A Maturation Process or Binders Based on Coordination Chemistry to Boost the Mechanical Strength and Cyclability of Si-Based Electrodes
Bibliographic record
Abstract
Silicon is attractive as negative electrode material for Li-ion batteries (LIBs) to increase their energy density [1]. The main challenge is to deal with the large silicon volume expansion induced by its lithiation, which damages the mechanical integrity of the electrode, and produces an unstable solid electrolyte interphase (SEI). Recently, we have discovered a post-processing treatment, called maturation, which very significantly improves the mechanical and electrochemical stabilities of silicon-based electrodes made with polycarboxylic acid binders [2-4]. It consists of storing the electrode in a humid atmosphere for a few days before drying and cell assembly. We found that during maturation, the atmospheric-induced corrosion of the current collector releases oxidized copper species that migrate into the electrode to physically crosslink the binder phase, substantially modifying its resiliency to the silicon volume variation as shown by various in situ and operando characterizations. The pre-addition of a small amount of copper or zinc salt in the electrode slurry allows to reach similar improvement of electrochemical performance than maturation [5]. This reveals that there is great potential to explore coordination chemistry to design new, more efficient binders through the medium strength and dynamic nature of coordination bonds [6]. Acknowledgments The authors thank the Natural Sciences and Engineering Research Council of Canada (NSERC) (grant RGPIN-2016-04524) and Transition Énergétique Québec (TEQ) (grant Techno-0040-0001) for financial support of this work. References [1] M.N. Obrovac, Si-alloy negative electrodes for Li-ion batteries. Current Opinion in Electrochemistr, 2018, 9, 8–17. [2] Z. Karkar et al., How silicon electrodes can be calendered without altering their mechanical strength and cycle life J. Power Sources, 2017, 371, 136-147. [3] C. Real Hernandez et al., A Facile and Very Effective Method to Enhance the Mechanical Strength and the Cyclability of Si-Based Electrodes for Li-Ion Batteries, Adv. Energy Mater, 2017, 1701787. [4] V. Vanpenne et al., Adv. Energy Mater, Dynamics of the morphological degradation of Si-based anodes for Li-ion batteries characterized by in-situ synchrotron X-ray tomography, Adv. Energy Mater. 9, 2019, 1803947 [5] D. Mazouzi et al., CMC-citric acid Cu(II) cross-linked binder approach to improve the electrochemical performance of Si-based electrodes Electrochimica Acta, 2019, 304, 495-504. [6] T. Devic, B. Lestriez, L. Roué, Silicon Electrodes for Li-Ion Batteries. Addressing the Challenges through Coordination Chemistry, ACS Energy Letters, ACS Energy Lett., 2019, 4, 550−557.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".