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Summary: Here, we present a protocol for developing an inorganic-organic hybrid interphase layer using the self-assembled monolayers technique to enhance the surface of the lithium metal anode. We describe steps for extracting organic molecules from open-sourced databases and calculating their microscopic properties. We then detail procedures for developing a machine learning model for predicting the ionic diffusion barrier and preparing the inputs for prediction. This protocol enables a cost-effective workflow to identify promising self-assembled monolayers with exceptional performance.For complete details on the use and execution of this protocol, please refer to Zhang et al. (2023).1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

In this work, we use density functional theory to investigate the electronic structure of poly(3,4-ethylenedioxythiophene) (PEDOT) oligomers with co-located AlCl4− anions, a promising combination for energy storage. The 1980s bipolaron model remains the dominant interpretation of the electronic structure of PEDOT despite recent theoretical progress that has provided new definitions of bipolarons and polarons. By considering the influence of oligomer length, oxidation or anion concentration and spin state, we find no evidence for many of the assertions of the 1980s bipolaron model and so further contribute to a new understanding. No self-localisation of positive charges in PEDOT is found, as predicted by the bipolaron model at the hybrid functional level. Instead, our results show distortions that exhibit a single or a double peak in bond length alternations and charge density. Either can occur at different oxidation or anion concentrations. Rather than representing bipolarons or polaron pairs in the original model, these are electron distributions driven by a range of factors. Distortions can span an arbitrary number of nearby anions. We also contribute a novel conductivity hypothesis. Conductivity in conducting polymers has been observed to reduce at anion concentrations above 0.5. We show that at high anion concentrations, the energy of the localised, non-bonding anionic orbitals approaches that of the system HOMO due to Coulombic repulsion between anions. We hypothesize that with nucleic motion in the macropolymer, these orbitals will interfere with the hopping of charge carriers between sites of similar energy, lowering conductivity.

Abstract Alloying is an established strategy to tune the properties of bulk compounds for desired applications. With the advent of nanotechnology, the same strategy can be applied to 2D materials for technological applications, like single-layer transistors and solid lubricants. Here we present a systematic analysis of the phase behaviour of substitutional 2D alloys in the Transition Metal Disulfides (TMD) family. The phase behaviour is quantified in terms of a metastability metric and benchmarked against many-body expansion of the energy landscape. We show how the metastability metric can be directly used as starting point for setting up rational search strategies in phase space, thus allowing for targeted further computational prediction and analysis of properties. The results presented here also constitute a useful guideline for synthesis of TMDs binary alloys via a range of synthesis techniques.

Summary: Oxygen redox electrocatalysis is the crucial electrode reaction among new-era energy sources. The prerequisite to rationally design an ideal electrocatalyst is accurately identifying the structure-activity relationship based on the so-called descriptors which link the catalytic performance with structural properties. However, the quick discovery of those descriptors remains challenging. In recent, the high-throughput computing and machine learning methods were identified to present great prospects for accelerating the screening of descriptors. That new research paradigm improves cognition in the way of oxygen evolution reaction/oxygen reduction reaction activity descriptor and reinforces the understanding of intrinsic physical and chemical features in the electrocatalytic process from a multiscale perspective. This review summarizes those new research paradigms for screening multiscale descriptors, especially from atomic scale to cluster mesoscale and bulk macroscale. The development of descriptors from traditional intermediate to eigen feature parameters has been addressed which provides guidance for the intelligent design of new energy materials.

