Energy storage is required in scale, to enable the transition from the current carbon- based to a renewable-energy driven economy. Hence shifting towards a paradigm wherein there is greater reliance on earth abundant materials for developing energy storage technologies is essential. Ceria is among the rare earth oxides that are well distributed and it exhibits promising redox behaviour (Ce4+/Ce3+) [1]. However, ceria also suffers from a bottleneck. It provides only moderate specific capacitance; hence given its relatively abundant global distribution, it deserves attention, especially with regards to improved performance in supercapacitor applications [2-4]. Hence this focuses on three major themes with ceria as the material of focus: (i) capacitive enhancement, (ii) computational performance prediction driven materials design, (iii) performance evaluation.

To begin with, we demonstrate two approaches to enhance supercapacitive performance of ceria: (i) the role of doping, and (ii) use of redox-additives in the traditional alkaline aqueous electrolyte. For substitutional doping, different dopants are used with primary oxidation states as 4+, and 3+. Dopant is found to aid in enhancement of (a) the oxygen-vacancy concentration, and (b) lattice oxygen mobility, compared to pristine ceria [5]. Hence, doping standalone is shown to offer significant improvement of specific capacitance over pristine ceria [6].

We also demonstrate another strategy that can offer supercapacitive enhancement. A redox- additive [K3Fe(CN)6] is used to enhance the specific capacitance of the supercapacitor. However, despite the enhancement reported by us (also consistent with recent reports from

other groups), from a technology-development standpoint, we show that there are challenges associated with a lack of standard techniques to measure the redox-additive based device- parameters [7]. To address this concern, we have developed a new method that enables the delineation of the contribution of the effective-mass of the redox-additive on the electrode- surface. This method is expected to be generic and will be of use to evaluate other redox additive based supercapacitor devices as well [8].

For enabling materials design a Mott-Littleton based method is developed to choose the more useful dopant; the design is then validated in a practical device [9]. Thereafter, machine learning based paradigm is used to predict the specific capacitance and cyclic stability of the material (ceria-based oxides, and oxynitrides) are used. Important outcomes of our proof-of- concept prediction-validation work on supercapacitors are (I) a single predictor namely the oxygen vacancy formation energy - as elucidated by the Mott-Littleton method is found to be a good measure of suitability, and (II) data-driven approach is proven to be handy in predicting the performance of a novel material. The thesis will edge towards conclusion through an engineering validation of II.
References:

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