Multifunctional Silicon Anode for Lithium-ion Batteries
Bibliographic record
Abstract
A silicon anode is prepared for application in lithium-ion batteries that can serve different functions such as decreasing the amount of the binder and thus allowing a larger quantity of the active material, better overall integration of the electrode components upon cycling and better stability of the solid-electrolyte interface that could enable to completely get rid of the expensive electrolyte additives. The synthesis of the materials involves the functionalization of a hydrogenated silicon nanopowder by covalent attachment of polyacrylic acid that partially substitutes the silicon-oxygen bond via a silicon-carbon bond. The presence and grafting of polyacrylic acid is confirmed by transmission and scanning microscopy and energy dispersive X-ray spectroscopy, thermogravimetric analysis, Fourier transform infrared and X-ray diffraction spectroscopy. The electrochemical performance of the silicon anode is evaluated by galvanostatic cycling, cyclic voltammetry and four point-probe electronic conductivity measurements. The composite silicon anode showed a significantly improved performance, relative to the hydrogenated Si electrode in terms of gravimetric capacitance (1200 mAh g-1 for more than 100 cycles at 0.5 C rate) with a 100 % capacity retention in a capacity limited discharge/charge cycling. When an unmodified and modified-based electrodes are allowed to fully discharge/charge, a lower initial capacity loss and a better overall physical integrity of the structure is achieved for the latter. Moreover, the composite electrode performs better at high rate discharge/charge cycles. Unlike reports making use of an electrolyte additive for silicon anode, mainly to afford stability of the solid-electrolyte interface and better cyclability, the polyacrylic acid (PAA) modified silicon composite electrode can be cycled without such additive and demonstrate a better electrochemical performance than the unmodified silicon. Reference: [1] S. Yang, Q. Pan, J. Liu, Electrochem. Commun. 2010, 12, 479. [2] B. D. Assresahegn, T. Brousse, D. Bélanger, Carbon 2015, 92, 362–381. [3] B. D. Assresahegn, D. Bélanger, Adv. Funct. Mater. 2015, 25, 6775. [4] M. P. Stewart, J. M. Buriak, Comments Inorg. Chem. 2010, 23, 179. [5] C. Martin, M. Alias, F. Christien, O. Crosnier, D. Bélanger, T. Brousse, Adv. Mater. 2009, 21, 4735. [6] C. Martin, O. Crosnier, R. Retoux, D. Bélanger, D. M. Schleich, T. Brousse, Adv. Funct. Mater. 2011, 21, 3524. 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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".