Missouri University of Science and Technology

About: Missouri University of Science and Technology is a education organization based out in Rolla, Missouri, United States. It is known for research contribution in the topics: Control theory & Artificial neural network. The organization has 9380 authors who have published 21161 publications receiving 462544 citations. The organization is also known as: Missouri S&T & University of Missouri–Rolla.

Reversible CO Scavenging via Adsorbate-Dependent Spin State Transitions in an Iron(II)–Triazolate Metal–Organic Framework

Abstract: A new metal–organic framework, Fe-BTTri (Fe3[(Fe4Cl)3(BTTri)8]2·18CH3OH, H3BTTri =1,3,5-tris(1H-1,2,3-triazol-5-yl)benzene)), is found to be highly selective in the adsorption of CO over a variety of other gas molecules, making it extremely effective, for example, in the removal of trace CO from mixtures with H2, N2, and CH4. This framework not only displays significant CO adsorption capacity at very low pressures (1.45 mmol/g at just 100 μbar), but, importantly, also exhibits readily reversible CO binding. Fe-BTTri utilizes a unique spin state change mechanism to bind CO in which the coordinatively unsaturated, high-spin FeII centers of the framework convert to octahedral, low-spin FeII centers upon CO coordination. Desorption of CO converts the FeII sites back to a high-spin ground state, enabling the facile regeneration and recyclability of the material. This spin state change is supported by characterization via infrared spectroscopy, single crystal X-ray analysis, Mossbauer spectroscopy, and magnetic...

HARKE: Human Activity Recognition from Kinetic Energy Harvesting Data in Wearable Devices

Abstract: Kinetic energy harvesting (KEH) may help combat battery issues in wearable devices. While the primary objective of KEH is to generate energy from human activities, the harvested energy itself contains information about human activities that most wearable devices try to detect using motion sensors. In principle, it is therefore possible to use KEH both as a power generator and a sensor for human activity recognition (HAR), saving sensor-related power consumption. Our aim is to quantify the potential of human activity recognition from kinetic energy harvesting (HARKE). We evaluate the performance of HARKE using two independent datasets: (i) a public accelerometer dataset converted into KEH data through theoretical modeling; and (ii) a real KEH dataset collected from volunteers performing activities of daily living while wearing a data-logger that we built of a piezoelectric energy harvester. Our results show that HARKE achieves an accuracy of 80 to 95 percent, depending on the dataset and the placement of the device on the human body. We conduct detailed power consumption measurements to understand and quantify the power saving opportunity of HARKE. The results demonstrate that HARKE can save 79 percent of the overall system power consumption of conventional accelerometer-based HAR.