Reactive evaporation of Cr from stainless steels used in solid oxide electrochemical systems, such as solid oxide fuel cell (SOFC) and solid oxide electrolysis cell (SOEC) systems, is well-documented as the cause of Cr poisoning; however, the condensation and interactions of volatilized Cr species onto and with surrounding interfaces during complex and dynamic system exposures is less understood. Understanding these interactions during operation is critical for improving system performance and safeguarding environmental, health and safety, as some condensed Cr forms toxic hexavalent chromium (Cr(VI)) species. The objective of this study is to investigate and report on condensation pathways of Cr vapors within representative high-temperature system environments. To accomplish this objective, Cr vapors, produced by high-temperature (800°C) air exposures of trivalent chromium (Cr(III)) oxide (Cr2O3) powder with variable moisture content, were condensed onto various ceramic materials at lower temperatures (<400°C). Both the total amount of Cr and ratios of oxidation states were measured using ICP-MS and colorimetric analyses. An increase of Cr condensation was observed with increased water content, with similar Cr(VI) to Cr(III) ratios. Results and interpretations are discussed in context of improved understanding of Cr reactive evaporation and condensation in SOFC/SOEC and related high-temperature materials and systems.
Smart wheelchairs with semi or fully autonomous functions, can greatly improve the mobility of physically impaired persons. However, most are controlled using inputs that require physical manipulation (e.g. joystick controllers) and for persons with severe physical impairments this method of control can be too demanding. A noninvasive brain-computer interface (BCI) technology-based controller could bridge between the smart wheelchairs users and physically impaired persons with severe conditions. Current BCI controlled wheelchairs rely on detecting steady-state visually evoked potential (SSVEP) responses as these typically have the greatest data transfer rate. However, this method requires the user to focus on a screen for an extended period of time. This causes strain on the user and takes their attention away from their surroundings, which could be dangerous in a scenario that requires navigation around multiple moving objects. The focus of this project is to design a hybrid BCI controller using an electroencephalogram (EEG) headset to detect hand motor imagery (MI) and jaw electromyography (EMG) signals to control a smart wheelchair in conjunction with its semi-autonomous capabilities. A controller of this kind is well-known to have low data transfer rates, and therefore has lower accuracy and longer response times as compared to other controllers. However, a properly structured controller hierarchy between the BCI controller and semi-autonomous system is developed to compensate the limitations of the controller’s accuracy.
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