Ab initio Molecular Dynamics Investigations of the Speciation and Reactivity of Deep Eutectic Electrolytes in Aluminum Batteries.

Invited for this month's cover is the Section for Atomic Scale Materials Modelling led by Prof. Tejs Vegge at the Department of Energy Conversion and Storage, Technical University of Denmark. The central image of the cover picture illustrates one of the chemical reaction mechanisms observed in a deep eutectic electrolyte formed by AlCl3 and urea. This is a promising electrolyte for inexpensive and environmentally friendly next-generation batteries based on aluminum. We have developed the computational techniques needed to identify chemical species and track reaction mechanisms across an ab initio molecular dynamics trajectory. The reaction mechanisms and speciation observed help to gain more insight in the development of such batteries. The Full Paper itself is available at 10.1002/cssc.202100163.

Ab initio Molecular Dynamics Investigations of the Speciation and Reactivity of Deep Eutectic Electrolytes in Aluminum Batteries.

Deep eutectic solvents (DESs) have emerged as an alternative for conventional ionic liquids in aluminum batteries. Elucidating DESs composition is fundamental to understand aluminum electrodeposition in the battery anode. Despite numerous experimental efforts, the speciation of these DESs remains elusive. This work shows how ab initio molecular dynamics (AIMD) simulations can shed light on the molecular composition of DESs. For the particular example of AlCl3 :urea, one of the most popular DESs, we carried out a systematic AIMD study, showing how an excess of AlCl3 in the AlCl3 :urea mixture promotes the stability of ionic species vs neutral ones and also favors the reactivity in the system. These two facts explain the experimentally observed enhanced electrochemical activity in salt-rich DESs. We also observe the transfer of simple [AlClx (urea)y ] clusters between different species in the liquid, giving rise to free [AlCl4 ]- units. The small size of these [AlCl4 ]- units favors the transport of ionic species towards the anode, facilitating the electrodeposition of aluminum.

Combined DFT and Differential Electrochemical Mass Spectrometry Investigation of the Effect of Dopants in Secondary Zinc-Air Batteries.

Zinc-air batteries offer the potential of low-cost energy storage with high specific energy, but at present secondary Zn-air batteries suffer from poor cyclability. To develop economically viable secondary Zn-air batteries, several properties need to be improved: choking of the cathode, catalyzing the oxygen evolution and reduction reactions, limiting dendrite formation and suppressing the hydrogen evolution reaction (HER). Understanding and alleviating HER at the negative electrode in a secondary Zn-air battery is a substantial challenge, for which it is necessary to combine computational and experimental research. Here, we combine differential electrochemical mass spectrometry (DEMS) and density functional theory (DFT) calculations to investigate the fundamental role and stability when cycling in the presence of selected beneficial additives, that is, In and Bi, and Ag as a potentially unfavorable additive. We show that both In and Bi have the desired property for a secondary battery, that is, upon recharging they will remain on the surface, thereby retaining the beneficial effects on Zn dissolution and suppression of HER. This is confirmed by DEMS, where it is observed that In reduces HER and Bi affects the discharge potential beneficially compared to a battery without additives. Using a simple procedure based on adsorption energies calculated with DFT, it is found that Ag suppresses OH adsorption, but, unlike In and Bi, it does not hinder HER. Finally, it is shown that mixing In and Bi is beneficial compared to the additives by themselves as it improves the electrochemical performance and cyclic stability of the secondary Zn-air battery.

The atomic simulation environment-a Python library for working with atoms.

The atomic simulation environment (ASE) is a software package written in the Python programming language with the aim of setting up, steering, and analyzing atomistic simulations. In ASE, tasks are fully scripted in Python. The powerful syntax of Python combined with the NumPy array library make it possible to perform very complex simulation tasks. For example, a sequence of calculations may be performed with the use of a simple 'for-loop' construction. Calculations of energy, forces, stresses and other quantities are performed through interfaces to many external electronic structure codes or force fields using a uniform interface. On top of this calculator interface, ASE provides modules for performing many standard simulation tasks such as structure optimization, molecular dynamics, handling of constraints and performing nudged elastic band calculations.

