Localized Tortuosity Determination of Li-Ion Battery Electrode Using SECM and SICM
Bibliographic record
Abstract
Li-ion batteries’ limited power represents a barrier to their application in the domain of renewable energy. To examine the origin of this limitation, the effect of microstructural parameters such as porosity and tortuosity on ionic transport, especially through the electrolyte within the electrode pores needs to be studied. In this work, we tried to determine the local tortuosity in Li4Ti5O12 (LTO) electrodes with various porosities using two scanning probe microscopic techniques: Scanning Ion Conductance Microscopy (SICM) and Scanning Electrochemical Microscopy (SECM). SICM measures the effective ionic conductivity of Li+ ions whereas SECM probes the diffusion of electroactive species like anthracene.1,2 We observed that in both cases, the normalized current increases with porosity as more ions reach the tip through the pores of the electrode. The MacMullin number calculated from SICM and SECM measurements for the LTO electrodes having porosities from 50% to 63% shows that these techniques are complementary. Improved transport through these porous electrodes is indicated by the lower tortuosity values obtained compared to those anticipated by the mass transport models like the Bruggeman relationship. Hence, the tortuosity values determined from these microscopic techniques can be used as a strong tool to improve the modeling of electrolyte transport within electrodes. In turn, this should be highly valuable in the design of batteries with high power density. References: N. A. Payne, J. I. G. Dawkins, S. B. Schougaard and J. Mauzeroll, Anal. Chem., 2019, 91, 15718–15725. Md. S. Hossain, L. I. Stephens, M. Hatami, M. Ghavidel, D. Chhin, J. I. G. Dawkins, L. Savignac, J. Mauzeroll and S. B. Schougaard, ACS Appl. Energy Mater., 2020, 3, 440–446.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".