Institute for Critical Technology and Applied Science (ICTAS)

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https://hdl.handle.net/10919/23765
ICTAS Vision: To be a premier institute to advance transformative, interdisciplinary research for a sustainable future. ICTAS Mission: To serve Virginia Tech, the Commonwealth of Virginia, the nation, and the world through high-impact research and scholarship at the intersections of engineering, the sciences -- physical, life, and social -- and the humanities. To this end, advance the frontiers of knowledge and education, enhance the educational experience of undergraduate, graduate, and professional students, and promote economic development through the creation and application of innovative research that enhances the quality of life and preserves our natural resources.

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Browsing Institute for Critical Technology and Applied Science (ICTAS) by Department "Center for Energy Harvesting Materials and Systems (CEHMS)"

Now showing 1 - 3 of 3
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    Combinatory Finite Element and Artificial Neural Network Model for Predicting Performance of Thermoelectric Generator
    Kishore, Ravi Anant; Mahajan, Roop L.; Priya, Shashank (MDPI, 2018-08-24)
    Thermoelectric generators (TEGs) are rapidly becoming the mainstream technology for converting thermal energy into electrical energy. The rise in the continuous deployment of TEGs is related to advancements in materials, figure of merit, and methods for module manufacturing. However, rapid optimization techniques for TEGs have not kept pace with these advancements, which presents a challenge regarding tailoring the device architecture for varying operating conditions. Here, we address this challenge by providing artificial neural network (ANN) models that can predict TEG performance on demand. Out of the several ANN models considered for TEGs, the most efficient one consists of two hidden layers with six neurons in each layer. The model predicted TEG power with an accuracy of ±0.1 W, and TEG efficiency with an accuracy of ±0.2%. The trained ANN model required only 26.4 ms per data point for predicting TEG performance against the 6.0 minutes needed for the traditional numerical simulations.
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    Discovery and ramifications of incidental Magnéli phase generation and release from industrial coal-burning
    Yang, Yi; Chen, Bo; Hower, James C.; Schindler, Michael; Winkler, Christopher; Brandt, Jessica E.; Di Giulio, Richard T.; Ge, Jianping; Liu, Min; Fu, Yuhao; Zhang, Lijun; Chen, Yu-ru; Priya, Shashank; Hochella, Michael F. Jr. (Nature Publishing Group, 2017-01-12)
    Coal, as one of the most economic and abundant energy sources, remains the leading fuel for producing electricity worldwide. Yet, burning coal produces more global warming CO2 relative to all other fossil fuels, and it is a major contributor to atmospheric particulate matter known to have a deleterious respiratory and cardiovascular impact in humans, especially in China and India. Here we have discovered that burning coal also produces large quantities of otherwise rare Magneli phases (Ti; x; O2x–1 with 4 ≤ x ≤ 9) from TiO2 minerals naturally present in coal. This provides a new tracer for tracking solid-state emissions worldwide from industrial coal-burning. In its first toxicity testing, we have also shown that nanoscale Magneli phases have potential toxicity pathways that are not photoactive like TiO2 phases, but instead seem to be biologically active without photostimulation. In the future, these phases should be thoroughly tested for their toxicity in the human lung. Solid-state emissions from coal burning remain an environmental concern. Here, the authors have found that TiO2 minerals present in coal are converted into titanium suboxides during burning, and initial biotoxicity screening suggests that further testing is needed to look into human lung consequences.
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    Large piezoresistivity phenomenon in SiCN-(La,Sr)MnO3 composites
    Karmarkar, Makarand; Singh, Gurpreet; Shah, Sandeep; Mahajan, Roop L.; Priya, Shashank (AIP Publishing, 2009-02-01)
    We present the results on SiCN-(La,Sr)MnO3 (LSMO) composites correlating the observed large piezoresistance behavior with the microstructural features and defect chemistry. Scanning electron microscopy characterization revealed the presence of self-assembled periodic microvalleys in the microstructure with width of 1-5 mu m and depth of 600-1000 nm. The microvalleys act as stress concentration points providing change in volume with applied stress. High resolution transmission electron microscopy measurements conducted on composites showed that LSMO grains consist of SiCN phase but no inclusions were observed.