Spontaneous Rise of Hydrogen Microbubbles in Interfacial Gas Evolution Reaction.

Liquid organic hydrogen carrier is a promising option for the transport and storage of hydrogen as a clean energy source. This study examines the stability and behavior of organic drops immobilized on a substrate during an interfacial hydrogen-evolution reaction (HER) at the drop surface and its surrounding aqueous solution. Hydrogen microbubbles form within the drop and rise to the drop apex. The growth rate of the hydrogen in-drop bubble increases with the concentration of the reactant in the surrounding medium. The drop remains stable till the buoyancy acting on the in-drop bubble is large enough to overcome the capillary force and the external viscous drag. The bubble spontaneously rises and carries a portion drop liquid to the solution surface. These spontaneous rising in-drop bubbles are detected in measurements using a high-precision sensor placed on the upper surface of the aqueous solution, reversing the settling phase from phase separation in the reactive emulsion. The finding from this work provides new insights into the behaviors of drops and bubbles in many interfacial gas evolution reactions in clean technologies.

Microfluidic Detection and Analysis of Microplastics Using Surface Nanodroplets.

Detection of microplastics from water is crucial for various reasons, such as food safety monitoring, monitoring of the fate and transport of microplastics, and development of preventive measures for their occurrence. Currently, microplastics are detected by isolating them using filtration, separation by centrifugation, or membrane filtration, subsequently followed by analysis using well-established analytical methods, such as Raman spectroscopy. However, due to their variability in shape, color, size, and density, isolation using the conventional methods mentioned above is cumbersome and time-consuming. In this work, we show a surface-nanodroplet-decorated microfluidic device for isolation and analysis of small microplastics (diameter of 10 µm) from water. Surface nanodroplets are able to capture nearby microplastics as water flows through the microfluidic device. Using a model microplastic solution, we show that microplastics of various sizes and types can be captured and visualized by using optical and fluorescence microscopy. More importantly, as the surface nanodroplets are pinned on the microfluidic channel, the captured microplastics can also be analyzed using a Raman spectroscope, which enables both physical (i.e., size and shape) and chemical (i.e., type) characterization of microplastics at a single-particle level. The technique shown here can be used as a simple, fast, and economical detection method for small microplastics.

Determination of Trace Organic Contaminant Concentration via Machine Classification of Surface-Enhanced Raman Spectra.

Surface-enhanced Raman spectroscopy (SERS) has been well explored as a highly effective characterization technique that is capable of chemical pollutant detection and identification at very low concentrations. Machine learning has been previously used to identify compounds based on SERS spectral data. However, utilization of SERS to quantify concentrations, with or without machine learning, has been difficult due to the spectral intensity being sensitive to confounding factors such as the substrate parameters, orientation of the analyte, and sample preparation technique. Here, we demonstrate an approach for predicting the concentration of sample pollutants from SERS spectra using machine learning. Frequency domain transform methods, including the Fourier and Walsh-Hadamard transforms, are applied to spectral data sets of three analytes (rhodamine 6G, chlorpyrifos, and triclosan), which are then used to train machine learning algorithms. Using standard machine learning models, the concentration of the sample pollutants is predicted with >80% cross-validation accuracy from raw SERS data. A cross-validation accuracy of 85% was achieved using deep learning for a moderately sized data set (∼100 spectra), and 70-80% was achieved for small data sets (∼50 spectra). Performance can be maintained within this range even when combining various sample preparation techniques and environmental media interference. Additionally, as a spectral pretreatment, the Fourier and Hadamard transforms are shown to consistently improve prediction accuracy across multiple data sets. Finally, standard models were shown to accurately identify characteristic peaks of compounds via analysis of their importance scores, further verifying their predictive value.

High-throughput sperm DNA analysis at the single-cell and population levels.

Clinical semen quality assessment is critical to the treatment of infertility. Sperm DNA integrity testing provides critical information that can steer treatment and influence outcomes and offspring health. Flow cytometry is the gold standard approach to assess DNA integrity, but it is not commonly applied at the clinical level. The sperm chromatin dispersion (SCD) assay provides a simpler and cheaper alternative. However, SCD is low-throughput and non-quantitative - sperm assessment is serial, manual and suffers inter- and intra-observer variations. Here, an automated SCD analysis method is presented that enables quantitative sperm DNA quality assessment at the single-cell and population levels. Levering automated optical microscopy and a chromatin diffusion-based analysis, a sample of thousands of sperm that would otherwise require 5 hours is assessed in under 10 minutes - a clinically viable workflow. The sperm DNA diffusion coefficient (DDNA) measurement correlates (R2 = 0.96) with DNA fragmentation index (DFI) from the cytometry-based sperm chromatin structure assay (SCSA). The automated measurement of population-level sperm DNA fragmentation (% sDF) prevents inter-observer variations and shows a good agreement with the SCSA % DFI (R2 = 0.98). This automated approach standardizes and accelerates SCD-based sperm DNA analysis, enabling the clinical application of sperm DNA integrity assessment.

