Enhanced Li-ion diffusion and electrochemical performance in strained-manganese-iron oxide core-shell nanoparticles.

Efforts to improve energy storage depend greatly on the development of efficient electrode materials. Recently, strain has been employed as an alternate approach to improve ion mobility. While lattice strain has been well-researched in catalytic applications, its effects on electrochemical energy storage are largely limited to computational studies due to complexities associated with strain control in nanomaterials as well as loss of strain due to the phase change of the active material during charging-discharging. In this work, we overcome these challenges and investigate the effects of strain on supercapacitor performance in Li-ion-based energy devices. We synthesize epitaxial Fe3O4@MnFe2O4 (core@shell) nanoparticles with varying shell thickness to control the lattice strain. A narrow voltage window for electrochemical testing is used to limit the storage mechanism to lithiation-delithiation, preventing a phase change and maintaining structural strain. Cyclic voltammetry reveals a pseudocapacitive behavior and similar levels of surface charge storage in both strained- and unstrained-MnFe2O4 samples; however, diffusive charge storage in the strained sample is twice as high as the unstrained sample. The strained-MnFe2O4 electrode exceeds the performance of the unstrained-MnFe2O4 electrode in energy density by ∼33%, power density by ∼28%, and specific capacitance by ∼48%. Density functional theory shows lower formation energies for Li-intercalation and lower activation barrier for Li-diffusion in strained-MnFe2O4, corresponding to a threefold increase in the diffusion coefficient. The enhanced Li-ion diffusion rate in the strained-electrodes is further confirmed using the galvanostatic intermittent titration technique. This work provides a starting point to using strain engineering as a novel approach for designing high performance energy storage devices.

Hydrogen transfer through different crystal phases of nickel oxy/hydroxide.

Nickel hydroxide phases are common in several energy conversion devices including battery electrochemical cell electrodes. These materials have a unique layered structure that may facilitate hydrogen transfer between oxygen sites and allow using this material also for proton conducting fuel cells. In order to assess this functionality, we use Density Functional Theory+U together with the Nudged Elastic Band method to calculate minimum energy diffusion paths and hydrogen vacancy formation energies of different crystal phases of NiOOH including ß-NiOOH, ß-Ni(OH)2 and α-Ni(OH)2. We follow several diffusion paths and mechanisms in several phases, both across layers and through them. We pin down the reason for efficient diffusion laterally on a layer and explain why diffusion through a layer is impossible. Our results suggest that hydrogen transfer may be possible for the ß-NiOOH phase with hydrogen added interstitially and transferred along the layers. This study significantly advances our understanding of diffusion in an uncommonly structured material.

Identifying the bottleneck of water oxidation by ab initio analysis of in situ optical absorbance spectrum.

Hematite's (α-Fe2O3) major limitation to efficiently splitting water using sunlight is the low rate of the oxygen evolution reaction (OER). Thus, identifying the OER rate limiting step is a cornerstone to enhancing the current under low applied potential. Different measurement techniques showed similar absorption difference spectra during a change in applied potential on the hematite anode below and above the onset of the OER in the dark and under light. This absorption change was shown to result from surface modification during the OER, but the specific surface species could not be resolved. On the basis of ab initio calculations, we analyze the calculated absorption spectra in relation to previous measurements. We provide for the first time solid evidence to specify H2O + *O → *OOH + H+ + e- as the rate limiting step and *O as the bottleneck intermediate of the hematite OER.

From density functional theory to machine learning predictive models for electrical properties of spinel oxides.

This work focuses on predicting and characterizing the electronic conductivity of spinel oxides, which are promising materials for energy storage devices and for the oxygen evolution and oxygen reduction reactions due to their attractive properties and abundance of transition metals that can act as active sites for catalysis. To this end, a new database was developed from first principles, including band structure and conductivity properties of spinel oxides, and machine learning algorithms were trained on this database to predict electronic conductivity and band gaps based solely on the compositions. The models developed in this study are scaled from the quantum level up to a continuum conductivity model. The relatively small database used in this study allowed for accurate predictions of band gap and conductivity. By altering the composition of spinel oxides, the model was able to predict high conductivity for spinels with high nickel content and to match experimental trends for manganese cobalt spinels. The ability to predict material properties is especially important in energy conversion devices such as batteries and supercapacitors where redox reactions take place.