Designing Niobium Oxide Anodes for Flexible and Fast‐Charging Lithium‐Ion Batteries
Bibliographic record
Abstract
Flexible lithium-ion batteries (LIBs) are critical for powering emerging technologies such as wearable electronics, biomedical devices, and soft robotics. These systems must maintain stable performance under mechanical deformation while enabling fast-charging capability. However, achieving both high areal capacity and structural durability remains a significant challenge. Increasing the electrode mass loading improves energy density, but it often results in mechanical brittleness and limited ionic transport-issues that are especially pronounced in conventional slurry-based fabrication methods, which typically rely on toxic solvents like N-methyl-2-pyrrolidone. In this work, a flexible anode is presented based on mixed-phase niobium oxide-a defect-engineered material known for its excellent rate performance and stability. The electrode is fabricated using a solvent-free dry process that incorporates carbon nanofibers and polytetrafluoroethylene to form a robust, fibrillated architecture. Moreover, a conductive polymer adhesive, poly(3,4-ethylenedioxythiophene):polystyrenesulfonate, is introduced to enhance interfacial contact between the electrode film and various current collectors, eliminating the need for heat or pressure during assembly. This integrated design not only overcomes the limitations of rigid LIBs under mechanical deformation but also mitigates environmental concerns by avoiding the use of toxic solvents. Overall, the approach offers a promising pathway toward high-performance, readily manufacturable flexible LIBs suitable for practical applications.
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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 | 0.001 | 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".