Microfluidic formation of surface nanodroplets using green deep eutectic solvents for liquid-liquid nanoextraction and controlled precipitation.

HYPOTHESIS: Surface nanodroplets have recently been employed for in situ chemical analysis leveraging their low volume, e.g. O(10-15 L), that enables rapid analyte extraction and pre-concentration. So far, most surface nanodroplets have been formed using single organic solvents such as 1-octanol, toluene, among others. Designing multicomponent surface nanodroplet with controllable composition is highly desirable for extending their application as extractant. EXPERIMENT: Here, we formed surface nanodroplets using green deep eutectic solvent (gDES) composed of thymol and decanoic acid, both of which are naturally occurring chemicals. The influence of parameters such as flowrate and the composition of deep eutectic solvent on the surface nanodroplet formation were studied. As proof-of-concept, the gDES surface nanodroplets were further used to extract and detect trace amounts of fluorescent rhodamine 6G dye and copper ions from water. FINDINGS: The formation of gDES surface nanodroplets follows the theoretical model which states that the final droplet volume (Vf) scales with the Peclét number (Pe) of the flow during formation by the solvent exchange process, that is Vf âˆ¼ Pe3/4, and the nanodroplets demonstrate excellent ability as extractant for rhodamine 6G and copper ions from water. Surprisingly, the confined volume of gDES surface nanodroplets enables fast and controlled formation of Cu (II)-decanoate crystal.

Surface Nanodroplet-Based Extraction Combined with Offline Analytic Techniques for Chemical Detection and Quantification.

Liquid-liquid extraction based on surface nanodroplets can be a green and sustainable technique to extract and concentrate analytes from a sample flow. However, because of the extremely small volume of each droplet (<10 fL, tens of micrometers in base radius and a few or less than 1 µm in height), only a few in situ analytical techniques, such as surface-enhanced Raman spectroscopy, were applicable for the online detection and analysis based on nanodroplet extraction. To demonstrate the versatility of surface nanodroplet-based extraction, in this work, the formation of octanol surface nanodroplets and extraction were performed inside a 3 m Teflon capillary tube. After extraction, surface nanodroplets were collected by injecting air into the tube, by which the contact line of surface droplets was collected by the capillary force. As the capillary allows for the formation of ∼1012 surface nanodroplets on the capillary wall, ≥2 mL of octanol can be collected after extraction. The volume of the collected octanol was enough for the analysis of offline analytical techniques such as UV-vis, GC-MS, and others. Coupled with UV-vis, reliable extraction and detection of two common water pollutants, triclosan and chlorpyrifos, was shown by a linear relationship between the analyte concentration in the sample solution and UV-vis absorbance. Moreover, the limit of detection (LOD) as low as 2 × 10-9 M for triclosan (∼0.58 µg/L) and 3 × 10-9 M for chlorpyrifos (∼1.05 µg/L) could be achieved. The collected surface droplets were also analyzed via gas chromatography (GC) and fluorescence microscopy. Our work shows that surface nanodroplet extraction may potentially streamline the process in sample pretreatment for sensitive chemical detection and quantification by using common analytic tools.

Propelling microdroplets generated and sustained by liquid-liquid phase separation in confined spaces.

Flow transport in confined spaces is ubiquitous in technological processes, ranging from separation and purification of pharmaceutical ingredients by microporous membranes and drug delivery in biomedical treatment to chemical and biomass conversion in catalyst-packed reactors and carbon dioxide sequestration. In this work, we suggest a distinct pathway for enhanced liquid transport in a confined space via propelling microdroplets. These microdroplets can form spontaneously from localized liquid-liquid phase separation as a ternary mixture is diluted by a diffusing poor solvent. High speed images reveal how the microdroplets grow, break up and propel rapidly along the solid surface, with a maximal velocity up to ∼160 µm s-1, in response to a sharp concentration gradient resulting from phase separation. The microdroplet propulsion induces a replenishing flow between the walls of the confined space towards the location of phase separation, which in turn drives the mixture out of equilibrium and leads to a repeating cascade of events. Our findings on the complex and rich phenomena of propelling droplets suggest an effective approach to enhanced flow motion of multicomponent liquid mixtures within confined spaces for time effective separation and smart transport processes.

