Combined Experimental and Computational Study of Molybdenum and Niobium for Nuclear Sensor Application
Source abstract
Abstract
Due to their novel electromagnetic and thermal properties, molybdenum (Mo) and niobium (Nb) become optimal temperature sensor materials for nuclear energy applications. We leveraged voltage recorded during a heat ramp to tune a computational method to predict the Seebeck electromotive force (EMF) of Mo and Nb. Using a combined Density Functional Theory (DFT) and Boltzmann Transport Equations (BTE) method the voltage was predicted but did not include the effects of temperature on atomic structure. Combining Ab Initio Molecular Dynamics (AIMD) and BTE included temperature effects on structure optimization and yielded voltages in a good agreement with experiment. Lanthanum (La) and Phosphorus (P) additives in Mo and Nb, respectively, could increase the EMF compared to those of the pure metals. The presence of oxygen (O) in Mo increases the EMF while O in Nb slightly reduces the EMF. Our studies suggested that heat treatment-induced structural changes that lead to a reduction in voltage occur not only at the mesoscale as previously understood but also at the atomic scale.
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Research context
OpenAlex topics: Advanced Chemical Physics Studies · Nuclear Materials and Properties · Machine Learning in Materials Science
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