Detecting lithium plating dynamics in a solid-state battery with operando X-ray computed tomography using machine learning

Operando X-ray micro-computed tomography(µCT)provides an opportunity to observe the evolution of Li structures inside pouch cells.Segmentation is an essential step to quantitatively analyzingµCT datasets but is challenging to achieve on operando Li-metal battery datasets due to the low X-ray attenuation of the Li metal and the sheer size of the datasets.Herein,we report a computational approach,batteryNET,to train an Iterative Residual U-Net-based network to detect Li structures.The resulting semantic segmentation shows singular Li-related component changes,addressing diverse morphologies in the dataset.In addition,visualizations of the dead Li are provided,including calculations about the volume and effective thickness of electrodes,deposited Li,and redeposited Li.We also report discoveries about the spatial relationships between these components.The approach focuses on a method for analyzing battery performance,which brings insight that significantly benefits future Li-metal battery design and a semantic segmentation transferrable to other datasets.

Electrochemical top-down synthesis of C-supported Pt nanoparticles with controllable shape and size:Mechanistic insights and application

In this work,we demonstrate the power of a simple top-down electrochemical erosion approach to obtain Pt nanoparticle with controlled shapes and sizes(in the range from-2 to-10 nm).Carbon supported nanoparticles with narrow size distributions have been synthesized by applying an alternating voltage to macroscopic bulk platinum structures,such as disks or wires.Without using any surfactants,the size and shape of the particles can be changed by adjusting simple parameters such as the applied potential,frequency and electrolyte composition.For instance,application of a sinusoidal AC voltage with lower frequencies results in cubic nanoparticles;whereas higher frequencies lead to predominantly spherical nanoparticles.On the other hand,the amplitude of the,sinusoidal signal was found to affect the particle size;the lower the amplitude of the applied AC signal,the smaller the resulting particle size.Pt/C catalysts prepared by this approach showed 0.76 A/mg mass activity towards the oxygen reduction reaction which is-2 times higher than the state-of-the-art commercial Pt/C catalyst(0.42 A/mg)from Tanaka.In addition to this,we discussed the mechanistic insights about the nanoparticle formation pathways.

Gas Diffusion Layers:Experimental and Modeling Approach for Morphological and Transport Properties

CONSPECTUS:Electrochemical technologies are key to decarbonizing the energy sector.Electrification of the energy sector is underway with battery technologies dominating the lightduty electric vehicles market.It is more challenging to decarbonize historically difficult to decarbonize sectors,such as heavy-duty transportation,planes,ships,and the chemical manufacturing industry(ammonia,cement,steel).Green hydrogen produced via electrolysis will be used as a fuel and a feedstock in some of these processes.At the heart of the hydrogen economy are polymer electrolyte fuel cells(PEFCs),devices that convert hydrogen into electricity.Gas diffusion layers(GDLs)have an integral role in PEFCs,as they are porous carbon layers that transport reactants and products and also remove heat and conduct electricity.To improve the PEFCs’performance and reduce degradation of materials,an understanding of coupled morphological properties and transport phenomena in the GDLs is needed.In this Account,we emphasize the integration of experimental and modeling approaches to achieve complete understanding of materials and transport properties of the GDLs.Our approach builds in complexity from simpler ex situ experiments to in situ and last to 3-D integrated modeling predictions.GDL morphology is complex,as its fabrication includes several stochastic steps(immersion of GDL in various baths to achieve the desired surface wettability)and only 3-D techniques,such as X-ray computed tomography can capture morphology correctly.Porosity,pore-size distribution,tortuosity,and formation factor are the most important morphological properties of the GDLs.For PEFC applications,water is generated in the catalyst layers and is transported through the GDLs.Therefore,GDL wettability directly impacts water permeability through the GDLs.Using in situ water injection experiments,we directly observe which pores water fill at what liquid pressure.This result provides information about the GDL’s affinity to intake water.GDLs are typically of mixed wettabilities,and internal wettability until recently has been unknown.Having images of water inside the GDL enabled us to track the triple-phase boundary at the fiber−water−air interface to obtain local contact angles in the locations where water was present.The percentage of contact angles that were hydrophilic correlated well to the percentage of surface oxides on the GDL surface using X-ray photoelectron spectroscopy(XPS).We envision many other groups using the method of XPS to determine internal surface wettability of the GDLs,as it is relatively fast.Heat transport and evaporation/condensation of water in the GDL is studied using in situ X-ray CT experiments.These provide direct insight into pore-scale water transport under thermal gradients.Three-dimensional geometries of GDLs are exported for transport simulations using the lattice Boltzmann method(LBM).Similarly,we advocate for building the LBM simulations,from water injection studies first to validate the model only to operando PEFC models later.LBM coupling with a continuum model enables a computational saving,allowing us to map local temperature,reactant,and product distributions in the GDLs.