Selection of high-quality sperm with thousands of parallel channels.

Sperm selection is essential for successful fertilization and embryo development. Current clinical sperm selection methods are labor-intensive and lack the selectivity required to isolate high-quality sperm. Microfluidic sperm selection approaches have shown promise but present a trade-off between the quality and quantity of selected sperm - clinicians demand both. The structure of the female reproductive tract helps to isolate a sufficient quantity of high-quality sperm for fertilization with densely folded epithelium that provides a multitude of longitudinally oriented pathways that guide sperm toward the fertilization site. Here, a three-dimensionally structured sperm selection device is presented that levers this highly parallelized in vivo mechanism for in vitro sperm selection. The device is inserted in a test tube atop 1 mL of raw semen and provides 6500 channels that isolate ∼100 000 high-DNA-integrity sperm for assisted reproduction. In side-by-side clinical testing, the developed approach outperforms the best current clinical methods by improving the DNA integrity of the selected sperm subpopulation up to 95%. Also, the device streamlines clinical workflow, reducing the time required for sperm preparation 3-fold. This single-tube, single-step sperm preparation approach promises to improve both the economics and outcomes of assisted reproduction practices, especially in cases with significant male-factors.

Surface nanodroplet-based nanoextraction from sub-milliliter volumes of dense suspensions.

A greener analytical technique for quantifying compounds in dense suspensions is needed for wastewater and environmental analysis, chemical or bio-conversion process monitoring, biomedical diagnostics, and food quality control, among others. In this work, we introduce a green, fast, one-step method called nanoextraction for extraction and detection of target analytes from sub-milliliter dense suspensions using surface nanodroplets without toxic solvents and pre-removal of the solid contents. With nanoextraction, we achieve a limit of detection (LOD) of 10-9 M for a fluorescent model analyte obtained from a particle suspension sample. The LOD is lower than that in water without particles (10-8 M), potentially due to the interaction of particles and the analyte. The high particle concentration in the suspension sample, thus, does not reduce the extraction efficiency, although the extraction process was slowed down up to 5 min. As a proof of principle, we demonstrate the nanoextraction for the quantification of model compounds in wastewater slurry containing 30 wt% solids and oily components (i.e. heavy oils). The nanoextraction and detection technology developed in this work may be used in fast analytical technologies for complex slurry samples in the environment, industrial waste, or in biomedical diagnostics.

Machine learning for sperm selection.

Infertility rates and the number of couples seeking fertility care have increased worldwide over the past few decades. Over 2.5 million cycles of assisted reproductive technologies are being performed globally every year, but the success rate has remained at ~33%. Machine learning, an automated method of data analysis based on patterns and inference, is increasingly being deployed within the health-care sector to improve diagnostics and therapeutics. This technique is already aiding embryo selection in some fertility clinics, and has also been applied in research laboratories to improve sperm analysis and selection. Tremendous opportunities exist for machine learning to advance male fertility treatments. The fundamental challenge of sperm selection - selecting the most promising candidate from 108 gametes - presents a challenge that is uniquely well-suited to the high-throughput capabilities of machine learning algorithms paired with modern data processing capabilities.

Microfluidic device coupled with total internal reflection microscopy for in situ observation of precipitation.

In situ observation of precipitation or phase separation induced by solvent addition is important in studying its dynamics. Combined with optical and fluorescence microscopy, microfluidic devices have been leveraged in studying the phase separation in various materials including biominerals, nanoparticles, and inorganic crystals. However, strong scattering from the subphases in the mixture is problematic for in situ study of phase separation with high temporal and spatial resolution. In this work, we present a quasi-2D microfluidic device combined with total internal reflection microscopy as an approach for in situ observation of phase separation. The quasi-2D microfluidic device comprises of a shallow main channel and a deep side channel. Mixing between a solution in the main channel (solution A) and another solution (solution B) in the side channel is predominantly driven by diffusion due to high fluid resistance from the shallow height of the main channel, which is confirmed using fluorescence microscopy. Moreover, relying on diffusive mixing, we can control the composition of the mixture in the main channel by tuning the composition of solution B. We demonstrate the application of our method for in situ observation of asphaltene precipitation and [Formula: see text]-alanine crystallization.

Effects of Chemical and Geometric Microstructures on the Crystallization of Surface Droplets during Solvent Exchange.

