The Significant Influence of the Conductive Carbon Additive on the Performance of High Areal Capacity Silicon Electrodes
Bibliographic record
Abstract
The constant demand for lithium-ion batteries with higher energy density requires finding new electrode materials. Silicon-based electrodes are particularly attractive due to the higher gravimetric capacity of Si (3579 mAh g-1) compared to conventionally used graphite (372 mAh g-1). However, during the process of lithiation/delithiation, the silicon material suffers from a huge volume change, leading to the fracturing of the silicon particles, an unstable solid electrolyte interphase layer (SEI) and the disconnection of inter-particle contacts, which all have a negative repercussion on the electrode cycle life. Another serious challenge for commercializing silicon electrodes is to reach a high areal capacity of more than 6 mAh cm-2, in order to achieve an energy density improvement over the use of conventional graphite-based anodes. Silicon electrodes with such high areal capacity require very careful design of their formulation at different scales. In particular, a special attention must be paid to creating durable intimate contacts between the active material particles and the conductive additive network, so that sufficient electron transfer could be achieved throughout the electrode from the copper current collector while good mechanical stability of the electrode coating is still maintained.1 Our group has recently shown that high performance silicon-based anodes can be achieved by combining (i) the use of high-energy ball-milling as a cheap and easy process to produce nanostructured silicon powder, (ii) the processing of the electrode with carboxymethylcellulose (CMC) binder at pH 3 condition, which promotes the covalent grafting of the CMC to the Si particles; (iii) the use of fluoroethylene and vinylene carbonates (FEC/VC) electrolyte additives resulting in a more stable SEI.2 In the present work, silicon-based electrodes of various areal capacities were prepared by using either carbon black, vapor grown carbon nanofibers, or graphite nanoplatelets as conductive additive.3 It was observed that the electrical conductivity, capacity retention, and coulombic efficiency of the silicon electrode are significantly affected by the morphological characteristics of the used carbon additives. Spherical-shaped carbon black particles have a strong tendency to agglomerate, and thus fail in creating a conductive network resilient to the silicon particle volume change. In contrast, vapor grown carbon nanofibers maintain more durable contacts with silicon particles, compared to carbon black, by forming a more resilient conductive network due to their wire-like structure.4 Graphite nanoplatelets also create a continuous conductive network, which limits the mechanical degradation of the electrode coating, likely by playing the role of electrically conducting lubricant.5These results demonstrate that the choice of the conductive additive is of crucial importance for the optimization of silicon negative electrodes with commercially relevant areal capacities. References (1) Mazouzi, D.; Karkar, Z.; Reale Hernandez, C.; Jimenez Manero, P.; Guyomard, D.; Roué, L.; Lestriez, B. J. Power Sources 2015, 280, 533–549. (2) Gauthier, M.; Mazouzi, D.; Reyter, D.; Lestriez, B.; Moreau, P.; Guyomard, D.; Roué, L. Energy Environ. Sci. 2013, 6(7), 2145. (3) Karkar, Z.; Mazouzi, D.; Reale Hernandez, C.; Guyomard, D.; Roué, L.; Lestriez, B., Submitted (4) Lestriez, B.; Desaever, S.; Danet, J.; Moreau, P.; Plée, D.; Guyomard, D. Electrochem. Solid-State Lett. 2009, 12(4), A76. (5) Nguyen, B. P. N.; Gaubicher, J.; Lestriez, B. Electrochim. Acta 2014, 120, 319–326. 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 | 0.002 |
| 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) | 0.003 | 0.002 |
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".