Scaling Law for the Onset of Solidification at Extreme Undercooling.

Quasi-isentropic compression enables one to study the solidification of metastable liquid states that are inaccessible through other experimental means. The onset of this nonequilibrium solidification is known to depend on the compression rate and material-specific factors, but this complex interdependence has not been well characterized. In this study, we use a combination of experiments, theory, and computational simulations to derive a general scaling law that quantifies this dependence. One of its applications is a novel means to elucidate melt temperatures at high pressures.

Model for the Solid-Liquid Interfacial Free Energy at High Pressures.

The free energy involved in the formation of an interface between two phases (e.g., a solid-liquid interface) is referred to as the interfacial free energy. For the case of solidification, the interfacial free energy dictates the height of the energy barrier required to nucleate stable clusters of the newly forming solid phase and is essential for producing an accurate solidification kinetics model using classical nucleation theory (CNT)-based methods. While various methods have been proposed for modeling the interfacial free energy for solid-liquid interfaces in prior literature, many of these formulations involve making restrictive assumptions or approximations, such as the system being at or near equilibrium (i.e., the system temperature is approximately equal to the melt temperature) or that the system is at pressures close to atmospheric. However, these approximations and assumptions may break down in highly non-equilibrium situations, such as in dynamic-compression experiments where metastable liquids that are undercooled by hundreds of kelvin or overpressurized by several gigapascals or more are formed before eventually solidifying. We derive a solid-liquid interfacial free-energy model for such high-pressure conditions by considering the enthalpies of interactions between pairs of atoms or molecules. We also consider the contribution of interface roughness (disordering) by incorporating a multilayer interface model known as the Temkin n-layer model. Our formulation is applicable to a diverse variety of materials, and we demonstrate it by developing models specifically for two different materials: water and gallium. We apply our interfacial free-energy formulation to CNT-based kinetics simulations of several suites of dynamic-compression experiments that cause liquid water to solidify to the high-pressure solid polymorph ice VII and have found good agreement to the observed kinetics with only minor empirical fitting.

Numerical modeling of solid-cluster evolution applied to the nanosecond solidification of water near the metastable limit.

Classical nucleation theory (CNT) is a promising way to predictively model the submicrosecond kinetics of phase transitions that occur under dynamic compression, such as the suite of experiments performed over the past two decades on the solidification of liquid water to the high-pressure ice VII phase. Myint et al. [Phys. Rev. Lett. 121, 155701 (2018)] presented the first CNT-based model for these types of rapid phase transitions, but relied on an empirical scaling parameter in their transient induction model to simulate the lag time that occurs prior to the onset of significant formation of ice VII clusters in the system. To build on that study, we model the liquid water-ice VII phase transformation using a numerical discretization scheme to solve the Zel'dovich-Frenkel partial differential equation, which is a fundamental CNT-based kinetic equation that describes the statistical time-dependent behavior of solid cluster formation. The Zel'dovich-Frenkel equation inherently accounts for transience in the nucleation kinetics and eliminates the need for the empirical scaling factor used by Myint et al. One major result of this research is that transience is found to play a relatively small role in the nucleation process for the dynamic-compression time scales of the liquid water-ice VII experiments being simulated. Instead, we show that it is possible to properly model the lag time using steady-state CNT by making small refinements to the interfacial free energy value. We have also developed a new dimensionless parameter that may be applied a priori to predict whether or not transient nucleation will be important in a given dynamic-compression experiment.

Design and Benchmark Testing for Open Architecture Reconfigurable Mobile Spirometer and Exhaled Breath Monitor with GPS and Data Telemetry.

Portable and wearable medical instruments are poised to play an increasingly important role in health monitoring. Mobile spirometers are available commercially, and are used to monitor patients with advanced lung disease. However, these commercial monitors have a fixed product architecture determined by the manufacturer, and researchers cannot easily experiment with new configurations or add additional novel sensors over time. Spirometry combined with exhaled breath metabolite monitoring has the potential to transform healthcare and improve clinical management strategies. This research provides an updated design and benchmark testing for a flexible, portable, open access architecture to measure lung function, using common Arduino/Android microcontroller technologies. To demonstrate the feasibility and the proof-of-concept of this easily-adaptable platform technology, we had 43 subjects (healthy, and those with lung diseases) perform three spirometry maneuvers using our reconfigurable device and an office-based commercial spirometer. We found that our system compared favorably with the traditional spirometer, with high accuracy and agreement for forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC), and gas measurements were feasible. This provides an adaptable/reconfigurable open access "personalized medicine" platform for researchers and patients, and new chemical sensors and other modular instrumentation can extend the flexibility of the device in the future.

