Rheological Behavior of Phase Change Slurries for Thermal Energy Applications.

Phase change materials that leverage the latent heat of solid-liquid transition have many applications in thermal energy transport and storage. When employed as particles within a carrier fluid, the resulting phase change slurries (PCSs) could outperform present-day single-phase working fluids─provided that viscous losses can be minimized. This work investigates the rheological behavior of encapsulated and nonencapsulated phase change slurries (PCSs) for applicability in flowing thermal energy systems. The physical and thermal properties of the PCS candidates, along with their rheological behavior, are investigated below and above their phase transition points at shear rates of 1-300 s-1, temperatures of 20-80 °C, and concentrations of 15-37.5 wt %. The effect of shell robustness and melting on local shear thickening and global shear thinning is discussed, followed by an analysis of the required pumping power. A hysteresis analysis is performed to test the transient response of the PCS under a range of shear rates. We assess the complex viscoelastic behavior by employing oscillatory flow tests and by delineating the flow indices─flow consistency index (K) and flow behavior index (n). We identify a viscosity limit of 0.1 Pa·s for optimal thermal performance in high-flow applications such as renewable geothermal energy.

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.

Exploring Anomalous Fluid Behavior at the Nanoscale: Direct Visualization and Quantification via Nanofluidic Devices.

Nanofluidics is the study of fluids under nanoscale confinement, where small-scale effects dictate fluid physics and continuum assumptions are no longer fully valid. At this scale, because of large surface-area-to-volume ratios, the fluid interaction with boundaries becomes more pronounced, and both short-range steric/hydration forces and long-range van der Waals forces and electrostatic forces dictate fluid behavior. These forces lead to a spectrum of anomalous transport and thermodynamic phenomena such as ultrafast water flow, enhanced ion transport, extreme phase transition temperatures, and slow biomolecule diffusion, which have been the subject of extensive computational studies. Experimental quantification of these phenomena was also enabled by the advent of nanofluidic technology, which has transformed challenging nanoscale fluid measurements into facile optical and electrical recordings. Our groups' focus is to investigate nanoscale (2 to 103 nm) fluid behaviors in the context of fluid mechanics and thermodynamics through the development of novel nanofluidic tools, to examine the applicability of classical equations at the nanoscale, to identify the source of deviations, and to explore new physics emerging at this scale. In this Account, we summarize our recent findings regarding liquid transport, vaporization, and condensation of nanoscale-confined liquids. Our study of nanoscale water transport identified an additional resistance in hydrophilic nanochannels, attributed to the reduced cross-sectional area caused by the formation of an immobile hydration layer on the surfaces. In contrast, a reduction in flow resistance was discovered in graphene-coated hydrophobic nanochannels, due to water slippage on the graphene surface. In the context of vaporization, the kinetic-limited evaporation flux was measured and found to exceed the classical theoretical prediction by an order of magnitude in hydrophilic nanochannels/nanopores as a result of the thin film evaporation outside of the apertures. This factor was eliminated by modifying the hydrophobicity of the aperture's exterior surface, enabling the identification of the true kinetic limits inside nanoconfinements and a crucial confinement-dependent evaporation coefficient. The transport-limited evaporation dynamics was also quantified, where experimental results confirmed the parallel diffusion-convection resistance model in both single nanoconduits and nanoporous systems at high accuracy. Furthermore, we have extended our studies to different aspects of condensation in nanoscale-confined spaces. The initiation of condensation for a single-component hydrocarbon was observed to follow the Kelvin equation, whereas for hydrocarbon mixtures it deviated from classical theory because of surface-selective adsorption, which has been corroborated by simulations. Moreover, the condensation dynamics deviates from the bulk and is governed by either vapor transport or liquid transport depending on the confinement scale. Overall, by using novel nanofluidic devices and measurement strategies, our work explores and further verifies the applicability of classical fluid mechanics and thermodynamic equations such as the Navier-Stokes, Kelvin, and Hertz-Knudsen equations at the nanoscale. The results not only deepen our understanding of the fundamental physical phenomena of nanoscale fluids but also have important implications for various industrial applications such as water desalination, oil extraction/recovery, and thermal management. Looking forward, we see tremendous opportunities for nanofluidic devices in probing and quantifying nanoscale fluid thermophysical properties and more broadly enabling nanoscale chemistry and materials science.

