Enabling Aluminum-Silicon Electrodes As Monolithic Anodes for Lithium-Ion Batteries
Bibliographic record
Abstract
Based on current trends in lithium-ion battery (LIB) production and forecasts for immense demand of these energy storage devices, it is evident that all cell components will need dramatic improvement in the future. Although anode materials may be considered by some to be a lower priority, compared to cathode materials, the cost of the anode (and Cu foil current collector) comprise nearly 25% of the materials cost of a LIB cell, and consume ~30% of the volume. Among the possible alternative anode materials for LIBs to replace slurry-based graphite electrodes, alloying materials such as Si, Sn, Al and Ge are promising candidates. Despite the attractive advantages of aluminum-based electrodes (such as low cost, good geographic dispersity and high abundancy), capacity fade due to a large volume change associated with the α/β (Al/LiAl) phase transformation during cycling is the key problem. Al alloy or composite electrodes are a promising strategy for improving the reliability of Al-based electrodes. Among the Al-based monolithic anode materials for LIBs, to the best of our knowledge, Al-rich Al-Si alloys or composites have rarely been investigated. The present work highlights a new achievement towards realizing monolithic, free-standing alloy anodes which can significantly reduce both the cost and processing complexity of LIB electrodes. Herein, we propose a novel strategy to enable the use of aluminum-silicon alloys as monolithic anodes for LIBs. Accordingly, the microstructural, morphological, and electrochemical evolution of Al-Si thin-films of various compositions were investigated, with a focus on understanding the process of Al-Si-Li ternary phase formation during lithiation (in the particular case of excess Al). Ex situ microscopy observations, along with comprehensive electrochemical analysis, suggests that remarkable performance can be achieved by controlling the electrochemical condition for the formation of Al-Si-Li ternary phase, such that no LiAl (β phase) forms during lithiation. In other words, Al-Si-Li ternary phase is cycled within a soft aluminum matrix, thereby avoiding the degradation associated with the de-/lithiation of the β phase [1]. [1] M. H. Tahmasebi, D. Kramer, R. Mönig, and S. T. Boles, “Insights into Phase Transformations and Degradation Mechanisms in Aluminum Anodes for Lithium-Ion Batteries,” J. Electrochem. Soc., vol. 166, no. 3, pp. A5001–A5007, 2019.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".