Electrode structure effects on the performance of open-cathode proton exchange membrane fuel cells: A multiscale modeling approach

In this paper we present a new dynamic multiscale model of an open-cathode Polymer Electrolyte Membrane Fuel Cell (PEMFC). The model describes two-phase water transport, electrochemistry and thermal management within a framework that combines a Computational Fluid Dynamics (CFD) approach with a micro-structurally-resolved model predicting the water filling dynamics of the electrode pores and the impact of these dynamics on the evolution of the electrochemically active surface area (ECSA). The model allows relating for the first time the cathode electrode structure to the cell voltage transient behavior during experimental changes in fuel cell temperature. The effect of evaporation rates, desorption rates and temperature changes on the performance of four different electrode pore size distributions are explored using steady-state and transient numerical simulations. The results are discussed with respect to water management and temperature control.This work is partially funded by the national project MICINNDPI2011-25649, as well as by the 7th Framework Programme of the European Commission in the context of the Fuel Cells and Hydrogen Joint Undertaking (FCH JU) through the project PUMA-MIND FP7 303419.Peer Reviewe

Global and Local Structure of Lithium Battery Electrolytes: Origin and Onset of Highly Concentrated Electrolyte Behavior

Highly concentrated electrolytes (HCEs), created simply by increasing the lithium salt concentration from the conventional 1 M to 3-5 M, have been suggested as a path towards safer and more stable lithium batteries. Their higher thermal and electrochemical stabilities and lower volatilities are usually attributed to the unique solvation structure of HCEs with not enough solvent available to fully solvate the Li+ ions—but much remains to be understood. Here the structural features that characterize the behavior of electrolytes in general and HCEs in particular, and especially the transition from conventional to highly concentrated behavior, are reported for lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) in acetonitrile (ACN), a common HCE system. We analyze four different salt concentrations using ab initio molecular dynamics (AIMD) and the CHAMPION software, to obtain trends in global and local structure, as well as configurational entropy, to elucidate what truly sets apart the highly concentrated regime

CHAMPION: Chalmers hierarchical atomic, molecular, polymeric and ionic analysis toolkit

We present CHAMPION (Chalmers hierarchical atomic, molecular, polymeric, and ionic analysis toolkit): a software developed to automatically detect time-dependent bonds between atoms based on their dynamics, classify the local graph topology around them, and analyze the physicochemical properties of these topologies by statistical physics. In stark contrast to methodologies where bonds are detected based on static conditions such as cut-off distances, CHAMPION considers pairs of atoms to be bound only if they move together and act as a bound pair over time. Furthermore, the time-dependent global bond graph is possible to split into dynamically shifting connected components or subgraphs around a certain chemical motif and thereby allow the physicochemical properties of each such topology to be analyzed by statistical physics. Applicable to condensed matter and liquids in general, and electrolytes in particular, this allows both quantitative and qualitative descriptions of local structure, as well as dynamical processes such as speciation and diffusion. We present here a detailed overview of CHAMPION, including its underlying methodology, implementation, and capabilities

Ion Transport Mechanisms via Time-Dependent Local Structure and Dynamics in Highly Concentrated Electrolytes

Highly concentrated electrolytes (HCEs) are attracting interest as safer and more stable alternatives to current lithium-ion battery electrolytes, but their structure, solvation dynamics and ion transport mechanisms are arguably more complex. We here present a novel general method for analyzing both the structure and the dynamics, and ultimately the ion transport mechanism(s), of electrolytes including HCEs. This is based on automated detection of bonds, both covalent and coordination bonds, including how they dynamically change, in molecular dynamics (MD) simulation trajectories. We thereafter classify distinct local structures by their bond topology and characterize their physicochemical properties by statistical mechanics, giving both a qualitative and quantitative description of the structure, solvation and coordination dynamics, and ion transport mechanism(s). We demonstrate the method by in detail analyzing an ab initio MD simulation trajectory of an HCE consisting of the LiTFSI salt dissolved in acetonitrile at a 1:2 molar ratio. We find this electrolyte to form a flexible percolating network which limits vehicular ion transport but enables the Li+\ua0ions to move between different TFSI coordination sites along with their first solvation shells. In contrast, the TFSI anions are immobilized in the network, but often free to rotate which further facilitates the Li+\ua0hopping mechanism.Highly concentrated electrolytes (HCEs) are attracting interest as safer and more stable alternatives to current lithium-ion battery electrolytes, but their structure, solvation dynamics and ion transport mechanisms are arguably more complex. We here present a novel general method for analyzing both the structure and the dynamics, and ultimately the ion transport mechanism(s), of electrolytes including HCEs. This is based on automated detection of bonds, both covalent and coordination bonds, including how they dynamically change, in molecular dynamics (MD) simulation trajectories. We thereafter classify distinct local structures by their bond topology and characterize their physicochemical properties by statistical mechanics, giving both a qualitative and quantitative description of the structure, solvation and coordination dynamics, and ion transport mechanism(s). We demonstrate the method by in detail analyzing an ab initio MD simulation trajectory of an HCE consisting of the LiTFSI salt dissolved in acetonitrile at a 1:2 molar ratio. We find this electrolyte to form a flexible percolating network which limits vehicular ion transport but enables the Li+\ua0ions to move between different TFSI coordination sites along with their first solvation shells. In contrast, the TFSI anions are immobilized in the network, but often free to rotate which further facilitates the Li+\ua0hopping mechanism.Highly concentrated electrolytes (HCEs) are attracting interest as safer and more stable alternatives to current lithium-ion battery electrolytes, but their structure, solvation dynamics and ion transport mechanisms are arguably more complex. We here present a novel general method for analyzing both the structure and the dynamics, and ultimately the ion transport mechanism(s), of electrolytes including HCEs. This is based on automated detection of bonds, both covalent and coordination bonds, including how they dynamically change, in molecular dynamics (MD) simulation trajectories. We thereafter classify distinct local structures by their bond topology and characterize their physicochemical properties by statistical mechanics, giving both a qualitative and quantitative description of the structure, solvation and coordination dynamics, and ion transport mechanism(s). We demonstrate the method by in detail analyzing an ab initio MD simulation trajectory of an HCE consisting of the LiTFSI salt dissolved in acetonitrile at a 1:2 molar ratio. We find this electrolyte to form a flexible percolating network which limits vehicular ion transport but enables the Li+\ua0ions to move between different TFSI coordination sites along with their first solvation shells. In contrast, the TFSI anions are immobilized in the network, but often free to rotate which further facilitates the Li+\ua0hopping mechanism