Periodic harvesting of microalgae from calcium alginate hydrogels for sustained high-density production.

High-density biomass production is currently only realized in biofilm-based photobioreactors. Harvest yields of whole biofilms are self-limited by daughter-upon-parent cell growth that hinders light and leads to respiratory biomass losses. In this work, we demonstrate a sustainable multi-harvest approach for prolonged generation of high-density biomass. Calcium-alginate hydrogel cultures loaded with Synechococcus elongatus PCC 7942 achieved production densities comparable to that of biofilms (109 cells/mL) and optimal total productivity in harvest periods of 2 or 3 days that allowed high-density surface growth without self-limiting cell buildup or surface death. Cross-linking calcium concentration had a strong influence on surface growth and harvest yields, especially in the first harvests. Subsequent harvests achieved more uniform biomass yields and distributions, unaffected by bulk respiration or light penetration. Collectively, these results demonstrate the feasibility of sustained, high-density biomass production by periodic harvesting within microalgal hydrogel cultures. Biotechnol. Bioeng. 2017;114: 2023-2031. © 2017 Wiley Periodicals, Inc.

The Full Pressure-Temperature Phase Envelope of a Mixture in 1000 Microfluidic Chambers.

Knowing the thermodynamic state of complex mixtures-liquid, gas, supercritical or two-phase-is essential to industrial chemical processes. Traditionally, phase diagrams are compiled piecemeal from individual measurements in a pressure-volume-temperature cell performed in series, where each point is subject to a long fluid equilibrium time. Herein, 1000 microfluidic chambers, each isolated by a liquid piston and set to a different pressure and temperature combination, provide the complete pressure-temperature phase diagram of a hydrocarbon mixture at once, including the thermodynamic phase envelope. Measurements closely match modeled values, with a standard deviation of 0.13 MPa between measurement and model for the dew and bubble point lines, and a difference of 0.04 MPa and 0.25 °C between measurement and model for the critical point.

Direct Measurement of the Fluid Phase Diagram.

The thermodynamic phase of a fluid (liquid, vapor or supercritical) is fundamental to all chemical processes, and the critical point is particularly important for supercritical chemical extraction. Conventional phase measurement methods require hours to obtain a single datum on the pressure and temperature diagram. Here, we present the direct measurement of the full pressure-temperature phase diagram, with 10 000 microwells. Orthogonal, linear, pressure and temperature gradients are obtained with 100 parallel microchannels (spanning the pressure range), each with 100 microwells (spanning the temperature range). The phase-mapping approach is demonstrated with both a pure substance (CO2) and a mixture (95% CO2 + 5% N2). Liquid, vapor, and supercritical regions are clearly differentiated, and the critical pressure is measured at 1.2% error with respect to the NIST standard. This approach provides over 100-fold improvement in measurement speed over conventional methods.

Microfluidic Manufacturing of Polymeric Nanoparticles: Comparing Flow Control of Multiscale Structure in Single-Phase Staggered Herringbone and Two-Phase Reactors.

We compare the microfluidic manufacturing of polycaprolactone-block-poly(ethylene oxide) (PCL-b-PEO) nanoparticles (NPs) in a single-phase staggered herringbone (SHB) mixer and in a two-phase gas-liquid segmented mixer. NPs generated from two different copolymer compositions in both reactors and at three different flow rates, along with NPs generated using a conventional bulk method, are compared with respect to morphologies, dimensions, and internal crystallinities. Our work, the first direct comparison between alternate microfluidic NP synthesis methods, shows three key findings: (i) NP morphologies and dimensions produced in the bulk are different from those produced in a microfluidic mixer, whereas NP crystallinities produced in the bulk and in the SHB mixer are similar; (ii) NP morphologies, dimensions, and crystallinities produced in the single-phase SHB and two-phase mixers at the lowest flow rate are similar; and (iii) NP morphologies, dimensions, and crystallinities change with flow rate in the two-phase mixer but not in the single-phase SHB mixer. These findings provide new insights into the relative roles of mixing and shear in the formation and flow-directed processing of polymeric NPs in microfluidics, informing future reactor designs for manufacturing NPs of low polydispersity and controlled multiscale structure and function.

