Highly flexible modules using thin 153 cm[superscript 2] silicon crystalline cells and transparent fluoropolymer foil are demonstrated. The modules can be flexed 200 times around a bend radius of 4 cm without change in efficiency. The silicon crystalline heterojunction solar cells are 65±5 μm-thick with efficiencies up to 18.4%. Cracks in the solar cells and interconnections that are induced by mechanical stress during module bending are examined using electroluminescence. Two interconnection solutions are discussed: ribbons affixed to the busbars using a conductive adhesive, and indium coated wires directly bonded to the cell fingers. Modules using wire interconnection are found to be highly flexible with efficiencies greatly exceeding existing commercial flexible modules using thin films and have potential applications in light-weight modules for building integrated and portable photovoltaic power.

Ambient air temperatures are expected to increase in the US desert southwest by 1-5 °C mid-century which will strain the electric power grid through increased loads, reduced power capacities, efficiencies, and material lifespans. To better understand and quantify this risk, a power infrastructure failure model is created to estimate changes in outage rates of components for increases in air temperatures in Arizona. Components analyzed include generation, transmission lines, and substations, because their outages can lead to cascading failures and interruptions of other critical infrastructure systems such as water, transportation, and information/communication technology. Preliminary results indicate that components could require maintenance or replacement up to 3 times more often due to mechanical failures, outages could occur up to 30 times more often due to overcurrent tripping, and the probability of cascading failures could increase 30 times as well for a 1 °C increase in ambient air temperature. Preventative measures can include infrastructure upgrades to more thermal resistant parts, installation of cooling systems, smart grid power flow controls, and expanding programs for demand side management and customer energy efficiency.

A tetradentate Pd(II) complex, Pd3O3, which exhibits highly efficient excimer emission is synthesized and characterized. Pd3O3 can achieve blue emission despite using phenyl-pyridine emissive ligands which have been a mainstay of stable green and red phosphorescent emitter designs, making Pd3O3 a good candidate for stable blue or white OLEDs. Pd3O3 exhibits strong and efficient phosphorescent excimer emission expanding the excimer based white OLEDs beyond the sole class of Pt complexes. Devices of Pd3O3 demonstrate peak external quantum efficiencies as high as 24.2% and power efficiencies of 67.9 Lm per W for warm white devices. Furthermore, Pd3O3 devices in a carefully designed stable structure achieved a device operational lifetime of nearly 3000 h at 1000 cd m^-2 without any outcoupling enhancement while simultaneously achieving peak external quantum efficiencies of 27.3% and power efficiencies over 81 Lm per W.

Design of novel infrastructure materials requires a proper understanding of the influence of microstructure on the desired performance. The priority is to seek new and innovative ways to develop sustainable infrastructure materials using natural resources and industrial solid wastes in a manner that is ecologically sustainable and yet economically viable. Structural materials are invariably designed based on mechanical performance. Accurate prediction of effective constitutive behavior of highly heterogeneous novel structural materials with multiple microstructural phases is a challenging task. This necessitates reliable classification and characterization of constituent phases in terms of their volume fractions, size distributions and intrinsic elastic properties, coupled with numerical homogenization technique. This paper explores a microstructure-guided numerical framework that derives inputs from nanoindentation and synchrotron x-ray tomography towards the prediction of effective constitutive response of novel sustainable structural materials so as to enable microstructure-guided design.

Risks are inherent in construction projects. In order to manage risks, contingency amount is set aside usually in an escrow account. Cost contingency can be a handsome amount that would get blocked during the execution of the project for further use, incurring constant opportunity cost. The stakeholders may wish to use this held amount for other endeavors during project execution. The managerial practices for dynamic contingency management are of extreme importance. Stakeholders anticipate risks and hindsight project performance by eyeing key performance indicators of a project to direct decisions. The aim of this research is to integrate project key performance indicators with future risk perception to develop a decision support system for facilitating cost contingency release requests. The model is expected to help decision making to ease the managerial burden ensuring effective use of contingency. The findings are not conclusive due to ongoing nature of research.