In this work, we investigate the crystallization of droplets formed on micropatterned surfaces. By solvent exchange in a microchamber, a ternary solution consisting of a model compound ß-alanine, water, and isopropanol was displaced by a flow of isopropanol. In the process, oiling-out droplets formed and crystallized. Our results showed that the shape and size of the crystals on surfaces with chemical micropatterns could be simply mediated by the flow conditions of solvent exchange. More uniform crystals formed on hydrophilic microdomains compared to hydrophobic microdomains or homogeneous surfaces. Varying flow rates or channel heights led to the formation of thin films with microholes, connected networks of crystals, or small diamond-shaped crystals. Physical microstructures (represented by microlenses) on the surface allowed the easy detachment of crystals from the surface. Beyond oiling-out crystallization, we demonstrated that the crystal formation of another solute dissolved in the droplets could be triggered by solvent exchange. The length of crystal fibers after the solvent-exchange process was shorter at a faster flow rate. This study may provide further understanding to effectively obtain the crystallization of surface droplets through the solvent-exchange approach.

Nanoadhesive layer to prevent protein absorption in a poly(dimethylsiloxane) microfluidic device.

Poly(dimethylsiloxane) (PDMS) is widely used as a microfluidics platform material; however, it absorbs various molecules, perturbing specific chemical concentrations in microfluidic channels. We present a simple solution to prevent adsorption into a PDMS microfluidic device. We used a vapor-phase-deposited nanoadhesive layer to seal PDMS microfluidic channels. Absorption of fluorescent molecules into PDMS was efficiently prevented in the nanolayer-treated PDMS device. Importantly, when cultured in a nanolayer-treated PDMS device, yeast cells exhibited the expected concentration-dependent response to a mating pheromone, including mating-specific morphological and gene expression changes, while yeast cultured in an untreated PDMS device did not properly respond to the pheromone. Our method greatly expands microfluidic applications that require precise control of molecule concentrations.

Identification of Microfibers in the Environment Using Multiple Lines of Evidence.

Microfibers, characterized as small fibers shed from textiles that are less than 5 mm in size, are a prominent contaminant in the environment. Thus, it is important that we have methods to accurately quantify and characterize them, including in water, sediment, wildlife, seafood, and drinking water samples. Unfortunately, their small size and the presence of different dyes on the microfibers themselves cause difficulties in identification via conventional spectroscopic methods of total attenuated resonance-Fourier transform infrared and Raman. To help solve some of these methodological challenges, we developed a new method employing polymer-dye binding chemistry, density tests, unique surface morphological traits, and fluorescent staining to identify microfibers in environmental samples. The identification method introduced here was tested in our laboratory via trials using microfibers shed from new textiles and environmental samples. We found that the method can be successfully applied to identify the different polymer types of microfibers, which can ultimately help source apportion microfiber contamination in the environment.

Prediction of DNA Integrity from Morphological Parameters Using a Single-Sperm DNA Fragmentation Index Assay.

Intracytoplasmic sperm injection is a popular form of in vitro fertilization, where single sperm are selected by a clinician and injected into an egg. Whereas clinicians employ general morphology-based guidelines to select the healthiest-looking sperm, it remains unclear to what extent an individual sperm's physical parameters correlate with the quality of internal DNA cargo-a measurement that cannot be obtained without first damaging the sperm. Herein, a single-cell DNA fragmentation index (DFI) assay is demonstrated, which combines the single-cell nature of the acridine orange test with the quantitative aspect of the sperm chromatin structure assay, to create a database of DFI-scored brightfield images. Two regression predictive models, linear and nonlinear regression, are used to quantify the correlations between individual sperm morphological parameters and DFI score (with model test r at 0.558 and 0.620 for linear and nonlinear regression models, respectively). The sample is also split into two categories of either relatively good or bad DFIs and a classification predictive model based on logistic regression is used to categorize sperm, resulting in a test accuracy of 0.827. Here, the first systematic study is presented on the correlation and prediction of sperm DNA integrity from morphological parameters at the single-cell level.

Deep learning-based selection of human sperm with high DNA integrity.

Despite the importance of sperm DNA to human reproduction, currently no method exists to assess individual sperm DNA quality prior to clinical selection. Traditionally, skilled clinicians select sperm based on a variety of morphological and motility criteria, but without direct knowledge of their DNA cargo. Here, we show how a deep convolutional neural network can be trained on a collection of ~1000 sperm cells of known DNA quality, to predict DNA quality from brightfield images alone. Our results demonstrate moderate correlation (bivariate correlation ~0.43) between a sperm cell image and DNA quality and the ability to identify higher DNA integrity cells relative to the median. This deep learning selection process is directly compatible with current, manual microscopy-based sperm selection and could assist clinicians, by providing rapid DNA quality predictions (under 10 ms per cell) and sperm selection within the 86th percentile from a given sample.