Fast fluorescence-based microfluidic method for measuring minimum miscibility pressure of CO2 in crude oils.

Carbon capture, storage, and utilization has emerged as an essential technology for near-term CO2 emission control. The largest CO2 projects globally combine storage and oil recovery. The efficiency of this process relies critically on the miscibility of CO2 in crude oils at reservoir conditions. We present a microfluidic approach to quantify the minimum miscibility pressure (MMP) that leverages the inherent fluorescence of crude oils, is faster than conventional technologies, and provides quantitative, operator-independent measurements. To validate the approach, synthetic oil mixtures of known composition (pentane, hexadecane) are tested and MMP values are compared to reported values. Results differ by less than 0.5 MPa on average, in contrast to comparison between conventional methods with variations on the order of 1-2 MPa. In terms of speed, a pressure scan for a single MMP measurement required less than 30 min (with potential to be sub-10 min), in stark contrast to days or weeks with existing approaches. The method is applied to determine the MMP for Pennsylvania, West Texas, and Saudi crudes. Importantly, our fluorescence-based approach enables rapid, automated, operator-independent measurement of MMP as needed to inform the world's largest CO2 projects.

Hollow core photonic crystal fiber as a reusable Raman biosensor.

We report that a single hollow core photonic crystal fiber (HC-PCF) can be used for repetitive characterization of multiple samples by Raman spectroscopy. This was achieved by integrating the HC-PCF to a differential pressure system that allowed effective filling, draining and re-filling of samples into a HC-PCF under identical optical conditions. Consequently, high-quality and reliable spectral data could be obtained which were suitable for multivariate analysis (partial least squares). With the present scheme, we were able to accurately predict different concentrations of heparin and adenosine in serum. Thus the detection scheme as presented here paves a path for the inclusion of HC-PCFs in point-of-care technologies and environmental monitoring where rapid sample characterization is of utmost importance.

Human sperm cooperate to transit highly viscous regions on the competitive pathway to fertilization.

Human sperm compete for fertilization. Here, we find that human sperm, unexpectedly, cooperate under conditions mimicking the viscosity contrasts in the female reproductive tract. Sperm attach at the head region to migrate as a cooperative group upon transit into and through a high viscosity medium (15-100 cP) from low viscosity seminal fluid. Sperm groups benefit from higher swimming velocity, exceeding that of individual sperm by over 50%. We find that sperm associated with a group possess high DNA integrity (7% fragmentation index) - a stark contrast to individual sperm exhibiting low DNA integrity (> 50% fragmentation index) - and feature membrane decapacitation factors that mediate sperm attachment to form the group. Cooperative behaviour becomes less prevalent upon capacitation and groups tend to disband as the surrounding viscosity reduces. When sperm from different male sources are present, related sperm preferentially form groups and achieve greater swimming velocity, while unrelated sperm are slowed by their involvement in a group. These findings reveal cooperation as a selective mode of human sperm motion - sperm with high DNA integrity cooperate to transit the highly viscous regions in the female tract and outcompete rival sperm for fertilization - and provide insight into cooperation-based sperm selection strategies for assisted reproduction.

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.

FertDish: microfluidic sperm selection-in-a-dish for intracytoplasmic sperm injection.

The selection of high quality sperm is critical for intracytoplasmic sperm injection (ICSI), a prevalent assisted reproduction technology. However, standard selection methods are time-consuming and fail to recover the most viable sperm, thereby limiting the ICSI success rate. Microfluidics enables rapid selection of viable sperm in a manner representing in vivo processes, however, existing platforms lack clinical applicability. Here, we present FertDish, which integrates the clinically established ICSI Petri dish with a film featuring an array of sperm-selecting microchannels for selection of sperm directly from semen. The FertDish format mimics the clinician-familiar ICSI dish setup, and provides rapid (<10 min) single stage sperm preparation that circumvents standard labour-intensive multi-stage sperm processing steps. Tests with human donor and patient semen samples show that FertDish enables the selection of a high quality sperm sub-population, featuring improvements in DNA fragmentation index of more than 91% (donor) and 74% (patient) versus raw semen and 50% (donor) and 63% (patient) versus standard methods, and a distribution of more than 97% sperm with viable and high level DNA. The FertDish enables a high sperm recovery rate (>3.3 × 105 sperm per mL), and is readily adaptable to the clinical workflow with potential to improve ICSI outcomes.