The water resources and hydrologic extremes in Mediterranean basins are heavily influenced by climate variability. Modeling these watersheds is difficult due to the complex nature of the hydrologic response as well as the sparseness of hydrometeorological observations. In this work, we present a strategy to calibrate a distributed hydrologic model, known as TIN-based Real-time Integrated Basin Simulator (tRIBS), in the Rio Mannu basin (RMB), a medium-sized watershed (472.5 km[superscript 2]) located in an agricultural area in Sardinia, Italy. In the RMB, precipitation, streamflow and meteorological data were collected within different historical periods and at diverse temporal resolutions. We designed two statistical tools for downscaling precipitation and potential evapotranspiration data to create the hourly, high-resolution forcing for the hydrologic model from daily records. Despite the presence of several sources of uncertainty in the observations and model parameterization, the use of the disaggregated forcing led to good calibration and validation performances for the tRIBS model, when daily discharge observations were available. The methodology proposed here can be also used to disaggregate outputs of climate models and conduct high-resolution hydrologic simulations with the goal of quantifying the impacts of climate change on water resources and the frequency of hydrologic extremes within medium-sized basins.

Previous studies in building energy assessment clearly state that to meet sustainable energy goals, existing buildings, as well as new buildings, will need to improve their energy efficiency. Thus, meeting energy goals relies on retrofitting existing buildings. Most building energy models are bottom-up engineering models, meaning these models calculate energy demand of individual buildings through their physical properties and energy use for specific end uses (e.g., lighting, appliances, and water heating). Researchers then scale up these model results to represent the building stock of the region studied.

Studies reveal that there is a lack of information about the building stock and associated modeling tools and this lack of knowledge affects the assessment of building energy efficiency strategies. Literature suggests that the level of complexity of energy models needs to be limited. Accuracy of these energy models can be elevated by reducing the input parameters, alleviating the need for users to make many assumptions about building construction and occupancy, among other factors. To mitigate the need for assumptions and the resulting model inaccuracies, the authors argue buildings should be described in a regional stock model with a restricted number of input parameters. One commonly-accepted method of identifying critical input parameters is sensitivity analysis, which requires a large number of runs that are both time consuming and may require high processing capacity.

This paper utilizes the Energy, Carbon and Cost Assessment for Buildings Stocks (ECCABS) model, which calculates the net energy demand of buildings and presents aggregated and individual- building-level, demand for specific end uses, e.g., heating, cooling, lighting, hot water and appliances. The model has already been validated using the Swedish, Spanish, and UK building stock data. This paper discusses potential improvements to this model by assessing the feasibility of using stepwise regression to identify the most important input parameters using the data from UK residential sector. The paper presents results of stepwise regression and compares these to sensitivity analysis; finally, the paper documents the advantages and challenges associated with each method.

Grain boundary grooves are common features on polycrystalline solid–liquid interfaces. Their local microstructure can be closely approximated as a “variational” groove, the theoretical profile for which is analyzed here for its Gibbs-Thomson thermo-potential distribution. The distribution of thermo-potentials for a variational groove exhibits gradients tangential to the solid–liquid interface. Energy fluxes stimulated by capillary-mediated tangential gradients are divergent and thus capable of redistributing energy on real or simulated grain boundary grooves. Moreover, the importance of such capillary-mediated energy fields on interfaces is their influence on stability and pattern formation dynamics. The capillary-mediated field expected to be present on a stationary grain boundary groove is verified quantitatively using the multiphase-field approach. Simulation and post-processing measurements fully corroborate the presence and intensity distribution of interfacial cooling, proving that thermodynamically-consistent numerical models already support, without any modification, capillary perturbation fields, the existence of which is currently overlooked in formulations of sharp interface dynamic models.

Preserving the utility of published datasets while simultaneously providing provable privacy guarantees is a well-known challenge. On the one hand, context-free privacy solutions, such as differential privacy, provide strong privacy guarantees, but often lead to a significant reduction in utility. On the other hand, context-aware privacy solutions, such as information theoretic privacy, achieve an improved privacy-utility tradeoff, but assume that the data holder has access to dataset statistics. We circumvent these limitations by introducing a novel context-aware privacy framework called generative adversarial privacy (GAP). GAP leverages recent advancements in generative adversarial networks (GANs) to allow the data holder to learn privatization schemes from the dataset itself. Under GAP, learning the privacy mechanism is formulated as a constrained minimax game between two players: a privatizer that sanitizes the dataset in a way that limits the risk of inference attacks on the individuals’ private variables, and an adversary that tries to infer the private variables from the sanitized dataset. To evaluate GAP’s performance, we investigate two simple (yet canonical) statistical dataset models: (a) the binary data model; and (b) the binary Gaussian mixture model. For both models, we derive game-theoretically optimal minimax privacy mechanisms, and show that the privacy mechanisms learned from data (in a generative adversarial fashion) match the theoretically optimal ones. This demonstrates that our framework can be easily applied in practice, even in the absence of dataset statistics.