Digitalization of Battery Manufacturing: Current Status, Challenges, and Opportunities

As the world races to respond to the diverse and expanding demands for electrochemical energy storage solutions, lithium-ion batteries (LIBs) remain the most advanced technology in the battery ecosystem. Even as unprecedented demand for state-of-the-art batteries drives gigascale production around the world, there are increasing calls for next-generation batteries that are safer, more affordable, and energy-dense. These trends motivate the intense pursuit of battery manufacturing processes that are cost effective, scalable, and sustainable. The digital transformation of battery manufacturing plants can help meet these needs. This review provides a detailed discussion of the current and near-term developments for the digitalization of the battery cell manufacturing chain and presents future perspectives in this field. Current modelling approaches are reviewed, and a discussion is presented on how these elements can be combined with data acquisition instruments and communication protocols in a framework for building a digital twin of the battery manufacturing chain. The challenges and emerging techniques provided here is expected to give scientists and engineers from both industry and academia a guide toward more intelligent and interconnected battery manufacturing processes in the future.publishedVersio

Field dependence of the magnetocaloric effect in Gd and (Er 1-xDyx)Al2: Does a universal curve exist?

The field dependence of the magnetic entropy change of ferromagnetic lanthanide- based materials has been studied. The recently proposed master curve for the field dependence of the magnetocaloric effect of Fe-based amorphous alloys can also be constructed for these lanthanide-based crystalline materials, suggesting a universal behavior. The exponent n controlling the field dependence of the magnetic entropy change can be used for the interpretation of results in the case of multiple magnetic ordering phenomena

Optimization of the refrigerant capacity in multiphase magnetocaloric materials

The refrigerant capacity (RC) of magnetocaloric materials can be enhanced using multiphase materials or composites, which expand the temperature range over which a significant magnetic entropy change can be obtained. Numerical simulations show that by controlling the parameters of the composite (the fraction of the different phases and their Curie temperatures) improvements of RC of ∼83% are possible. The maximum applied field plays a crucial, nonmonotonic, role in the optimization. As a proof of concept, it is shown that the combination of two Fe88−2xCoxNixZr7B4Cu1 alloys produces an enhancement in RC of ∼37%, making it ∼92% larger than that of Gd5Si2Ge1.9Fe0.

Influence of Co and Ni addition on the magnetocaloric effect in Fe88−2xCoxNixZr7B4Cu1 soft magnetic amorphous alloys

We have studied the magnetocaloric effect in a series of Fe88−2xCoxNixZr7B4Cu1Fe88−2xCoxNixZr7B4Cu1alloys. The partial substitution of Fe by Co and Ni leads to a monotonic increase in the Curie temperature(TC)(TC) of the alloys from 287 K for x=0x=0 to 626 K for x=11x=11. The maximum magnetic entropy change (ΔSpkM)(ΔSMpk) at an applied field of 1.5 T, shows a value of 1.98 J K−1 kg−11.98 J K−1 kg−1 for x=8.25x=8.25. The refrigerant capacity (RC) has maximum values near 166 J kg−1166 J kg−1 (for x=0x=0 and 2.75). These values place the present series of alloys among the best magnetic refrigerant materials, with an RC ∼40%∼40% larger than Gd5Si2Ge1.9Fe0.1Gd5Si2Ge1.9Fe0.1 and ∼15%∼15% larger than Fe-based amorphousalloys

Magnetocaloric effect and critical exponents of Fe77Co 5.5Ni5.5Zr7B4Cu1: A detailed study

The critical exponents of the alloy have been determined with the Kouvel–Fisher method to predict the field dependence of the magnetic entropy change DSM . The nonlinear fit of DSM ðHÞ to a power law provides a field exponent in perfect agreement with the predictions of the relevant scaling laws using the obtained critical exponent values. It is shown that possible discrepancies between these two methods for determining the field dependence of DSM might arise due to a poor resolution in the temperature of the experiments