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.

When robotics met fluidics.

High-throughput fluidic technologies have increased the speed and accuracy of fluid processing to the extent that unlocking further gains will require replacing the human operator with a robotic counterpart. Recent advances in chemistry and biology, such as gene editing, have further exacerbated the need for smart, high-throughput experimentation. A growing number of innovations at the intersection of robotics and fluidics illustrate the tremendous opportunity in achieving fully self-driving fluid systems. We envision that the fields of synthetic chemistry and synthetic biology will be the first beneficiaries of AI-directed robotic and fluidic systems, and largely fall within two modalities: complex integrated centralized facilities that produce data, and distributed systems that synthesize products and conduct disease surveillance.

Deep learning for the classification of human sperm.

BACKGROUND: Infertility is a global health concern, and couples are increasingly seeking medical assistance to achieve reproduction. Semen analysis is a primary assessment performed by a clinician, in which the morphology of the sperm population is evaluated. Machine learning algorithms that automate, standardize, and expedite sperm classification are the subject of ongoing research. METHOD: We demonstrate a deep learning method to classify sperm into one of several World Health Organization (WHO) shape-based categories. Our method uses VGG16, a deep convolutional neural network (CNN) initially trained on ImageNet, a collection of human-annotated everyday images, which we retrain for sperm classification using two freely-available sperm head datasets (HuSHeM and SCIAN). RESULTS: Our deep learning approach classifies sperm at high accuracy and performs well in head-to-head comparisons with earlier approaches using identical datasets. We demonstrate improvement in true positive rate over a classifier approach based on a cascade ensemble of support vector machines (CE-SVM) and show similar true positive rates as compared to an adaptive patch-based dictionary learning (APDL) method. Retraining an off-the-shelf VGG16 network avoids excessive neural network computation or having to learn and use the massive dictionaries required for sparse representation, both of which can be computationally expensive. CONCLUSIONS: We show that our deep learning approach to sperm head classification represents a viable method to automate, standardize, and accelerate semen analysis. Our approach highlights the potential of artificial intelligence technologies to eventually exceed human experts in terms of accuracy, reliability, and throughput.

Deep Learning with Microfluidics for Biotechnology.

Advances in high-throughput and multiplexed microfluidics have rewarded biotechnology researchers with vast amounts of data but not necessarily the ability to analyze complex data effectively. Over the past few years, deep artificial neural networks (ANNs) leveraging modern graphics processing units (GPUs) have enabled the rapid analysis of structured input data - sequences, images, videos - to predict complex outputs with unprecedented accuracy. While there have been early successes in flow cytometry, for example, the extensive potential of pairing microfluidics (to acquire data) and deep learning (to analyze data) to tackle biotechnology challenges remains largely untapped. Here we provide a roadmap to integrating deep learning and microfluidics in biotechnology laboratories that matches computational architectures to problem types, and provide an outlook on emerging opportunities.

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.

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.

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.

Capillary Condensation in 8 nm Deep Channels.

Condensation on the nanoscale is essential to understand many natural and synthetic systems relevant to water, air, and energy. Despite its importance, the underlying physics of condensation initiation and propagation remain largely unknown at sub-10 nm, mainly due to the challenges of controlling and probing such small systems. Here we study the condensation of n-propane down to 8 nm confinement in a nanofluidic system, distinct from previous studies at ∼100 nm. The condensation initiates significantly earlier in the 8 nm channels, and it initiates from the entrance, in contrast to channels just 10 times larger. The condensate propagation is observed to be governed by two liquid-vapor interfaces with an interplay between film and bridging effects. We model the experimental results using classical theories and find good agreement, demonstrating that this 8 nm nonpolar fluid system can be treated as a continuum from a thermodynamic perspective, despite having only 10-20 molecular layers.