Urbanization with increasing demand for energy at a rapid pace has prompted researchers to explore effective and efficient energy storage technology. Fuel cells, being well-known among other existing devices, are categorized based on the nature of the electrolyte employed. In several areas, proton conduction solid oxide fuel cells have surpassed conventional solid oxide fuel cells. Nonetheless, there still prevail drawbacks accompanied with the proton-conducting electrolyte materials. However, the disadvantages associated with proton-conducting electrolytic materials persists. Besides chemical stability, one of the significant concerns is the fluctuation in proton conductivity among acceptor-doped compositions. Recent introspection interprets the proton trapping effect in the vicinity of the substituent attenuating proton mobility, the fundamentals of which point toward a proton-dopant complex interaction and altering basicity of the dopant neighboring oxygen atoms, escalating the activation energy. This implies a pronounced affinity of proton-trapped sites in the close coordination of the acceptor dopant while implying trap-free sites elsewhere. In the following review article, we direct our attention to the assimilation of factors responsible for the genesis of proton trapping sites with an additional motive to explore the optimal composition while achieving maximum productivity.

Proton conductors (PCs) are multifunctional perovskite materials with structural, electrical, and chemical compatibilities that serve as principal components in proton conducting solid oxide fuel cells (PC-SOFC). The synergetic advantages of PC-SOFC dominate lucrative applications of conventional SOFC. However, adequate advantages accompany certain key limitations in PCs. The inverse interplay between proton conductivity and chemical stability are two broader challenges which demonstrate a clear trade-off. As a consequence, different reactive constituents within the host BaCeO 3 and BaZrO 3 PCs enforce the gradient in chemical stability under diverse atmospheres, while altered charge chemistry and structural perturbations escalate the activation barrier and impose reduced proton conductivity. Such occurrences are more pronounced in acceptor-doped PCs. Although material engineering via acceptor defect substitutions assists protonation and charge dynamics, on the other hand, it provokes novel challenges (proton trapping effect) at a higher dopant volume fraction beyond the threshold. The principal entropy among chemical and electrophysical parameters is proportionally associated with dopant-incorporated subcategorical material limitations. In this study, a compilation of factors responsible for structural and electrophysical diversities as a consequence of compositional-induced symmetry variations is highlighted with a plausible conclusion and future research perspectives for smart and advanced energy applications.

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Aerosols are an integral part of the earth's climate system and their effect on climate makes this field a relevant research problem. The artificial neural network (ANN) technique is an upcoming technique in different research fields. In the current work, we have evaluated the performance of an ANN with its parameters in simulating the aerosol's properties. ANN evaluation is performed over three sites (Kanpur, Jaipur, and Gandhi College) in the Indian region. We evaluated the performance of ANN for model's hyperparameter (number of hidden layers) and optimizer's hyperparameters (learning rate and number of iterations). The optical properties of aerosols from AERONET (AErosol RObotic NETwork) are used as input to ANN to estimate the aerosol optical depth (AOD) and Angstrom exponent. Results emphasized the need for optimal learning rate values and the number of iterations to get accurate results with low computational cost and to avoid overfitting. We observed a 23–25% increase in computational time with an increase in iteration. Thus, a meticulous selection of these parameters should be made for accurate estimations. The result indicates that the developed ANN can be utilized to derive AOD, which is not assessed at AERONET stations.