The electrodeposition of a polymer (polyacrylonitrile, PAN) is used to reduce the risk of thermal runaway in lithium-ion batteries, which is the most important cause of battery accidents and fires. PAN was electrodeposited on a graphite battery electrode, using cyclic voltammetry or chronoamperometry, in a solution with acrylonitrile as the solvent. The electrodeposited PAN film was characterised by Raman spectroscopy, microscopy, energy dispersive X-ray analysis, and thermogravimetric analysis, and it was found that the film thickness could be controlled by the amount of charge passed in the electrochemical experiments. The PAN-coated graphite battery electrode was then tested in lithium half-cells, obtaining capacities close to the uncoated graphite sample (ca. 360 mA h g−1) for thin (20 µm) it was found that the capacity decreased drastically as the temperature increased beyond 80 °C. Such suppression in capacity has applications for thermal runaway protection since the electrochemical reactions of degradation of the electrolyte in contact with the electrode are the root cause of the thermal runaway process. Further work should look into alternative polymer and liquid electrolyte formulations to achieve the desired suppression of electrochemical capacity at high temperatures while retaining high capacities at the operational temperature range.

In the search for alternatives to lithium batteries, aluminium makes a promising negative electrode due to its high theoretical specific energy and energy density. One battery chemistry making use of an aluminium negative electrode is the aluminium–poly(3,4-ethylenedioxythiophene) (PEDOT) battery, which has been shown to have long cycle life and specific energy comparable to other aluminium rechargeable batteries. The battery stores AlCl4− anions in the PEDOT cathode when charged. However, the storage mechanism is not well understood. Here, ab initio molecular dynamics simulations (AIMD) are used to help understand the optimum (relaxed) configuration of AlCl4− anions when stored on a single chain of PEDOT. Two main conclusions arise. Firstly, it is generally not stable to have two anions adsorbed to one monomer unit, and this configuration can be avoided for future work. Secondly, AIMD does not find lower energy configurations for the PEDOT/AlCl4− system than DFT geometry relaxation, providing that the starting geometry does not have two anions on the same monomer unit. Based on our results, we believe it is likely that similar behaviour will be observed in other conducting polymer systems.

In recent years, x-ray tomography has emerged as a powerful analytical tool for the study of lithium ion batteries and the processes occurring within. A region of specific interest is the electrode and, in particular, the heterogeneous and porous structure. The present paper is a review of studies that use x-ray tomography to characterise electrode structure, at both the cell and microstructure scales. At the cell level, x-ray tomography is used to investigate macroscopic design parameters, such as anode and cathode thicknesses, packing density and alignment of assembled cells, as well as to visualise any macroscopic structural defects, such as islanding. At the microstructure level, x-ray tomography allows for quantitative analysis of electrode structures to ascertain parameters such as particle size, tortuosity and volume fraction. The paper also explores different techniques that have been used across the field, from ex-situ, in-situ and operando techniques, to multimodal imaging methods, tomography informed design and results informed imaging.

Mathematical models have been used extensively to simulate physical and electrochemical processes occurring inside lithium-ion batteries. Physical based models, coupled with experimental validation, have revealed greater scientific understanding of the processes inside the battery. A region of specific interest is the porous electrode. However, the heterogeneous geometry of the porous structure presents practical difficulties in developing suitable models. The present paper is a review of the studies on the physical modelling of lithium ion porous electrodes. Here we review common methods to model the (de)intercalation behaviour of porous Li-ion battery electrodes. Advantages and drawbacks are contrasted to highlight some challenges that suggest directions and priorities for further research in the field.

Image-based modelling has emerged as a popular method within the field of lithium-ion battery modelling due to its ability to represent the heterogeneity of the porous electrodes. A common challenge from image-based modelling is the size of 3D tomography datasets, which can be of the order of several billion voxels. Previously, different approximation methods have been used to simplify the computational problem, but each of these come with associated limitations. Here we develop a data-driven, fully parallelisable, image-based modelling framework called OpenImpala. Micro X-ray computed tomography (CT) is used to obtain 3D microstructural data from samples non-destructively. These 3D datasets are then directly used as the computational domain for finite-differences based direct physical modelling (e.g. to solve the diffusion equation directly on the CT obtained datasets). OpenImpala then calculates the equivalent homogenised transport coefficients for the given microstructure. These coefficients are written into parameterised files for direct compatibility with two popular continuum battery models: PyBamm and DandeLiion, facilitating the link between different scales of computational battery modelling. OpenImpala has been shown to scale well with an increasing number of computational cores on distributed memory architectures, making it applicable to large datasets typical of modern tomography.

