Electrical transport properties in LiMn2O4

Oxygen vacancy chemistry in oxide cathodes

Secondary batteries are a core technology for clean energy storage and conversion systems, to reduce environmental pollution and alleviate the energy crisis. Oxide cathodes play a vital role in revolutionizing battery technology due to their high capacity and voltage for oxide-based batteries. However, oxygen vacancies (OVs) are an essential type of defect that exist predominantly in both the bulk and surface regions of transition metal (TM) oxide batteries, and have a crucial impact on battery performance. This paper reviews previous studies from the past few decades that have investigated the intrinsic and anionic redox-mediated OVs in the field of secondary batteries. We focus on discussing the formation and evolution of these OVs from both thermodynamic and kinetic perspectives, as well as their impact on the thermodynamic and kinetic properties of oxide cathodes. Finally, we offer insights into the utilization of OVs to enhance the energy density and lifespan of batteries. We expect that this review will advance our understanding of the role of OVs and subsequently boost the development of high-performance electrode materials for next-generation energy storage devices.

Local electronic transport across probe/ionic conductor interface in scanning probe microscopy

Charge carrier transport through the probe-sample junction can have substantial consequences for outcomes of electrical and electromechanical atomic-force-microscopy (AFM) measurements. For understanding physical processes under the probe, we carried out conductive-AFM (C-AFM) measurements of local current-voltage (I-V) curves as well as their derivatives on samples of a mixed ionic-electronic conductor Li_1-xMn₂O₄ and developed an analytical framework for the data analysis. The implemented approach discriminates between contributions the highly resistive sample surface layer and the bulk with the account of ion redistribution in the field of the probe. It was found that, with increasing probe voltage, the conductance mechanism in the surface layer transforms from Pool-Frenkel to space-charge-limited current. The surface layer significantly alters the ion dynamics in the sample bulk under the probe, which leads, in particular, to a decrease of the effective electromechanical AFM signal associated with the ionic motion in the sample. The framework can be applied for the analysis of electronic transport mechanisms across the probe/sample interface as well as to uncover the role of the charge transport in the electric field distribution, mechanical, and other responses in AFM measurements of a broad spectrum of conducting materials.

Predicting oxidation and spin states by high-dimensional neural networks: Applications to lithium manganese oxide spinels

Lithium ion batteries often contain transition metal oxides such as Li_xMn₂O₄ (0 ≤ x ≤ 2). Depending on the Li content, different ratios of Mn^III to Mn^IV ions are present. In combination with electron hopping, the Jahn-Teller distortions of the Mn^IIIO₆ octahedra can give rise to complex phenomena such as structural transitions and conductance. While for small model systems oxidation and spin states can be determined using density functional theory (DFT), the investigation of dynamical phenomena by DFT is too demanding. Previously, we have shown that a high-dimensional neural network potential can extend molecular dynamics (MD) simulations of Li_xMn₂O₄ to nanosecond time scales, but these simulations did not provide information about the electronic structure. Here, we extend the use of neural networks to the prediction of atomic oxidation and spin states. The resulting high-dimensional neural network is able to predict the spins of the Mn ions with an error of only 0.03 ℏ. We find that the Mn e_g electrons are correctly conserved and that the number of Jahn-Teller distorted Mn^IIIO₆ octahedra is predicted precisely for different Li loadings. A charge ordering transition is observed between 280 K and 300 K, which matches resistivity measurements. Moreover, the activation energy of the electron hopping conduction above the phase transition is predicted to be 0.18 eV, deviating only 0.02 eV from experiment. This work demonstrates that machine learning is able to provide an accurate representation of both the geometric and the electronic structure dynamics of Li_xMn₂O₄ on time and length scales that are not accessible by ab initio MD.

Comprehensive Enhancement of Nanostructured Lithium-Ion Battery Cathode Materials via Conformal Graphene Dispersion

Efficient energy storage systems based on lithium-ion batteries represent a critical technology across many sectors including consumer electronics, electrified transportation, and a smart grid accommodating intermittent renewable energy sources. Nanostructured electrode materials present compelling opportunities for high-performance lithium-ion batteries, but inherent problems related to the high surface area to volume ratios at the nanometer-scale have impeded their adoption for commercial applications. Here, we demonstrate a materials and processing platform that realizes high-performance nanostructured lithium manganese oxide (nano-LMO) spinel cathodes with conformal graphene coatings as a conductive additive. The resulting nanostructured composite cathodes concurrently resolve multiple problems that have plagued nanoparticle-based lithium-ion battery electrodes including low packing density, high additive content, and poor cycling stability. Moreover, this strategy enhances the intrinsic advantages of nano-LMO, resulting in extraordinary rate capability and low temperature performance. With 75% capacity retention at a 20C cycling rate at room temperature and nearly full capacity retention at -20 °C, this work advances lithium-ion battery technology into unprecedented regimes of operation.