Observation of magnetic resonance of electron spins with engineered membrane resonators

Within the field of nuclear magnetic resonance (NMR), it has long been considered that using force-based detection instead of a pick-up coil for electromagnetic waves may be an intriguing way to achieve nanoscale resolution for detection of nuclear spins. This idea, known as magnetic resonance force microscopy (MRFM), is nonetheless a very difficult experimental proposition due to the extreme sensitivities necessary. Silicon nitride membrane resonators are one potential way that we want to explore increasing the force sensitivity of MRFM devices and improving imaging resolution. Specifically, engineered silicon nitride resonators may have lower surface noise effects due to higher frequencies and reduced force noise floors do to high quality factors. In my thesis, we took a first step towards this goal, demonstrating observation of magnetic resonance of electron spins in DPPH (a spin sample that is easier to detect both in concentration and gyromagnetic ratio) and achieve force sensitivities as low as 67 aN/&radic;Hz. Additonally, future membrane resonators are introduced that hint at force sensitivities as low as 0.6 aN/&radic;Hz with resonant frequencies above 1 MHz. Finally, discussion is opened around the integration of MRFM devices into an optical cavity, for which silicon nitride membrane resonators as used in the Regal group are aptly fit. Benefits of cavity optomechanical integration lie in passive damping of the mechanics to increase measurement bandwidth and improved detection sensitivity.</p

Unlocking the potential of polymeric desalination membranes by understanding molecular-level interactions and transport mechanisms

Polyamide reverse osmosis (PA-RO) membranes achieve remarkably high water permeability and salt rejection, making them a key technology for addressing water shortages through processes including seawater desalination and wastewater reuse. However, current state-of-the-art membranes suffer from challenges related to inadequate selectivity, fouling, and a poor ability of existing models to predict performance. In this Perspective, we assert that a molecular understanding of the mechanisms that govern selectivity and transport of PA-RO and other polymer membranes is crucial to both guide future membrane development efforts and improve the predictive capability of transport models. We summarize the current understanding of ion, water, and polymer interactions in PA-RO membranes, drawing insights from nanofiltration and ion exchange membranes. Building on this knowledge, we explore how these interactions impact the transport properties of membranes, highlighting assumptions of transport models that warrant further investigation to improve predictive capabilities and elucidate underlying transport mechanisms. We then underscore recent advances in in situ characterization techniques that allow for direct measurements of previously difficult-to-obtain information on hydrated polymer membrane properties, hydrated ion properties, and ion&ndash;water&ndash;membrane interactions as well as powerful computational and electrochemical methods that facilitate systematic studies of transport phenomena. &nbsp;</p

Exploring a platinum nanocatalytic microcombustion-thermoelectric coupled device

This work aimed to create a first-generation power device for eventual application to portable electronics. A platinum nanoparticle catalytic substrate was employed in a microcombustion-thermoelectric coupled (MTC) device for the purpose of chemical-to-electrical energy conversion. Multiple microcombustion reactors were designed, fabricated, and investigated. Most importantly, the reactor configuration was designed to accommodate thermoelectric generators (TEGs) for power production. Temperature studies with catalytic combustion of methanol-air fuel mixtures were used to evaluate the thermal power generation performance of each reactor. The final reactor design enabled ignition at room temperature with the ability to achieve repeat catalytic cycles upon subsequent exposure to methanol-air mixtures. Preliminary performance studies achieved a maximum temperature difference T of 58 degrees C with a fuel mixture flow rate of 800 mL/min. While the temperature difference indicates a respectable potential for power generation, the importance of thermal design was a key finding of this work. It was thought that improved thermal management could make better use of thermal energy lost in the exhaust stream, potentially increasing reactor surface temperatures and corresponding thermoelectric generator parameter T. Thermal design changes would significantly enhance the performance of a later generation of this device, detailed at the close of this thesis

A Study of Fuel and Reactor Design for Platinum Nanoparticle Catalyzed Microreactors

Typical microcombustion-based power devices entail the use of catalyst to sustain combustion in less than millimeter scale channels. This work explores the use of several other candidate fuels for ~8 nm diameter Pt particle catalyzed combustion within 800 μm channel width cordierite substrates. The results demonstrate while commercial hydrocarbon fuels such as methane, propane, butane, and ethanol can be used to sustain catalytic combustion, room temperature ignition was only observed using methanol-air mixtures. Fuels, other than methanol, required preheating at temperatures >200°C, yet repeated catalytic cycling similar to methanol-air mixtures was demonstrated. Subsequently, a new reactor design was investigated to couple with thermoelectric generators. The modified reactor design enabled ignition of methanol-air mixtures at room temperature with the ability to achieve repeat catalytic cycles. Preliminary performance studies achieved a maximum temperature difference ΔT of 55°C with a flow rate of 800 mL/min. While the temperature difference indicates a respectable potential for power generation, reduced exhaust temperature and improved thermal management could significantly enhance the eventual device performance

A mixed-model approach for estimating drivers of microbiota community composition and differential taxonomic abundance

Next-generation sequencing (NGS) and metabarcoding approaches are increasingly applied to wild animal populations, but there is a disconnect between the widely applied generalized linear mixed model (GLMM) approaches commonly used to study phenotypic variation and the statistical toolkit from community ecology typically applied to metabarcoding data. Here, we describe the suitability of a novel GLMM-based approach for analyzing the taxon-specific sequence read counts derived from standard metabarcoding data. This approach allows decomposition of the contribution of different drivers to variation in community composition (e.g., age, season, individual) via interaction terms in the model random-effects structure. We provide guidance to implementing this approach and show how these models can identify how responsible specific taxonomic groups are for the effects attributed to different drivers. We applied this approach to two cross-sectional data sets from the Soay sheep population of St. Kilda. GLMMs showed agreement with dissimilarity-based approaches highlighting the substantial contribution of age and minimal contribution of season to microbiota community compositions, and simultaneously estimated the contribution of other technical and biological factors. We further used model predictions to show that age effects were principally due to increases in taxa of the phylum Bacteroidetes and declines in taxa of the phylum Firmicutes. This approach offers a powerful means for understanding the influence of drivers of community structure derived from metabarcoding data. We discuss how our approach could be readily adapted to allow researchers to estimate contributions of additional factors such as host or microbe phylogeny to answer emerging questions surrounding the ecological and evolutionary roles of within-host communities. IMPORTANCE NGS and fecal metabarcoding methods have provided powerful opportunities to study the wild gut microbiome. A wealth of data is, therefore, amassing across wild systems, generating the need for analytical approaches that can appropriately investigate simultaneous factors at the host and environmental scale that determine the composition of these communities. Here, we describe a generalized linear mixed-effects model (GLMM) approach to analyze read count data from metabarcoding of the gut microbiota, allowing us to quantify the contributions of multiple host and environmental factors to within-host community structure. Our approach provides outputs that are familiar to a majority of field ecologists and can be run using any standard mixed-effects modeling packages. We illustrate this approach using two metabarcoding data sets from the Soay sheep population of St. Kilda investigating age and season effects as worked examples