Effect of temperature control on the metabolite content in exhaled breath condensate.

The non-invasive, quick, and safe collection of exhaled breath condensate makes it a candidate as a diagnostic matrix in personalized health monitoring devices. The lack of standardization in collection methods and sample analysis is a persistent limitation preventing its practical use. The collection method and hardware design are recognized to significantly affect the metabolomic content of EBC samples, but this has not been systematically studied. Here, we completed a series of experiments to determine the sole effect of collection temperature on the metabolomic content of EBC. Temperature is a likely parameter that can be controlled to standardize among different devices. The study considered six temperature levels covering two physical phases of the sample; liquid and solid. The use of a single device in our study allowed keeping saliva filtering and collector surface effects as constant parameters and the temperature as a controlled variable; the physiological differences were minimized by averaging samples from a group of volunteers and a period of time. After EBC collection, we used an organic solvent rinse to collect the non-water-soluble compounds from the condenser surface. This additional matrix enhanced metabolites recovery, was less dependent on temperature changes, and may possibly serve as an additional pointer to standardize EBC sampling methodologies. The collected EBC samples were analyzed with a set of mass spectrometry methods to provide an overview of the compounds and their concentrations present at each temperature level. The total number of volatile and polar non-volatile compounds slightly increased in each physical phase as the collection temperature was lowered to minimum, 0 °C for liquid and -30, -56 °C for solid. The low-polarity non-volatile compounds showed a weak dependence on the collection temperature. The metabolomic content of EBC samples may not be solely dependent on temperature but may be influenced by other phenomena such as greater sample dilution due to condensation from the ambient air at colder temperatures, or due to adhesion properties of the collector surface and occurring chemical reactions. The relative importance of other design parameters such as condenser coating versus temperature requires further investigation.

Power-efficient self-cleaning hydrophilic condenser surface for portable exhaled breath condensate (EBC) metabolomic sampling.

In this work, we present a hydrophilic self-cleaning condenser surface for the collection of biological and environmental aerosol samples. The condenser is installed in a battery-operated hand-held breath sampling device. The device performance is characterized by the collection and analysis of exhaled breath samples from a group of volunteers. The exhaled breath condensate is collected on a subcooled condenser surface, transferred into a storage vial, and its chemical content is analyzed using mass spectrometric methods. The engineered surface supports upon it a continuous condensation cycle, and this allows the collection of liquid samples exceeding the saturation mass/area limit of a plain hydrophilic surface. The condenser surface employs two constituent parameters: a low surface energy barrier to enhance nucleation and condensation efficiency, and a network of surface microstructures to create a self-cleaning mechanism for fluid aggregation into a reservoir. Removal of the liquid condensate from the condenser surface prevents the formation of a thick liquid layer, and thus maintains a continuous condensation cycle with a minimum decrease in heat transfer efficiency as condensation occurs on the surface. The self-cleaning condenser surfaces may have a number of applications in the collection of biological, chemical, or environmental aerosol samples. Sample phase conversion to liquid can facilitate sample manipulation and chemical analysis of matrices with low concentrations. Here, we demonstrate the use of a self-cleaning microcondenser for the collection of exhaled breath condensate with a hand-held portable device. All breath collections with the two devices were performed with the same group of volunteers under UC Davis IRB protocol 63701-3.

Creation of a Robust and Generalizable Machine Learning Classifier for Patient Ventilator Asynchrony.

