S. R.
Daemi
a,
X.
Lu
a,
D.
Sykes
b,
J.
Behnsen
b,
C.
Tan
a,
A.
Palacios-Padros
c,
J.
Cookson
c,
E.
Petrucco
c,
P. J.
Withers
b,
D. J. L.
Brett
a and
P. R.
Shearing
*a
aElectrochemical Innovation Lab, Department of Chemical Engineering, UCL, London, WC1E 7JE, UK. E-mail: p.shearing@ucl.ac.uk
bHenry Moseley X-ray Imaging Facility, Photon Science Institute, The University of Manchester, Manchester, M13 9PY, UK
cJohnson Matthey, Technology Centre, Blounts Court Road, Sonning Common, Reading, RG4 9NH, UK
First published on 18th December 2018
Lithium-ion (Li-ion) batteries operate via electrochemical reactions between positive and negative electrodes, formed by complex porous microstructures. An improved understanding of these materials can lead to a greater insight into the link between microscopic electrode morphology and macroscopic performance. The practice of calendering electrodes after manufacturing has been widely used to increase the volumetric energy density and improve the electrical contact between electrode material particles and with the foil substrate. In this paper we present, for the first time to the authors’ knowledge, a technique to image battery electrodes in situ and in 3D whilst undergoing uniaxial compression with the intent of emulating the calendering process. This technique allows the tracking of electrode strain during compression and its further application will lead to a thorough understanding of crack initiation and propagation mechanisms within electrode particles, ultimately optimising their design and performance.
Conceptual insightsLi-ion battery electrodes are ‘calendered’, a process whereby these are pressed to improve packing density and enhance electrical conductivity. Whilst this process is universally adopted, there remains a lack of fundamental understanding of the influence of calendering on the resulting microstructure. This study presents a novel technique that allows the imaging of Li-ion battery electrodes during uniaxial compression with X-ray nano-computed tomography to emulate calendering. By combining novel sample preparation techniques, a nano-mechanical stage and digital volume correlation, we were able to map the strain evolution within an electrode during compression. Our study differs from previous literature as the imaging is done in situ: this is the only study to our knowledge which looks at nano-scale microstructure evolution in response to an applied mechanical load. We uniquely observe how different areas of the same electrode react to a compressive force offering unparalleled insight into the mechanical behaviour of electrodes and their constituent particles. Our work is not only a significant advance in battery characterisation, but offers a technique that could be applicable to a wide range of functional porous materials, leading to a further understanding of their mechanical properties and optimisation of their design. |
A range of ex situ studies have investigated the effect that compression has on electrodes and successfully established a link between calendering and its effect on the electrochemical performance of the electrode.7,9–11 A recent study for example, has examined slices of LiNi0.33Mn0.33Co0.33O2 (NMC) electrodes compressed to different porosities with a scanning electron microscope (SEM), linking the change in the porous network to the electrochemical performance.12 One of the main limitations of this approach is that a 2D characterisation technique is applied to a complex 3D microstructure. Consequently there is a lack of understanding of how a compressive force is distributed within these porous networks and how this causes particle displacement. Furthermore, the role of particle cracking and fragmentation and its effect in long term capacity degradation has not been explored thoroughly in previous literature.
To address these shortcomings, we have devised a technique to mimic calendering in situ whilst imaging the electrode on the nano-scale by using a combination of X-ray nano-tomography, a novel sample preparation technique and an in situ nano-mechanical stage. In this work, we have been able to directly track secondary particle movement and visualise the development of strain hotspots throughout an electrode during uniaxial compression. We have also applied physical characterisation and computational tools to further the understanding of the material and quantify microstructural change within the electrode.
Advances in lab-based X-ray computed tomography (CT) instruments have paved the way for sub-micron resolution imaging of energy materials obtained via a transmission X-ray microscopy (TXM) architecture.4,13–15 In conventional lab-based micro-CT instruments, the optical magnification requires the sample to be as close as possible to the source and detector for the highest resolution. The TXM architecture leads to a sufficient increase in the distances between the sample, source and detector to allow for certain in situ rigs to be installed.16 Specifically, a nano-mechanical in situ loading stage has been developed and used to gauge the effect of compression on materials in the nano-scale.17 To fully utilise the sub-micron resolution offered by the nano-CT instrument and reduce artefacts during imaging, a novel sample preparation technique has been developed that consists in laser-milling electrode pillars below 100 μm.18
After mounting and manually aligning the sample holder to the compression flat head tip, indicated in Fig. 1b, the assembly was inserted in the chamber of the X-ray nano-CT instrument and allowed to thermally equilibrate. Compression was then continuously applied by controlling the sample displacement in the z-axis until the load stage reached the maximum displacement of 500 μm with two pause steps for imaging. A piezo actuator controls the sample displacement in the z-axis, pressing the sample upon the compression flat head. These two compression steps are named step A and B respectively. The full force-stage displacement graph can be found in the ESI.†
Fig. 2a–c present sequential radiographs of the sample taken in its uncompressed and post-compression states, whereas Fig. 2d–f present a virtual slice taken from the fully registered and aligned 3D tomographic dataset. The collected radiographs are reconstructed into a 3D volume for analysis by using a filtered back projection algorithm.19
Fig. 3a–c represent the volume renderings of each tomographic dataset overlaid by the respective strain map, along with the vector slice in the z direction. The compression flat head is also added for ease in visualisation. Fig. 3e and f represent the CC spatially mapped onto the entire dataset. A direct comparison between the particles presented in Fig. 3a–c and the corresponding spatially mapped CC presented in Fig. 3e and f can be achieved. The opacity of the subvolume cubes is gradually decreased as the CC approaches zero to aid in the visualisation on the sides of the dataset.