The prevention of thermal runaway (TR) in lithium‐ion batteries is vital as the technology is pushed to its limit of power and energy delivery in applications such as electric vehicles. TR and the resulting fire and explosion have been responsible for several high‐profile accidents and product recalls over the past decade. Herein, the causes of TR are described and novel preventative methods are examined, approaching the problem from different angles by altering the internal structure of the battery to undergo thermal shutdown or developing the battery and thermal management systems so that they can detect and prevent TR. Ultimately, a variety of different technologies is needed to address the emerging market of highly specialized lithium‐ion batteries. Key innovations discussed include positive temperature coefficient (PTC) materials, self‐healing polymer electrolytes, and hybrid liquid–solid‐state electrolytes. Mist cooling achieves a highly uniform temperature inside the battery pack without the need for pumps to circulate a coolant. The development of battery management systems (BMSs) which model the internal temperature of the cell from real‐time data and prevent the cell reaching a critical temperature is an essential area for further research.

Multistage crystallization of transition metal oxides often proceeds through a number of metastable intermediates. Here, the authors shed light on this phenomenon by extending Pourbaix diagrams to capture the metastable and nanoscale thermodynamics of manganese oxide nucleation.

D-band engineering via alloying platinum is a leading design principle for advanced oxygen reduction electrocatalysts, but stability remains a concern. Here the authors make Pt nanoparticles supported on graphite-rich boron carbide for enhanced activity and stability, isolating and optimizing the electronic metal-support interactions.

Test and measurement methods, particularly electrochemical methods, applicable to fuel cell technology are reviewed. The difficulties of application of diagnostic tools and interpretation of results are highlighted. Specific requirements of the different fuel cell types, both high and low temperature variants, are dealt with. Finally, recently developed in situ imaging methods and their diagnostic significance are presented.

The commercial success of the polymer electrolyte fuel cell (PEFC) will to a large extent be determined by the nature, properties, functionality, and cost of the electrochemical sub-components used in the membrane electrode assembly (MEA). Materials research activities in Switzerland for the PEFC are being pursued at the Paul Scherrer Institut (Villigen AG) and the Swiss Federal Institute of Technology in Lausanne with different objectives. The radiation grafted proton exchange membrane developed at the Paul Scherrer Institut (PSI) has been brought to a near-product-like quality level with encouraging performance close to state-of-the-art materials and a life-time of several thousand hours. Furthermore, the membrane shows low methanol crossover in the direct methanol fuel cell. In addition, polyarylene block copolymer membranes have been investigated as an option for fluorine-free membranes. The electrocatalysis of Pt in acidic solution and in contact with a solid electrolyte, the development of new methanol oxidation and oxygen reduction catalysts, and co-sputtering of Pt and carbon as an alternative method for catalyst preparation are areas of fundamental research. More applied research is performed in the characterization of commercial electrodes in single cells, using standard as well as advanced diagnostic tools developed in-house. This article gives an overview over the research and development projects in Switzerland related to materials and components for the PEFC.

Surface energies of several low-index surfaces of layered LiCoO2are investigated as a function of the external lithium and oxygen chemical potentials by means of First Principles calculations in the generalized gradient approximation (GGA) to density functional theory (DFT), treating on-site electron correlation within the DFT+U framework. We find the set of surfaces contained in the equilibrium shape to be depending on environment. The (0001) and (101̅4) surfaces are present for all reasonable values of the Li and O chemical potentials. The (011̅2) surface, however, is stable only under oxidizing conditions. The equilibrium shape is sensitive to the equilibration environment because the thermodynamically favorable surface terminations and surface energies of the polar (0001) and (011̅2) surfaces are a function of environment. This provides a lever to tailor the crystal shape according to application requirements (e.g., as active material in lithium-ion batteries). [ABSTRACT FROM AUTHOR]