based DIHM system consists of a coherent laser beam, a pinhole, a sample holder, a three-dimensional printed attachment, and a modified built-in smartphone camera module. The portable device has a physical dimension of 4 × 8 × 10 cm3 and 220 g in weight. Holograms of various polystyrene microparticles with different sizes (d = 2-50 µm) were recorded with a wide field-of-view and high spatial resolution. To establish a proper classification model, tens of features including geometrical parameters and light-intensity distributions were extracted from holograms of individual particles, and five machine-learning algorithms were used. After examining the performance of several classifiers, the resulting support vector machine model trained by using three geometrical parameters and three extracted parameters from light-intensity distributions shows the highest accuracy in the particle classification of the training and test sets (>98%). Therefore, the developed handheld smartphone-based platform can be potentially utilized to cope with various imaging needs in mobile healthcare and environmental monitoring.

Removal of fine particulate matter (PM2.5) via atmospheric humidity caused by evapotranspiration.

Reduction of particulate matter (PM) has emerged as one of the most significant challenges in public health and environment protection worldwide. To address PM-related problems and effectively remove fine particulate matter (PM2.5), environmentalists proposed tree planting and afforestation as eco-friendly strategies. However, the PM removal effect of plants and its primary mechanism remains uncertain. In this study, we experimentally investigated the PM removal performance of five plant species in a closed chamber and the effects of relative humidity (RH) caused by plant evapotranspiration, as a governing parameter. On the basis of the PM removal test for various plant species, we selected Epipremnum aureum (Scindapsus) as a representative plant to identify the PM removal efficiency depending on evapotranspiration and particle type. Results showed that Scindapsus yielded a high PM removal efficiency for smoke type PM2.5 under active transpiration. We examined the correlation of PM removal and relative humidity (RH) and evaluated the increased effect of RH on PM2.5 removal by using a plant-inspired in vitro model. Based on the present results, the increase of RH due to evapotranspiration is crucial to the reduction of PM2.5 using plants.

Machine learning-based in-line holographic sensing of unstained malaria-infected red blood cells.

Accurate and immediate diagnosis of malaria is important for medication of the infectious disease. Conventional methods for diagnosing malaria are time consuming and rely on the skill of experts. Therefore, an automatic and simple diagnostic modality is essential for healthcare in developing countries that lack the expertise of trained microscopists. In the present study, a new automatic sensing method using digital in-line holographic microscopy (DIHM) combined with machine learning algorithms was proposed to sensitively detect unstained malaria-infected red blood cells (iRBCs). To identify the RBC characteristics, 13 descriptors were extracted from segmented holograms of individual RBCs. Among the 13 descriptors, 10 features were highly statistically different between healthy RBCs (hRBCs) and iRBCs. Six machine learning algorithms were applied to effectively combine the dominant features and to greatly improve the diagnostic capacity of the present method. Among the classification models trained by the 6 tested algorithms, the model trained by the support vector machine (SVM) showed the best accuracy in separating hRBCs and iRBCs for training (n = 280, 96.78%) and testing sets (n = 120, 97.50%). This DIHM-based artificial intelligence methodology is simple and does not require blood staining. Thus, it will be beneficial and valuable in the diagnosis of malaria.

Label-free sensor for automatic identification of erythrocytes using digital in-line holographic microscopy and machine learning.

Cell types of erythrocytes should be identified because they are closely related to their functionality and viability. Conventional methods for classifying erythrocytes are time consuming and labor intensive. Therefore, an automatic and accurate erythrocyte classification system is indispensable in healthcare and biomedical fields. In this study, we proposed a new label-free sensor for automatic identification of erythrocyte cell types using a digital in-line holographic microscopy (DIHM) combined with machine learning algorithms. A total of 12 features, including information on intensity distributions, morphological descriptors, and optical focusing characteristics, is quantitatively obtained from numerically reconstructed holographic images. All individual features for discocytes, echinocytes, and spherocytes are statistically different. To improve the performance of cell type identification, we adopted several machine learning algorithms, such as decision tree model, support vector machine, linear discriminant classification, and k-nearest neighbor classification. With the aid of these machine learning algorithms, the extracted features are effectively utilized to distinguish erythrocytes. Among the four tested algorithms, the decision tree model exhibits the best identification performance for the training sets (n = 440, 98.18%) and test sets (n = 190, 97.37%). This proposed methodology, which smartly combined DIHM and machine learning, would be helpful for sensing abnormal erythrocytes and computer-aided diagnosis of hematological diseases in clinic.

Label-Free Sensing and Classification of Old Stored Blood.

Transfusion is crucial in surgical operation and anemia treatment. However, several hemorheological properties of blood are adversely altered during blood storage. After transfusion, these adverse alterations are related with decrease of oxygen and ion transport in blood circulation, which increase the mortality of transfused patients. Therefore, accurate sensing of whether a blood supply is still viable for transfusion or not is extremely important. In this study, a H-shaped microfluidic device and digital in-line holographic microscopy were employed to measure temporal variations of blood viscosity and the optical focusing property of erythrocytes during blood storage. Stored rat blood samples separately preserved in citrate phosphate dextrose adenine-1 (CPDA-1) and ethylenediaminetetraacetic acid (EDTA) underwent considerable changes in their biophysical parameters after 2 weeks. Compared with EDTA, CPDA-1 preserves the hemorheological properties of stored blood more effectively. We propose new criteria for depository period of stored blood and indexes, such as viscosity and focal length of erythrocytes, to determine its appropriateness for transfusion. These criteria and indexes can be effectively used for high-throughput prescreening to reduce the risk of transfusion of aged blood or diagnose hematological diseases.

Focusing and alignment of erythrocytes in a viscoelastic medium.

Viscoelastic fluid flow-induced cross-streamline migration has recently received considerable attention because this process provides simple focusing and alignment over a wide range of flow rates. The lateral migration of particles depends on the channel geometry and physicochemical properties of particles. In this study, digital in-line holographic microscopy (DIHM) is employed to investigate the lateral migration of human erythrocytes induced by viscoelastic fluid flow in a rectangular microchannel. DIHM provides 3D spatial distributions of particles and information on particle orientation in the microchannel. The elastic forces generated in the pressure-driven flows of a viscoelastic fluid push suspended particles away from the walls and enforce erythrocytes to have a fixed orientation. Blood cell deformability influences the lateral focusing and fixed orientation in the microchannel. Different from rigid spheres and hardened erythrocytes, deformable normal erythrocytes disperse from the channel center plane, as the flow rate increases. Furthermore, normal erythrocytes have a higher angle of inclination than hardened erythrocytes in the region near the side-walls of the channel. These results may guide the label-free diagnosis of hematological diseases caused by abnormal erythrocyte deformability.

Precise measurement of orientations of transparent ellipsoidal particles through digital holographic microscopy.

A method to measure the orientations of transparent ellipsoidal particles using digital holographic microscopy (DHM) is proposed in this study. This approach includes volumetric recording and numerical reconstruction at different depths. Distinctive light scatterings from an ellipsoid with different angles of orientation are analyzed. A focus function is applied to obtain a reconstructed image that contains a bright line parallel to the major axis of the projected particle, which provides in-plane orientation information. An intensity profile is collected along the major axis of the projected particle in the direction of the optical axis, and this profile is then utilized to measure the out-of-plane orientation of the ellipsoid. After being verified for an ellipsoid with known orientations, the proposed method is applied to ellipsoids suspended in a pipe flow with random orientations. This DHM method can extract the essential information of ellipsoids and therefore has great potential applications in particle dynamics.