Bibliographic record
Abstract
Lithium intercalation electrodes are composite devices that consist of typically at least four different constituents that provide separately charge storage, electronic conduction, ionic conduction and mechanical integrity. Consequently, the microstructure of these composites has an important role to play in intercalation electrode performance. An inhomogeneous electrode microstructure affects battery performance by increasing peak local current densities and resistances during the operation. This reduces accessible capacity, compromises safety, as it facilitates localized overcharging, and impacts ion transport and consequently power performance.1 Origins for inhomogeneity in electrode microstructure can lie in the casting procedure, slow carbon agglomeration over cycling as well as in binder adhesive failure due to active material volume changes, which causes a connectivity loss between active material and the matrix. Given this impact of the electrode microstructure, an improved understanding of microstructure and microscopic performance variations is of great importance to optimizing overall battery performance. As such, we need tools that allow us to measure microscopic performance, and correlate to microstructure, interfaces and their development over cycling. Thus a variety of spatial imaging techniques can be used to characterize the processes ex situ, in situ, and operando. Each technique gives a unique and specific information about certain process.2 , 1 We are highlighting herein X-ray tomography and scanning electrochemical microscopy (SECM) as tools for the investigation of disconnection mechanisms. X-ray Tomography (XRT) is a 3D imaging technique used to visualize complex mechanical interactions in batteries during operation. This nondestructive technique can provide a spatial resolution down to the nanometer scale. X-ray tomography can provide information of the material porosity and tortuosity, chemical composition and crystals and the reactions inside the battery.4 , 2 We are using this technique in the imaging, analysis and tracking of electrode microstructure with cycling. Scanning electrochemical microscopy (SECM) is a scanning probe technique that uses a microscopic electrochemical electrode as probe. This allows the measurement of local electrochemical activity on a substrate surface. It has previously been used to investigate the properties of solid electrolyte interphase (SEI) of the negative electrode as well as exploring the lithium ion activity at micro and nano scales resolution.3 We are employing this method to correlate microstructure and performance inhomogeneity. This presentation summarizes first results from both techniques and contrasts structural with performance inhomogeneity. We will be using these tools as we are developing new binders and microstructures for intercalation electrodes to improve the synergy between electrode constituents and optimize overall electrode performance. References (1) Müller, S.; Eller, J.; Ebner, M.; Burns, C.; Dahn, J.; Wood, V. Quantifying Inhomogeneity of Lithium Ion Battery Electrodes and Its Influence on Electrochemical Performance. J. Electrochem. Soc. 2018, 165 (2), A339–A344. https://doi.org/10.1149/2.0311802jes. (2) Schröder, D.; Bender, C. L.; Arlt, T.; Osenberg, M.; Hilger, A.; Risse, S.; Ballauff, M.; Manke, I.; Janek, J. In Operando X-Ray Tomography for next-Generation Batteries: A Systematic Approach to Monitor Reaction Product Distribution and Transport Processes. J. Phys. D. Appl. Phys. 2016, 49 (40), 404001. https://doi.org/10.1088/0022-3727/49/40/404001. (3) Ventosa, E.; Schuhmann, W. Scanning Electrochemical Microscopy of Li-Ion Batteries. Phys. Chem. Chem. Phys. 2015, 17 (43), 28441–28450. https://doi.org/10.1039/c5cp02268a. (4) Pietsch, P.; Wood, V. X-Ray Tomography for Lithium Ion Battery Research: A Practical Guide. Annu. Rev. Mater. Res. 2017, 47 (1), 451–479. https://doi.org/10.1146/annurev-matsci-070616-123957.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".