Thick Architected Silicon Composite Battery Electrodes Using Honeycomb Patterned Carbon Nanotube Forests
Bibliographic record
Abstract
To meet the growing performance demands for personal electronics and electric vehicles the energy density of lithium-ion batteries can be increased by incorporating thicker electrodes. We present thick “honeycomb” electrodes based on patterned, vertically aligned carbon nanotubes (CNTs) on Cu foils. Thick electrodes are created by Si deposition on >100 μm tall honeycomb patterned CNTs. Si-CNT electrodes are cycled in half-cells, demonstrating electronic connection between the Si and Cu foil via the aligned CNTs. For ~4.7 mAh cm−2 capacity the honeycomb patterning improves capacity retention (78%) over 30 cycles compared to non-patterned electrodes (58%). We attribute this improvement to the honeycomb’s ability to accommodate Si expansion, thereby reducing cracking that causes active material loss and solid electrolyte interphase instability, and to provide pathways for Li-ion transport into the electrode. The Si-CNT electrode capacity is further increased to 20 mAh cm−2 by increasing the Si loading. Finally, a fluoroethylene carbonate containing electrolyte is used to increase cell lifetime. Here, the honeycomb electrodes have a higher areal (~10.2 mAh cm−2) and retained (65%) capacity over 180 cycles, and exhibit superior rate performance to their non-patterned counterparts. Our work demonstrates the role of patterning in enabling aligned CNTs as a robust template for thick battery electrodes.
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 | 0.000 | 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".