(Digital Presentation) The Electrode’s Inhomogeneous Microstructure Effect on Battery Performance
Bibliographic record
Abstract
Currently, rechargeable batteries are used in everyday life applications, such as in mobiles, cameras, laptops, and cars. Accordingly, the demand for rechargeable batteries, in particular Lithium-ion batteries, is increasing. Their electrodes consist of at least four different materials: active material, a conductive additive, polymer binder, and current collector. The battery performance in terms of cycling performance, energy density, mechanical integrity, and ionic and electronic mobility depends on these materials. 1 The casting procedure, the multiscale packing during the evaporation process after casting, the accumulation of the conductive additive, the adhesive failure during the charging and discharging process, and the natural irregular shape of the electrode materials produce an inhomogeneous electrode microstructure. 2 This inhomogeneity causes current density variation and a non-uniform Lithium ion concentration leading to damage to the battery structure, reducing the capacity, and degrading the battery. 2 The electrode microstructure and its effect on the battery performance have great importance. As such, several techniques have been developed to investigate electrode microstructure. Each technique gives specific information. Here, we will combine scanning electron microscopy (SEM), scanning electrochemical microscopy (SECM), and battery cycling to study the effect of microstructure on the battery performance. SECM is a high resolution probe technique that scans the electrode surface using an ultra-microelectrode. Thus it can measure the electrode`s local conductivity. 3 , 4 Therefore, we can quantify the inhomogeneous microstructure distribution. Thus, by combining these results with SEM and cycling test results we will be able to find the correlation between the battery performance and the distribution of the microstructure. References: Stein M, Chen CF, Robles DJ, Rhodes C, Mukherjee PP. Non-aqueous electrode processing and construction of lithium-ion coin cells. J Vis Exp . 2016;2016(108):1-10. doi:10.3791/53490 Xiong R, Zhang T, Huang T, Li M, Zhang Y, Zhou H. Improvement of electrochemical homogeneity for lithium-ion batteries enabled by a conjoined-electrode structure. Appl Energy . 2020;270(May):115109. doi:10.1016/j.apenergy.2020.115109 Ventosa E, Schuhmann W. Scanning electrochemical microscopy of Li-ion batteries. Phys Chem Chem Phys . 2015;17(43):28441-28450. doi:10.1039/c5cp02268a Polcari D, Dauphin-Ducharme P, Mauzeroll J. Scanning Electrochemical Microscopy: A Comprehensive Review of Experimental Parameters from 1989 to 2015. Chem Rev . 2016;116(22):13234-13278. doi:10.1021/acs.chemrev.6b00067
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".