ASU Scholarship Showcase

We investigate near-field radiative heat transfer between Indium Tin Oxide (ITO) nanowire arrays which behave as type 1 and 2 hyperbolic metamaterials. Using spatial dispersion dependent effective medium theory to model the dielectric function of the nanowires, the impact of filling fraction on the heat transfer is analyzed. Depending on the filling fraction, it is possible to achieve both types of hyperbolic modes. At 150 nm vacuum gap, the heat transfer between the nanowires with 0.5 filling fraction can be 11 times higher than that between two bulk ITOs. For vacuum gaps less than 150 nm the heat transfer increases as the filling fraction decreases. Results obtained from this study will facilitate applications of ITO nanowires as hyperbolic metamaterials for energy systems.

The estimation of energy demand (by power plants) has traditionally relied on historical energy use data for the region(s) that a plant produces for. Regression analysis, artificial neural network and Bayesian theory are the most common approaches for analysing these data. Such data and techniques do not generate reliable results. Consequently, excess energy has to be generated to prevent blackout; causes for energy surge are not easily determined; and potential energy use reduction from energy efficiency solutions is usually not translated into actual energy use reduction. The paper highlights the weaknesses of traditional techniques, and lays out a framework to improve the prediction of energy demand by combining energy use models of equipment, physical systems and buildings, with the proposed data mining algorithms for reverse engineering. The research team first analyses data samples from large complex energy data, and then, presents a set of computationally efficient data mining algorithms for reverse engineering. In order to develop a structural system model for reverse engineering, two focus groups are developed that has direct relation with cause and effect variables. The research findings of this paper includes testing out different sets of reverse engineering algorithms, understand their output patterns and modify algorithms to elevate accuracy of the outputs.

Small and medium office buildings consume a significant parcel of the U.S. building stock energy consumption. Still, owners lack resources and experience to conduct detailed energy audits and retrofit analysis. We present an eight-steps framework for an energy retrofit assessment in small and medium office buildings. Through a bottom-up approach and a web-based retrofit toolkit tested on a case study in Arizona, this methodology was able to save about 50% of the total energy consumed by the case study building, depending on the adopted measures and invested capital. While the case study presented is a deep energy retrofit, the proposed framework is effective in guiding the decision-making process that precedes any energy retrofit, deep or light.

Commercial buildings’ consumption is driven by multiple factors that include occupancy, system and equipment efficiency, thermal heat transfer, equipment plug loads, maintenance and operational procedures, and outdoor and indoor temperatures. A modern building energy system can be viewed as a complex dynamical system that is interconnected and influenced by external and internal factors. Modern large scale sensor measures some physical signals to monitor real-time system behaviors. Such data has the potentials to detect anomalies, identify consumption patterns, and analyze peak loads. The paper proposes a novel method to detect hidden anomalies in commercial building energy consumption system. The framework is based on Hilbert-Huang transform and instantaneous frequency analysis. The objectives are to develop an automated data pre-processing system that can detect anomalies and provide solutions with real-time consumption database using Ensemble Empirical Mode Decomposition (EEMD) method. The finding of this paper will also include the comparisons of Empirical mode decomposition and Ensemble empirical mode decomposition of three important type of institutional buildings.

There are many data mining and machine learning techniques to manage large sets of complex energy supply and demand data for building, organization and city. As the amount of data continues to grow, new data analysis methods are needed to address the increasing complexity. Using data from the energy loss between the supply (energy production sources) and demand (buildings and cities consumption), this paper proposes a Semi-Supervised Energy Model (SSEM) to analyse different loss factors for a building cluster. This is done by deep machine learning by training machines to semi-supervise the learning, understanding and manage the process of energy losses. Semi-Supervised Energy Model (SSEM) aims at understanding the demand-supply characteristics of a building cluster and utilizes the confident unlabelled data (loss factors) using deep machine learning techniques. The research findings involves sample data from one of the university campuses and presents the output, which provides an estimate of losses that can be reduced. The paper also provides a list of loss factors that contributes to the total losses and suggests a threshold value for each loss factor, which is determined through real time experiments. The conclusion of this paper provides a proposed energy model that can provide accurate numbers on energy demand, which in turn helps the suppliers to adopt such a model to optimize their supply strategies.

In this letter, we study the near-field radiative heat transfer between two metamaterial substrates coated with silicon carbide (SiC) thin films. It is known that metamaterials can enhance the near-field heat transfer over ordinary materials due to excitation of magnetic plasmons associated with s polarization, while strong surface phonon polariton exists for SiC. By careful tuning of the optical properties of metamaterial, it is possible to excite electrical and magnetic resonances for the metamaterial and surface phonon polaritons for SiC at different spectral regions, resulting in the enhanced heat transfer. The effect of the SiC film thickness at different vacuum gaps is investigated. Results obtained from this study will be beneficial for application of thin film coatings for energy harvesting.