BACKGROUND: As healthcare increasingly digitizes, streaming waveform data is being made available from an variety of sources, but there still remains a paucity of performant clinical decision support systems. For example, in the intensive care unit (ICU) existing automated alarm systems typically rely on simple thresholding that result in frequent false positives. Recurrent false positive alerts create distrust of alarm mechanisms that can be directly detrimental to patient health. To improve patient care in the ICU, we need alert systems that are both pervasive, and accurate so as to be informative and trusted by providers. OBJECTIVE: We aimed to develop a machine learning-based classifier to detect abnormal waveform events using the use case of mechanical ventilation waveform analysis, and the detection of harmful forms of ventilation delivery to patients. We specifically focused on detecting injurious subtypes of patient-ventilator asynchrony (PVA). METHODS: Using a dataset of breaths recorded from 35 different patients, we used machine learning to create computational models to automatically detect, and classify two types of injurious PVA, double trigger asynchrony (DTA), breath stacking asynchrony (BSA). We examined the use of synthetic minority over-sampling technique (SMOTE) to overcome class imbalance problems, varied methods for feature selection, and use of ensemble methods to optimize the performance of our model. RESULTS: We created an ensemble classifier that is able to accurately detect DTA at a sensitivity/specificity of 0.960/0.975, BSA at sensitivity/specificity of 0.944/0.987, and non-PVA events at sensitivity/specificity of .967/.980. CONCLUSIONS: Our results suggest that it is possible to create a high-performing machine learning-based model for detecting PVA in mechanical ventilator waveform data in spite of both intra-patient, and inter-patient variability in waveform patterns, and the presence of clinical artifacts like cough and suction procedures. Our work highlights the importance of addressing class imbalance in clinical data sets, and the combined use of statistical methods and expert knowledge in feature selection.

Amphotericin B concentrations in healthy mallard ducks (Anas platyrhynchos) following a single intratracheal dose of liposomal amphotericin B using an atomizer.

Aspergillosis is a fungal infection that primarily affects the respiratory tract. Amphotericin B has broad antifungal activity and is commonly used to treat aspergillosis, a fungal pneumonia that is a common sequela in oiled waterfowl as well as other birds in wildlife rehabilitation. Pharmacokinetic parameters of nebulized amphotericin B in an avian model have been reported, but those of direct intratracheal delivery have yet to be established. The objective of this study was to evaluate if a single 3 mg/kg dose of liposomal amphotericin B delivered intratracheally using a commercial atomizer would achieve plasma and lung tissue concentrations exceeding targeted minimum inhibitory concentrations (MIC) for Aspergillus species in adult mallard ducks (Anas platyrhynchos). Following intratracheal delivery, amphotericin B was present in lung parenchyma at concentrations above the targeted MIC of 1 µg/g for up to 9 days post-administration; however, distribution of the drug was uneven, with the majority of the drug concentrated in one lung lobe. Concentrations in the contralateral lung lobe and the kidneys were above the targeted MIC 1 day after administration but declined exponentially with a half-life of approximately 2 days. Plasma concentrations were never above the targeted MIC. Histological examination of the trachea, bronchi, lungs, heart, liver, and kidneys did not reveal any toxic changes. Using a commercial atomizer, intratracheal delivery of amphotericin B at 3 mg/kg resulted in lung parenchyma concentrations above 1 µg/ml with no discernable systemic effects. Further studies to establish a system of drug delivery to both sides of the pulmonary parenchyma need to be performed, and the efficacy of this treatment for disease prevention remains to be determined.

Development of a research-oriented system for collecting mechanical ventilator waveform data.

Lack of access to high-frequency, high-volume patient-derived data, such as mechanical ventilator waveform data, has limited the secondary use of these data for research, quality improvement, and decision support. Existing methods for collecting these data are obtrusive, require high levels of technical expertise, and are often cost-prohibitive, limiting their use and scalability for research applications. We describe here the development of an unobtrusive, open-source, scalable, and user-friendly architecture for collecting, transmitting, and storing mechanical ventilator waveform data that is generalizable to other patient care devices. The system implements a software framework that automates and enforces end-to-end data collection and transmission. A web-based data management application facilitates nontechnical end users' abilities to manage data acquisition devices, mitigates data loss and misattribution, and automates data storage. Using this integrated system, we have been able to collect ventilator waveform data from >450 patients as part of an ongoing clinical study.

Development and Validation of a Multi-Algorithm Analytic Platform to Detect Off-Target Mechanical Ventilation.

Healthcare-specific analytic software is needed to process the large volumes of streaming physiologic waveform data increasingly available from life support devices such as mechanical ventilators. Detection of clinically relevant events from these data streams will advance understanding of critical illness, enable real-time clinical decision support, and improve both clinical outcomes and patient experience. We used mechanical ventilation waveform data (VWD) as a use case to address broader issues of data access and analysis including discrimination between true events and waveform artifacts. We developed an open source data acquisition platform to acquire VWD, and a modular, multi-algorithm analytic platform (ventMAP) to enable automated detection of off-target ventilation (OTV) delivery in critically-ill patients. We tested the hypothesis that use of artifact correction logic would improve the specificity of clinical event detection without compromising sensitivity. We showed that ventMAP could accurately detect harmful forms of OTV including excessive tidal volumes and common forms of patient-ventilator asynchrony, and that artifact correction significantly improved the specificity of event detection without decreasing sensitivity. Our multi-disciplinary approach has enabled automated analysis of high-volume streaming patient waveform data for clinical and translational research, and will advance the study and management of critically ill patients requiring mechanical ventilation.

