Strategies to Improve the Energy Storage Properties of Perovskite Lead-Free Relaxor Ferroelectrics: A Review

TL;DR

This review discusses strategies to enhance the energy storage capabilities of lead-free relaxor ferroelectric perovskites, emphasizing chemical modifications, additives, and advanced processing techniques to achieve higher energy densities for electronic applications.

Contribution

It provides a comprehensive overview of recent methods and advancements in improving energy density in lead-free relaxor ferroelectric materials, including computational design trends.

Findings

Chemical modifications improve energy density.

Additives enhance relaxor behavior.

Advanced processing techniques optimize material performance.

Abstract

Electrical energy storage systems (EESSs) with high energy density and power density are essential for the effective miniaturization of future electronic devices. Among different EESSs available in the market, dielectric capacitors relying on swift electronic and ionic polarization-based mechanisms to store and deliver energy already demonstrate high power densities. However, different intrinsic and extrinsic contributions to energy dissipations prevent ceramic-based dielectric capacitors from reaching high recoverable energy density levels. Interestingly, relaxor ferroelectric-based dielectric capacitors, because of their low remnant polarization, show relatively high energy density and thus display great potential for applications requiring high energy density properties. Here, some of the main strategies to improve the energy density properties of perovskite lead-free relaxor systems are reviewed. This includes (i) chemical modification at different crystallographic sites, (ii) chemical additives that do not target lattice sites and (iii) novel processing approaches dedicated to bulk ceramics, thick and thin films, respectively. Recent advancements are summarized concerning the search for relaxor materials with superior energy density properties and the appropriate choice of both composition and processing route to match various needs in the application. Finally, future trends in computationally-aided materials design are presented.