ASU Scholarship Showcase

Multilayer structures of TiO₂/Ag/TiO₂ have been deposited onto flexible substrates by room temperature sputtering to develop indium-free transparent composite electrodes. The effect of Ag thicknesses on optical and electrical properties and the mechanism of conduction have been discussed. The critical thickness (t_c) of Ag mid-layer to form a continuous conducting layer is 9.5 nm and the multilayer has been optimized to obtain a sheet resistance of 5.7 Ω/sq and an average optical transmittance of 90% at 590 nm. The Haacke figure of merit (FOM) for t_c has one of the highest FOMs with 61.4 × 10^-3 Ω^-1/sq.

Lithium-beryllium metal hydrides, which are structurally related to their parent compound, BeH₂, offer the highest hydrogen storage capacity by weight among the metal hydrides (15.93 wt. % of hydrogen for LiBeH₃). Challenging synthesis protocols have precluded conclusive determination of their crystallographic structure to date, but here we analyze directly the hydrogen hopping mechanisms in BeH₂ and LiBeH₃ using quasielastic neutron scattering, which is especially sensitive to single-particle dynamics of hydrogen. We find that, unlike its parent compound BeH₂, lithium-beryllium hydride LiBeH₃ exhibits a sharp increase in hydrogen mobility above 265 K, so dramatic that it can be viewed as melting of hydrogen sublattice. We perform comparative analysis of hydrogen jump mechanisms observed in BeH₂ and LiBeH₃ over a broad temperature range. As microscopic diffusivity of hydrogen is directly related to its macroscopic kinetics, a transition in LiBeH₃ so close to ambient temperature may offer a straightforward and effective mechanism to influence hydrogen uptake and release in this very lightweight hydrogen storage compound.

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.

Cyan fluorescent proteins (CFPs), such as Cerulean, are widely used as donor fluorophores in Förster resonance energy transfer (FRET) experiments. Nonetheless, the most widely used variants suffer from drawbacks that include low quantum yields and unstable flurorescence. To improve the fluorescence properties of Cerulean, we used the X-ray structure to rationally target specific amino acids for optimization by site-directed mutagenesis. Optimization of residues in strands 7 and 8 of the β-barrel improved the quantum yield of Cerulean from 0.48 to 0.60. Further optimization by incorporating the wild-type T65S mutation in the chromophore improved the quantum yield to 0.87. This variant, mCerulean3, is 20% brighter and shows greatly reduced fluorescence photoswitching behavior compared to the recently described mTurquoise fluorescent protein in vitro and in living cells. The fluorescence lifetime of mCerulean3 also fits to a single exponential time constant, making mCerulean3 a suitable choice for fluorescence lifetime microscopy experiments. Furthermore, inclusion of mCerulean3 in a fusion protein with mVenus produced FRET ratios with less variance than mTurquoise-containing fusions in living cells. Thus, mCerulean3 is a bright, photostable cyan fluorescent protein which possesses several characteristics that are highly desirable for FRET experiments.

In proteins, functional divergence involves mutations that modify structure and dynamics. Here we provide experimental evidence for an evolutionary mechanism driven solely by long-range dynamic motions without significant backbone adjustments, catalytic group rearrangements, or changes in subunit assembly. Crystallographic structures were determined for several reconstructed ancestral proteins belonging to a GFP class frequently employed in superresolution microscopy. Their chain flexibility was analyzed using molecular dynamics and perturbation response scanning. The green-to-red photoconvertible phenotype appears to have arisen from a common green ancestor by migration of a knob-like anchoring region away from the active site diagonally across the β barrel fold. The allosterically coupled mutational sites provide active site conformational mobility via epistasis. We propose that light-induced chromophore twisting is enhanced in a reverse-protonated subpopulation, activating internal acid-base chemistry and backbone cleavage to enlarge the chromophore. Dynamics-driven hinge migration may represent a more general platform for the evolution of novel enzyme activities.

A water drop on a superhydrophobic surface that is pinned by wire loops can be reproducibly cut without formation of satellite droplets. Drops placed on low-density polyethylene surfaces and Teflon-coated glass slides were cut with superhydrophobic knives of low-density polyethylene and treated copper or zinc sheets, respectively. Distortion of drop shape by the superhydrophobic knife enables a clean break. The driving force for droplet formation arises from the lower surface free energy for two separate drops, and it is modeled as a 2-D system. An estimate of the free energy change serves to guide when droplets will form based on the variation of drop volume, loop spacing and knife depth. Combining the cutting process with an electrofocusing driving force could enable a reproducible biomolecular separation without troubling satellite drop formation.

Natural selection among tumor cell clones is thought to produce hallmark properties of malignancy. Efforts to understand evolution of one such hallmark—the angiogenic switch—has suggested that selection for angiogenesis can “run away” and generate a hypertumor, a form of evolutionary suicide by extreme vascular hypo- or hyperplasia. This phenomenon is predicted by models of tumor angiogenesis studied with the techniques of adaptive dynamics. These techniques also predict that selection drives tumor proliferative potential towards an evolutionarily stable strategy (ESS) that is also convergence-stable. However, adaptive dynamics are predicated on two key assumptions: (i) no more than two distinct clones or evolutionary strategies can exist in the tumor at any given time; and (ii) mutations cause small phenotypic changes. Here we show, using a stochastic simulation, that relaxation of these assumptions has no effect on the predictions of adaptive dynamics in this case. In particular, selection drives proliferative potential towards, and angiogenic potential away from, their respective ESSs. However, these simulations also show that tumor behavior is highly contingent on mutational history, particularly for angiogenesis. Individual tumors frequently grow to lethal size before the evolutionary endpoint is approached. In fact, most tumor dynamics are predicted to be in the evolutionarily transient regime throughout their natural history, so that clinically, the ESS is often largely irrelevant. In addition, we show that clonal diversity as measured by the Shannon Information Index correlates with the speed of approach to the evolutionary endpoint. This observation dovetails with results showing that clonal diversity in Barrett's esophagus predicts progression to malignancy.