Two-dimensional planar swimming selects for high DNA integrity sperm.

Selection of high-quality sperm is critical to the success of assisted reproductive technologies. Clinical screening for top sperm has long focused on sperm swimming ability when following boundaries or when fully free of constraints. In this work, we demonstrate a sperm selection approach with parallel 2 µm tall confined selection channels that prohibit rotation of the sperm head and require planar swimming. We demonstrate that a planar swimming subpopulation of sperm capable of entering and navigating these channels has DNA integrity superior to the freely-swimming motile or raw sperm populations over a wide range of semen sample qualities. The DNA integrity of the selected sperm was significantly higher than that of the corresponding raw samples for donor samples and clinical patient samples, respectively. In side-by-side testing, this method outperforms current clinical selection methods, density gradient centrifugation and swim-up, as well as sperm selected via general motility. Planar swimming represents a viable sperm selection mechanism with the potential to improve outcomes for couples and offspring.

Accessory-free quantitative smartphone imaging of colorimetric paper-based assays.

The combination of smartphone technology and colorimetric paper-based microfluidics can enable simple, inexpensive diagnostics. However, imaging colorimetric diagnostic results via smartphones currently requires accessories to mitigate the influence of variability in surrounding lighting conditions. Here, we present an accessory-free smartphone-based colorimetric imaging method that enlists the built-in LED light source to dominate ambient lighting in combination with background and colour rescaling. This simple approach enables quantitative measurements from paper-based tests by compensating for different environmental lighting conditions and is universally applicable with respect to phone models and manufacturers. We demonstrate the method with three dominant phone makes and models in a cell counting application with a paper-based yeast detection device. The detection results are in good agreement with cell counting using automated cell counters. Eliminating the need for make/model specific accessories, this approach helps realize the potential for low-cost, broadly applicable paper-based diagnostics.

Live sperm trap microarray for high throughput imaging and analysis.

There is a growing appreciation and understanding of cell-to-cell variability in biological samples. However, research and clinical practice in male fertility has relied on population, or sample-based characteristics. Single-cell resolution is particularly important given the winner-takes-all nature of both natural and in vitro fertilization: it is the properties of a single cell, not the population, that are passed to the next generation. While there are a range of methods for single cell analysis, arraying a larger number of live sperm has not been possible due to the strong locomotion of the cells. Here we present a 103-trap microarray that traps, aligns and arrays individual live sperm. The method enables high-resolution imaging of the aligned cell head, the application of dye-based DNA and mitochondrial analyses, and the quantification of motility characteristics, such as tail beat. In testing, a 2400-post array trapped ∼400 sperm for individual analyses of tail beating frequency and amplitude, DNA integrity via acridine orange staining, and mitochondrial activity via staining. While literature results are mixed regarding a possible correlation between motility and DNA integrity of sperm at sample-level, results here find no statistical correlation between tail beat characteristics and DNA integrity at the cell-level. The trap array uniquely enables the high-throughput study of individual live sperm in semen samples - assessing the inherently single-cell selection process of fertilization, with single-cell resolution.

An efficient isolation of foodborne pathogen using surface-modified porous sponge.

Rapid and efficient detection of pathogenic bacteria from food is critical to prevent epidemic food poisoning. However, the isolation of pathogenic bacteria from spoiled food is hampered by the lack of proper cell cultivation and/or isolation methods. Most of currently used methods suffer from complex, time-consuming culturing steps, low scalability, and high operation cost. Herein, we developed an alternative approach for the isolation of pathogenic bacteria directly from food using a surface-modified, highly porous sponge via initiated chemical vapor deposition (iCVD) process. A hydrophobic polymer, poly(2,4,6,8-tetravinyl-2,4,6,8-tetramethyl cyclotetra-siloxane) (pV4D4), was deposited conformally on amphiphilic 3-dimensional (3D) melamine sponge to incorporate hydrophobicity as well as oleophilicity to the porous sponge surface, which is appropriate for absorbing oil component selectively from food extracts. Furthermore, the surface-modified sponge was capable of the isolation of Escherichia coli O157:H7 (E. coli O157:H7) from heterogeneous mixture with oil/water/food particles with undistinguisible efficiency compare to artificial model system. The surface-modified sponge developed in this study will be a novel platform for oil/water separation and isolation of foodborne pathogens directly from heterogeneous mixture to enhance the efficiency of molecular diagnostics.