The first step in this analysis consists in decoupling the areas of poor correlation: namely those located on the sides or top of the full dataset volume, and those with a higher displacement and smaller particle size. From the CC map, it is possible to observe that the majority of areas of low correlation belong to the side and top edges of the dataset, where the pixel values are attributed to noise due to the presence of the compression flat head, or to edge pixels having null values. These can be viewed by the “empty” cubes in Fig. 3d and e. Typically, the strain calculated for these areas is either positive indicating tension or an outlying value of a negative compressive strain. As values below −0.10 are identified as belonging to the latter, the strain is capped at −0.10 for ease in visualisation.
From step A, it is possible to observe that the highest valid displacement occurs in a hotspot in the top section of the electrode, below the compression flat head. By comparing the strain and CC maps, a zone with low correlation can be identified within an area of high strain, highlighted by the recurring black outline in Fig. 3. This is thought to be due to the presence of a high displacement of small particle fragments with size below the correlation window. This may indicate that smaller particles, along with fragments that may be formed by particles crushing during calendering may act as strain hot-spots. Examining the compression of the upper layer of the electrode may also lead to a further understanding of how the uppermost porosity varies and how this affects the interpenetration of electrolyte within the electrode. The remaining regions of the electrode have a higher CC indicating the reliability of the result. This is confirmed by comparing the CC and the grayscale data: as the displacement increases, the correlation decreases in areas where particles are small but a strong correlation persists in the rest of the dataset.
With step B, the strain can be seen as concentrating throughout the top part of the electrode whilst spreading towards the lower half. The vector field also highlights that the particles are moving laterally within the electrode. This behaviour could be attributed to the different stress–strain propagation mechanisms within the electrode particles according to their morphology, local heterogeneity or dimensions. Lateral movement could also be emphasized by the fact that the edge of the sample is in close proximity the bulk. The above considerations indicate that by analysing all the outputs of the DVC calculation, it is possible to obtain an excellent representation of particle movement and strain evolution within an electrode.
Fig. 4a and b present a binarised slice of the electrode and the corresponding CPSD can be viewed in Fig. 4c. The average radius size decreases from ca. 2 μm to 1.7 μm and 1.5 μm for step A and step B respectively. While the overall pore volume can be seen as decreasing between the two compression steps, most of this decrease occurs with the first compression step: this indicates that as the particles are reducing their separation, a major resistance to compression is taking place and in the context of increased loads, could indicate the onset of particle cracking. The inactive phase fraction was also calculated as 68% for the uncompressed sample, 58% for compression step A and 55% for compression step B. These results highlight the potential in the technique to shed light on the effect of compression on the microstructural evolution of the electrode particles and porous phases.
Fig. 4 (a) Sample grayscale slice taken from reconstructed dataset and (b) its binarised equivalent. (c) Cumulative CPSD of porous networks and overall pore size and volume. |
The potential implications of further application of this technique range from understanding the mechanical behaviour of battery electrodes to generating compressed electrode datasets that can be used for further modelling and the validation of novel electrochemical and microstructural models. An example application could be the study of species transport within an electrode, where diffusion within all phases could be considered to understand how inhomogeneous zones generated by compression can affect Li+ concentration gradients, electrical conduction, and the related local current density.28
In conclusion, we have used a nano-mechanical stage to compress micro-machined NMC pillars of ca. 80 μm diameter whilst performing nano-scale 3D imaging. A first analysis of two compression steps have been conducted to track the early-stage displacement, which has demonstrated that the combination of this setup along with DVC is a promising technique to understand the mechanisms of the Li-ion battery electrode microstructural evolution under compression. We have been able to accurately track the movement of secondary particles through the electrode as a function of applied load, as well as apply computational characterisation techniques to quantify how the inter-particle separation is decreasing.
This could potentially provide new insights into the industrial calendering process. Further work will be carried out to understand the particle cracking mechanisms using higher compressive loads. We believe that developing this technique can benefit the study of battery electrodes by bridging the knowledge between the microstructural evolution within electrode particles and the resulting effect on battery performance and capacity degradation.
Footnote |
† Electronic supplementary information (ESI) available. See DOI: 10.1039/c8mh01533c |
This journal is © The Royal Society of Chemistry 2019 |