Atomic layer deposition niobium oxide and lithium niobium oxide as a protection technique for anode‐free batteries
Bibliographic record
Abstract
Abstract As demand for extended range in electric vehicles and longer battery lifetimes in consumer electronics has grown, so have the requirements for higher energy densities and longer cycle lifetimes of the cells that power them. One solution to this is the implementation of an “anode‐free” battery. By removing the anode and plating lithium directly onto the current collector, it is possible to access the same capacities and voltage windows as traditional lithium metal batteries, with the entirety of the lithium source coming from the cathode. Herein, a copper foil current collector coated with niobium oxide or lithium niobium oxide through atomic layer deposition (ALD) is applied to extend the cycling life of the anode‐free batteries by reducing dendrite formation and improving the stability of the lithium metal surface throughout cycling. The ALD coatings are able to extend the cycle lifetime in full coin cells from 20 cycles to 80% capacity retained in the bare copper controls to 50 and 115 cycles for the NbO and LiNbO coatings, respectively. Over the lifetime of the cells, the ALD‐LiNbO is able to cumulatively offer a staggering improvement of an additional 100 kWh L−1 compared to the bare copper control.
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".