A film-coupled metamaterial structure is numerically investigated for enhancing the light absorption in an ultrathin photovoltaic layer of crystalline gallium arsenide (GaAs). The top subwavelength concave grating and the bottom metallic film could not only effectively trap light with the help of wave interference and magnetic resonance effects excited above the bandgap, but also practically serve as electrical contacts for photon-generated charge collection. The energy absorbed by the active layer is greatly enhanced with the help of the film-coupled metamaterial structure, resulting in significant improvement on the short-circuit current density by three times over a free-standing GaAs layer at the same thickness. The performance of the proposed light trapping structure is demonstrated to be little affected by the grating ridge width considering the geometric tolerance during fabrication. The optical absorption at oblique incidences also shows direction-insensitive behavior, which is highly desired for efficiently converting off-normal sunlight to electricity. The results would facilitate the development of next-generation ultrathin solar cells with lower cost and higher efficiency.

Strategies are needed to improve repopulation of decellularized lung scaffolds with stromal and functional epithelial cells. We demonstrate that decellularized mouse lungs recellularized in a dynamic low fluid shear suspension bioreactor, termed the rotating wall vessel (RWV), contained more cells with decreased apoptosis, increased proliferation and enhanced levels of total RNA compared to static recellularization conditions. These results were observed with two relevant mouse cell types: bone marrow-derived mesenchymal stromal (stem) cells (MSCs) and alveolar type II cells (C10). In addition, MSCs cultured in decellularized lungs under static but not bioreactor conditions formed multilayered aggregates. Gene expression and immunohistochemical analyses suggested differentiation of MSCs into collagen I-producing fibroblast-like cells in the bioreactor, indicating enhanced potential for remodeling of the decellularized scaffold matrix. In conclusion, dynamic suspension culture is promising for enhancing repopulation of decellularized lungs, and could contribute to remodeling the extracellular matrix of the scaffolds with subsequent effects on differentiation and functionality of inoculated cells.

Investigation into the causes underlying the rapid, global amphibian decline provides critical insight into the effects of changing ecosystems. Hypothesized and confirmed links between amphibian declines, disease, and environmental changes are increasingly represented in published literature. However, there are few long-term amphibian studies that include data on population size, abnormality/injury rates, disease, and habitat variables to adequately assess changes through time. We cultured and identified microorganisms isolated from abnormal/injured and repressed tissue regeneration sites of the endangered Ozark Hellbender, Cryptobranchus alleganiensis bishopi, to discover potential causative agents responsible for their significant decline in health and population. This organism and our study site were chosen because the population and habitat of C. a. bishopi have been intensively studied from 1969–2009, and the abnormality/injury rate and apparent lack of regeneration were established.

Although many bacterial and fungal isolates recovered were common environmental organisms, several opportunistic pathogens were identified in association with only the injured tissues of C.a. bishopi. Bacterial isolates included Aeromonas hydrophila, a known amphibian pathogen, Granulicetella adiacens, Gordonai terrae, Stenotrophomonas maltophilia, Aerococcus viridans, Streptococcus pneumoniae and a variety of Pseudomonads, including Pseudomonas aeruginosa, P. stutzeri, and P. alcaligenes. Fungal isolates included species in the genera Penicillium, Acremonium, Cladosporium, Curvularia, Fusarium, Streptomycetes, and the Class Hyphomycetes. Many of the opportunistic pathogens identified are known to form biofilms. Lack of isolation of the same organism from all wounds suggests that the etiological agent responsible for the damage to C. a. bishopi may not be a single organism. To our knowledge, this is the first study to profile the external microbial consortia cultured from a Cryptobranchid salamander. The incidence of abnormalities/injury and retarded regeneration in C. a. bishopi may have many contributing factors including disease and habitat degradation. Results from this study may provide insight into other amphibian population declines.

A distinct pathovar of Salmonella enterica serovar Typhimurium, ST313, has emerged in sub-Saharan Africa as a major cause of fatal bacteremia in young children and HIV-infected adults. D23580, a multidrug resistant clinical isolate of ST313, was previously shown to have undergone genome reduction in a manner that resembles that of the more human-restricted pathogen, Salmonella enterica serovar Typhi. It has since been shown through tissue distribution studies that D23580 is able to establish an invasive infection in chickens. However, it remains unclear whether ST313 can cause lethal disease in a non-human host following a natural course of infection. Herein we report that D23580 causes lethal and invasive disease in a murine model of infection following peroral challenge. The LD⁵⁰ of D23580 in female BALB/c mice was 4.7 x 10⁵ CFU. Tissue distribution studies performed 3 and 5 days post-infection confirmed that D23580 was able to more rapidly colonize the spleen, mesenteric lymph nodes and gall bladder in mice when compared to the well-characterized S. Typhimurium strain SL1344. D23580 exhibited enhanced resistance to acid stress relative to SL1344, which may lend towards increased capability to survive passage through the gastrointestinal tract as well as during its intracellular lifecycle. Interestingly, D23580 also displayed higher swimming motility relative to SL1344, S. Typhi strain Ty2, and the ST313 strain A130. Biochemical tests revealed that D23580 shares many similar metabolic features with SL1344, with several notable differences in the Voges-Proskauer and catalase tests, as well alterations in melibiose, and inositol utilization. These results represent the first full duration infection study using an ST313 strain following the entire natural course of disease progression, and serve as a benchmark for ongoing and future studies into the pathogenesis of D23580.