Promoting lithium-ion battery performance by application of crystalline cathodes LixMn1−zFezPO4

Computational Framework Combining Quantum Mechanics, Molecular Dynamics, and Deep Neural Networks to Evaluate the Intrinsic Properties of Materials

The design and evaluation of future nanomaterials with specific properties is a challenging task as the current traditional methods rely on trial and error approaches that are time-consuming and expensive. On the computational front, design tools such as molecular dynamics (MD) simulations help us reduce the costs and times. However, nonbonded potential parameters, the key input parameters for an MD simulation, are usually not available for designing and studying new materials. Resolving this, quantum mechanics (QM) calculations could be used to evaluate the system's energy as a function of the nonbonded distances, and the resulting data set could be fit to a generic potential equation to obtain the fitting constants (potential parameters). However, fitting this massive data set containing thousands of unknown parameters using traditional mathematical formulations is not feasible. Hence, most computational frameworks in the literature utilize several simplifications, leading to a severe loss of accuracy. Addressing this deficiency, in this work, we propose a multi-scale framework that couples QM calculations and MD with advanced deep neural networks to determine the potential parameters. This advanced framework has been extensively validated by employing it to predict properties such as the density, boiling point, and melting point of five different types of molecules that are well-understood, namely, the polar molecule H2O, ionic compound LiPF6, ethanol (C2H5OH), long-chain molecule C8H18, and the complex molecular system ethylene carbonate (EC).

Extension of the TraPPE Force Field for Battery Electrolyte Solvents

Optimizing electrolyte formulations is key to improving performance of Li-/Na-ion batteries, where transport properties (diffusion coefficient, viscosity) and permittivity need to be predicted as functions of temperature, salt concentration and solvent composition. More efficient and reliable simulation models are urgently needed, owing to the high cost of experimental methods and the lack of united-atom molecular dynamics force fields validated for electrolyte solvents. Here the computationally efficient TraPPE united-atom force field is extended to be compatible with carbonate solvents, optimizing the charges and dihedral potential. Computing the properties of electrolyte solvents, ethylene carbonate (EC), propylene carbonate (PC), dimethyl carbonate (DMC), diethyl carbonate (DEC), and dimethoxyethane (DME), we observe that the average absolute errors in the density, self-diffusion coefficient, permittivity, viscosity, and surface tension are approximately 15% of the corresponding experimental values. Results compare favorably to all-atom CHARMM and OPLS-AA force fields, offering computational performance improvement of at least 80%. We further use TraPPE to predict the structure and properties of LiPF₆ salt in these solvents and their mixtures. EC and PC form complete solvation shells around Li⁺ ions, while the salt in DMC forms chain-like structures. In the poorest solvent, DME, LiPF₆ forms globular clusters despite DME's higher permittivity than DMC.

Simple synthesis of a hierarchical LiMn0.8Fe0.2PO4/C cathode by investigation of iron sources for lithium-ion batteries

Iron (Fe) substitution is an effective strategy for improving the electrochemical performance of LiMnPO₄ which has poor conductivity. Herein, we focus on investigating the effect of substitution of Mn with different iron sources, on the structure and electrochemical performances of the LiMnPO₄ materials. The Fe-substituted LiMnPO₄/C composites were synthesized via a simple and rational solid-state method, and will be of benefit for engineering applications. The characterization of the materials shows an obvious influence of the iron sources on structure and morphology. The N-LMFP material prepared using soluble FeNO₃ as iron sources exhibits an excellent rate capacity of 122 mA h g⁻¹ at 5C, and superior cyclability with a capacity retention of 98.9% after 400 cycles at 2C. The enhanced rate capability and cycling stability of N-LMFP are the result of the lowered electron/ion resistance and the improved reversibility of the reaction, that originates from the homogeneous fine particles and hierarchical structure with large mesopores. This research provides significant guidelines for designing an LiMnPO₄ cathode with a high performance.

A hierarchical porous LiMn_0.8Fe_0.2PO₄/C (N-LMFP) was synthesized by a simple solid-state method beneficial for engineering applications. The fine particle and hierarchical porous structure enable a superior rate performance of the N-LMFP sample.

Hydroxypropyl methylcellulose-controlled in vitro calcium phosphate biomineralization

Scanning pyroelectric microscopy of DCPD single crystals.

Multiscale Investigation of the Diffusion Mechanism within the Solid-Electrolyte Interface Layer: Coupling Quantum Mechanics, Molecular Dynamics, and Macroscale Mathematical Modeling

The solid-electrolyte interface (SEI) layer has a critical role in Li-ion batteries' (LIBs) life span. The SEI layer, even in modern commercial LIBs, is responsible for more than 50% of capacity loss. Due to the inherent complexity in studying the SEI layer, many aspects of its performance and characteristics, including diffusion mechanisms in this layer, are unknown. As a result, most mathematical models use a constant value of the diffusion coefficient, instead of a variable formulation, to predict LIBs' properties and performance such as capacity fading and the SEI growth rate. In this work, by employing a multiscale investigation using a combination of quantum mechanics, molecular dynamics, and macroscale mathematical modeling, some equations are presented to evaluate the energy barrier against diffusion and the diffusion coefficient in different crystal structures in the inner section of the SEI layer. The equations are evaluated as a function of temperature and concentration and can be used to study the diffusion mechanism in the SEI layer. They can also be integrated with other mathematical models of LIBs to increase the accuracy of the latter.

Ternary Cathode Blend Electrodes for Environmentally Friendly Lithium-Ion Batteries

The combination of two active materials into one positive electrode of a lithium-ion battery is an uncomplicated and cost-effective way to combine the advantages of different active materials while reducing the disadvantages of each material. In this work, the concept of binary blends is extended to ternary compositions. The combination of three different active materials provides high versatility in designing the properties of an electrode. Therefore, the unique properties of a layered oxide, phospho-olivine, and spinel type material are mixed to design a high-energy cathode with improved environmental friendliness. Four different compositions of blend electrodes are investigated, each with individual benefits. Synergistic effects improved the rate capability, power density, thermal and chemical stability simultaneously. The blend electrode consisting of 75 % NMC, 12.5 % LMFP and LMO provides similar energy and power density as a pure NMC electrode while economizing 25 % cobalt and nickel.

Effect of salt concentration on properties of mixed carbonate-based electrolyte for Li-ion batteries: a molecular dynamics simulation study

In this work, a computational framework is proposed by utilizing molecular dynamics simulation to explore the existing relation between molecular structure and ionic conductivity of the electrolyte system [LiPF₆+(EC+DMC 1:1)] consisting of a mixture of cyclic ethylene carbonate (EC) and acyclic dimethyl carbonate (DMC) solvents and lithium hexafluorophosphate (LiPF₆) salt to propose as a novel mixed organic solvent-based electrolytes to promote the performance of lithium-ion batteries (LIBs). To acquire a clear understanding of the structural and transport properties of the designed electrolytes, quantum chemistry (QC) calculations and molecular dynamics (MD) simulation are used. In the first step, the accurate molecular structures of the studied electrolytes in addition to their corresponding atomic partial charges are evaluated. The MD simulations are performed at 330 K varying the LiPF₆ concentration (0.5 M to 2.2 M). Analysis of the obtained results indicated that ionic diffusivity and conductivity of the electrolytes are dependent on the structure of solvated ions and lithium salt (LiPF₆) concentration. It is found that the obtained MD simulation results are in reasonable agreement with experimental results. Graphical abstract A representation of dependence of transport properties of electrolyte system [LiPF6 +(EC+DMC 1:1)] as function of salt concentration to be used in Lithium-ion batteries (LIBs).