Human breath metabolomics using an optimized non-invasive exhaled breath condensate sampler.

Exhaled breath condensate (EBC) analysis is a developing field with tremendous promise to advance personalized, non-invasive health diagnostics as new analytical instrumentation platforms and detection methods are developed. Multiple commercially-available and researcher-built experimental samplers are reported in the literature. However, there is very limited information available to determine an effective breath sampling approach, especially regarding the dependence of breath sample metabolomic content on the collection device design and sampling methodology. This lack of an optimal standard procedure results in a range of reported results that are sometimes contradictory. Here, we present a design of a portable human EBC sampler optimized for collection and preservation of the rich metabolomic content of breath. The performance of the engineered device is compared to two commercially available breath collection devices: the RTube™ and TurboDECCS. A number of design and performance parameters are considered, including: condenser temperature stability during sampling, collection efficiency, condenser material choice, and saliva contamination in the collected breath samples. The significance of the biological content of breath samples, collected with each device, is evaluated with a set of mass spectrometry methods and was the primary factor for evaluating device performance. The design includes an adjustable mass-size threshold for aerodynamic filtering of saliva droplets from the breath flow. Engineering an inexpensive device that allows efficient collection of metalomic-rich breath samples is intended to aid further advancement in the field of breath analysis for non-invasive health diagnostic. EBC sampling from human volunteers was performed under UC Davis IRB protocol 63701-3 (09/30/2014-07/07/2017).

Enhanced non-invasive respiratory sampling from bottlenose dolphins for breath metabolomics measurements.

Chemical analysis of exhaled breath metabolites is an emerging alternative to traditional clinical testing for many physiological conditions. The main advantage of breath analysis is its inherent non-invasive nature and ease of sample collection. Therefore, there exists a great interest in further development of this method for both humans and animals. The physiology of cetaceans is exceptionally well suited for breath analysis due to their explosive breathing behavior and respiratory tract morphology. At the present time, breath analysis in cetaceans has very limited practical applications, in large part due to lack of widely adopted sampling device(s) and methodologies that are well-standardized. Here, we present an optimized design and the operating principles of a portable apparatus for reproducible collection of exhaled breath condensate from small cetaceans, such as bottlenose dolphins (Tursiops truncatus). The device design is optimized to meet two criteria: standardized collection and preservation of information-rich metabolomic content of the biological sample, and animal comfort and ease of breath sample collection. The intent is to furnish a fully-benchmarked technology that can be widely adopted by researchers and conservationists to spur further developments of breath analysis applications for marine mammal health assessments.

Continuous droplet removal upon dropwise condensation of humid air on a hydrophobic micropatterned surface.

Combination of two physical phenomena, capillary pressure gradient and wettability gradient, allows a simple two-step fabrication process that yields a reliable hydrophobic self-cleaning condenser surface. The surface is fabricated with specific microscopic topography and further treatment with a chemically inert low-surface-energy material. This process does not require growth of nanofeatures (nanotubes) or hydrophilic-hydrophobic patterning of the surface. Trapezoidal geometry of the microfeatures facilitates droplet transfer from the Wenzel to the Cassie state and reduces droplet critical diameter. The geometry of the micropatterns enhances local coalescence and directional movement for droplets with diameter much smaller than the radial length of the micropatterns. The hydrophobic self-cleaning micropatterned condenser surface prevents liquid film formation and promotes continuous dropwise condensation cycle. Upon dropwise condensation, droplets follow a designed wettability gradient created with micropatterns from the most hydrophobic to the least hydrophobic end of the surface. The surface has higher condensation efficiency, due to its directional self-cleaning property, than a plain hydrophobic surface. We explain the self-actuated droplet collection mechanism on the condenser surface and demonstrate experimentally the creation of an effective wettability gradient over a 6 mm radial distance. In spite of its fabrication simplicity, the fabricated surface demonstrates self-cleaning property, enhanced condensation performance, and reliability over time. Our work enables creation of a hydrophobic condenser surface with the directional self-cleaning property that can be used for collection of biological (chemical, environmental) aerosol samples or for condensation enhancement.