Study of Li Metal/C Paper Electrodes for Li/S Batteries By Operando Dilatometry
Bibliographic record
Abstract
The use of Li metal anode in rechargeable batteries such as Li-S and Li-air is challenging due to the uncontrolled formation of lithium dendrites during repeated charge/discharge cycling, which induces various issues such as cell short circuit, aggravated adverse reactions, dead Li formation, polarization increase and large volume changes, resulting in safety concerns and low coulombic efficiencies [1]. Different characterization techniques have been used to study Li metal electrode morphological changes associated with Li dendrite formation such as scanning electron microscopy, transmission electron microscopy, atomic force microscopy and optical microscopy [2]. A large part of these characterizations were performed ex situ or in situ but under static conditions (no cycling) or operando using open cell requiring none volatile electrolyte, giving limited information on the Li growth dynamics in real systems. Recently, operando synchrotron X-ray tomography has been used to visualize the morphological evolution of Li metal electrodes in various cell configurations (e.g. Li-S cell [3]). However, the limited access to beamtime at synchrotron X-ray sources and challenges with data processing (image segmentation, volume reconstruction, artefact minimization, etc.) are major drawbacks. In the present study, a simple and low-cost method is used to monitor the electrode thickness variation associated with the Li plating/stripping process. This technique, named ‘’electrochemical dilatometry’’, consists in integrating a gap sensor or a displacement transducer within the electrochemical cell for measuring the vertical displacement of the working electrode during its cycling. It has been successfully applied for studying the volume expansion/contraction of Si-based electrodes for Li-ion batteries [4]. To the best of our knowledge, electrochemical dilatometry has never been applied to the study of Li metal electrodes. In the present study, this technique is used for studying the expansion/contraction behavior of electrodes integrating commercial porous C papers as 3D matrices for Li electrodeposition and compared to what is observed on a 2D Cu foil substrate (Figure 1). References [1] X.B. Cheng, R. Zhang, C.Z. Zhao, Q. Zhang, Toward safe lithium metal anode in rechargeable batteries: a review, Chem. Rev 117 (2017) 10403-10473. [2] D. Lin, Y. Liu, Y. Cui, Reviving the lithium metal anode for high-energy batteries, Nat. Nanotechnol. 12 (2017) 194-206. [3] G. Torin, G. Vaughan, R. Boucher, F. Alloin, M. Di Michiel, L. Boutafa, J.F. Colin, C. Barchasz, Multiscale characterization of lithium/sulfur battery coupling operando X-ray tomography and spatially-resolved diffraction, Sci. Rep. 7 (2017) 2755. [4] A. Tranchot, P-X. Thivel, H. Idrissi, L. Roué, Impact of the slurry pH on the expansion/contraction behavior of silicon/carbon/carboxymethylcellulose electrodes for Li-ion batteries. J. Electrochem. Soc. 163 (2016). Figure 1
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".