Decoupling strain and ligand effects in ternary nanoparticles for improved ORR electrocatalysis.

Ternary Pt-Au-M (M = 3d transition metal) nanoparticles show reduced OH adsorption energies and improved activity for the oxygen reduction reaction (ORR) compared to pure Pt nanoparticles, as obtained by density functional theory. The strain and ligand effects in nanoparticles are decoupled and correlated with the extended Pt(111) surface for benchmarking. The ternary metal in the core allows for tuning the catalytic activity through strain effects. Pt-Au-M for M = Cr, Mn, Co, Cu, Zn nanoparticles are of particular interest as they exhibit an optimal contribution of strain, ligand effects and stability. Good agreement is found with experimental studies showing increased activity of Pt-Au-Fe/Ni nanoparticles, and mid to late 3d transition metals are predicted to exhibit enhance activity and stability with respect to pure Pt nanoparticles.

A DFT-based genetic algorithm search for AuCu nanoalloy electrocatalysts for CO2 reduction.

Using a DFT-based genetic algorithm (GA) approach, we have determined the most stable structure and stoichiometry of a 309-atom icosahedral AuCu nanoalloy, for potential use as an electrocatalyst for CO2 reduction. The identified core-shell nano-particle consists of a copper core interspersed with gold atoms having only copper neighbors and a gold surface with a few copper atoms in the terraces. We also present an adsorbate-dependent correction scheme, which enables an accurate determination of adsorption energies using a computationally fast, localized LCAO-basis set. These show that it is possible to use the LCAO mode to obtain a realistic estimate of the molecular chemisorption energy for systems where the computation in normal grid mode is not computationally feasible. These corrections are employed when calculating adsorption energies on the Cu, Au and most stable mixed particles. This shows that the mixed Cu135@Au174 core-shell nanoalloy has a similar adsorption energy, for the most favorable site, as a pure gold nano-particle. Cu, however, has the effect of stabilizing the icosahedral structure because Au particles are easily distorted when adding adsorbates.

Designing mixed metal halide ammines for ammonia storage using density functional theory and genetic algorithms.

Metal halide ammines have great potential as a future, high-density energy carrier in vehicles. So far known materials, e.g. Mg(NH3)6Cl2 and Sr(NH3)8Cl2, are not suitable for automotive, fuel cell applications, because the release of ammonia is a multi-step reaction, requiring too much heat to be supplied, making the total efficiency lower. Here, we apply density functional theory (DFT) calculations to predict new mixed metal halide ammines with improved storage capacities and the ability to release the stored ammonia in one step, at temperatures suitable for system integration with polymer electrolyte membrane fuel cells (PEMFC). We use genetic algorithms (GAs) to search for materials containing up to three different metals (alkaline-earth, 3d and 4d) and two different halides (Cl, Br and I) - almost 27,000 combinations, and have identified novel mixtures, with significantly improved storage capacities. The size of the search space and the chosen fitness function make it possible to verify that the found candidates are the best possible candidates in the search space, proving that the GA implementation is ideal for this kind of computational materials design, requiring calculations on less than two percent of the candidates to identify the global optimum.

Surface adsorption in strontium chloride ammines.

An adsorbed state and its implications on the ab- and desorption kinetics of ammonia in strontium chloride ammine is identified using a combination of ammonia absorption measurements, thermogravimetric analysis, and density functional theory calculations. During thermogravimetric analysis, ammonia desorption originating from the adsorbed state is directly observed below the bulk desorption temperature, as confirmed by density functional theory calculations. The desorption enthalpy of the adsorbed state of strontium chloride octa-ammine is determined with both techniques to be around 37-39 kJ∕mol. A simple kinetic model is proposed that accounts for the absorption of ammonia through the adsorbed state.