Peer review of: Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion batt (2020)

Reviewers' comments: 

Reviewer #1 (Remarks to the Author): 

This manuscript reports a mechanism study of particles’ evolving (de)attachment with the conductive 
matrix in Li-ion battery cathodes based on both multiscale experimental approaches and machine-
learning-assisted statistical analysis. The approach and the characterization are very nice and novel. 
As a result, the manuscript can be recommended for publication after the following suggestions are 
taken into account. 

Foremost, why traditional methods fail to distinguish different particles and the machine learning 
approach demonstrates significantly improved performance? It would be better if the author can give 
more details about the difference between the ML approach and the traditional methods. 

Second, the author talks about a very novel and important topic in this study, in contrast to the 
tremendous efforts in researching the active materials. So it would be better if the author can give 
some implications for follow-up experimental and computational study. 

Third, in introduction section, the abbreviation, CBD, PDF-CT et al. should be explained. 



Reviewer #2 (Remarks to the Author): 

Jiang et al. reported a study of NMC-811 electrode system using hard x-ray nano-tomography based 
on the phase contrast modality. The resolution of their method is high, allowing them to identify CBD, 
NMC and void space. The authors connected the level of detachment of the NMC particle to relative 
electrical resistance using a numerical model. They then developed a machine learning method to 
automatically segment 650 electrode and statistically quantify how the level of detachment varies as a 
function C-rate and particle size. The authors also discussed a correlation between the local electron 
density and SoC. 

High resolution characterisation of electrode structure subjected to different cycling conditions 
contributes towards understanding degradation, which is a very timely area of research. The authors' 
approach of high throughput segmentation and arriving at statistically meaningful conclusions is well-
taken. I think this manuscript is potentially publishable in Nature Communication, after substantial 
revision. 

My main concern is how the authors analysed their machine learning method. "Machine learning" are 
literally the first 2 words in the title, yet their description and discussion of the actual method is 
lacking. The authors mentioned "more details about the training procedure, as well as performance 
evaluation, will be described in a follow-up manuscript focusing on the computational aspect of this 
work", but this is inadequate. 

Specific questions: 

1. The authors should clearly state and discuss the size of the training set. 

650 samples is a small dataset size for image recognition models. If the training set size is larger than 
650, and the training set is hand annotated, then machine learning confers no time/performance 
advantage over hand annotation. In that case, the machine learning part in the manuscript is 
irrelevant. 

2. How did the authors converge to this specific model? Which model architectures were considered? 
What is the model accuracy on a held-out test set? Just showing handpicked results, e.g. Fig 3, is not 
informative. 

3. What is "traditional segmentation" (c.f. Fig 3), and how does that compare with the authors' ML 
model? 

4. How sensitive is the result presented in Fig 4 to the choice of ML algorithm to segment the images? 

5. Data availability "upon reasonable request" is rather disappointing. Machine learning models tend to 
be quite intricate and essentially not reproducible unless the code and data are published in full. The 
authors should consider putting their code on GitHub and publish the data in a publicly accessible 
repository. 



Reviewer #3 (Remarks to the Author): 

The paper describes efforts to better understand degradation phenomena in LIB cathodes that are of 
nickel rich composition such as NMC811. In particular, it describes a method how data from X-ray 
phase contrast tomography and X-ray spectro-microscopy can subjected to a statical analysis based 
on machine learning. The experiments described are based on two battery cells and 650 particles 
analysed. Partial detachment is found of active particles from the particle ensemble embedded in 
carbon and binder. This reduces the electronic conductivity since only point contacts are left while the 
gaps created becomes filled with electrolyte solution which creates a larger solid-liquid interface. 
The approach is of using X-ray tomography and X-ray spectro-microscopy together with machine 
learning to more precisely conduct the statistical analysis. While this combination of techniques 
appears very powerful the extraction of understanding of the cathode processes stays limited. The 
authors don't discuss the implications of the described processes or observations, e.g. 
(i) In the inital phase of particles becoming detached the battery processes should be enhanced since 
the particle-electrolyte interface becomes larger. Since the charge carrier concentration is much higher 
in the solid state compared to the liquid an overall increased activity should be observed. Only in a 
later stage the contribution of the detaching particle should become deminished. 
(ii) While the electron density changes with the SoC the authors rightly correlate it with the valence 
state of Ni. Here it would be useful to give an estimate of the valence change in Fig.5d and/or Fig.5e. 
In addition, it should be discussed why Mn and Co are not considered to undergo valency changes. 
(iii) As stated, 10 charge-discharge cycles were performed at different C-rates. This may considered to 
be just in the break-in phase, longer cycling would be preferable. 
(iii) All experiments are done ex-situ after disassembling the battery. In addition to saying that in-
operando experiments are desirable, it should clearly be discussed where the limitation are with the 
current procedure and why in-operando experiments would be more revealing. 
The paper is initial work illustrating a novel methodological approach but little discussion of what we 
learn from it regarding the battery processes. The paper is interesting from an experimental point of 
view, the scientific content is rather shallow. The paper should not be published in its current form. 
A minor point: Why is Fig.1 in gray scale in contrast to all other figures, it is hard to make out the 
differences.