Suppressing Lithium Dendrite Growth with Nanoparticle-Dispersed Colloidal Electrolytes
Bibliographic record
Abstract
Lithium (Li) metal has been considered as a very promising anode material for next-generation batteries because of its low electronegativity (-3.04 V), low gravimetric density (0.53 g cc-1) and ultrahigh specific capacity (3860 mAh g-1). The recent simulations forecast that the rechargeable Li metal batteries (LMBs) coupled with the conventional transient metal oxide cathodes and the Li metal anode can deliver a high gravimetric energy density of up to 500 Wh kg-1. However, significant challenges, including low Coulombic efficiency (CE), short cycle life, and safety concerns have plagued the practical applications of Li metal as the anode in recharge batteries. However, its poor cycling stability due to Li dendrite formation and excessive Li pulverization is the major hurdle for its practical applications. In this work, we present a silica (SiO2) nanoparticle-dispersed colloidal electrolyte (NDCE) and its design principle for suppressing Li dendrite formation. SiO2 nanoclusters over the percolated threshold of Li+ ion transport play roles of enhancing the Li+ transference number and increasing the Li+ diffusivity in the vicinity of the Li plating substrate. NDCE enables less-dendritic Li plating by manipulating the nucleation-growth mode and extending Sand's time. Moreover, SiO2 can interplay with the electrolyte at the Li-metal surface, and thus, it can modify the solid-electrolyte interphase (SEI) structure by enriching fluorinated compounds. The initial control of the Li plating morphology and SEI structure by NDCE leads to a more uniform and denser Li deposition upon subsequent cycling, resulting in three-fold enhancement of the cycle life. The efficacy of the NDCEs has been further demonstrated by the practical battery design featuring a commercial-level NMC cathode (4.2 mAh cm-2) and very thin Li metal (40 μm) anode.
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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) | 0.000 | 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".