An Efficient and Controllable Prelithiation of Silicon Monoxide for Improving Energy Density of Lithium-Ion Rechargeable Full Cells
Bibliographic record
Abstract
High capacity silicon (Si) anodes are expected to play a key role in increasing the energy density of current lithium-ion batteries (LIBs). In spite of this attractive feature, its huge volume change over repeated cycles impairs the cycle life through pulverization, film delamination, and unstable solid-electrolyte-interphase (SEI) formation, and thereby hinders its practical applications. As an alternative approach, silicon monoxide (SiOx) phase has been recently adopted because its SiO2 background matrix can buffer the volume expansion of inner Si nano-domains. Nonetheless, most of these phases suffer from inferior performance, namely initial Coulombic efficiency (ICE). This shortcoming originates from Li ion trapping in the matrix and SEI layer formation during the first lithiation. In an attempt to catch the two challenging rabbits (cycle life and ICE), the current study has developed delicately controlled pre-lithiation for SiOx anodes. It should be first noted that a proper degree of lithiation is very critical for stable full-cell operations. We pre-lithiate the pristine electrode via an electrical short with Li metal foil in the presence of an optimized circuit resistance while simultaneously monitoring the voltage between both electrodes. Utilizing a fine-tuning capability in the degree of lithiation, a pre-lithiation condition that simultaneously maximizes the ICE and cycle life was found and was also engaged for robust full-cell operations by pairing with a commercially available high capacity cathode. The accurate shorting time and voltage monitoring allow a fine tuning on the degree of pre-lithiation without lithium plating, to a level that the initial CE reaches 94.9%. The excellent reversibility enables robust full-cell operations in pairing with an emerging nickel-rich layered cathode, Li[Ni0.8Co0.15Al0.05]O2, leading to a full cell energy density 1.5 times as high as that of graphite-LiCoO2 counterpart in terms of the active material weight. The given procedure is compatible with the existing roll-to-roll manufacturing line and so must be immediately applicable to the current state of the art LIB anodes containing certain contents of SiOx. Figure 1. Electrochemical characterization of c-SiOx/ Li[Ni0.8Co0.15Al0.05]O2 full cells mad e of the pristine and 30 min pre-lithiated c-SiOx. (a) Voltage profiles of the first cycles. (b) Cycling performance, with regard to the areal capacity. 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) | 0.000 | 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) | 0.001 | 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".