From Spent Lithium‐Ion Batteries to High‐Performance Supercapacitors: Enabling Universal Gradient Recycling via Spin Capacitance
Bibliographic record
Abstract
Abstract Driven by environmental imperatives and the growing economic challenges posed by the accumulation of spent batteries, developing effective recycling strategies has become paramount. Current direct battery recycling methodologies primarily focus on structural restoration, but the universality of this approach is hampered by the variability in electrode degradation mechanisms and the extent of irreversible damage sustained after cycling. To overcome these inherent limitations, this research introduces a universally applicable in situ recycling strategy that rejuvenates the metal components within batteries. Through an in situ facile electrochemical treatment, the cathode material is engineered to create a nanostructured interface composed of transition metal/lithium compounds, enhancing intrinsic electron/ion conduction and enabling substantial charge storage with accelerated transfer capabilities. Furthermore, operando magnetometry reveals that the energy storage mechanism aligns with a space charge mechanism, manifesting as spin‐polarized capacitance. As proof of concept, the recycled LiFePO 4 ‐based batteries are in situ converted into high‐performance supercapacitors, boasting an energy density of 106 Wh kg −1 and a power density of 10,714 W kg −1 , alongside impressive cycling stability with 91.3% capacitance retention after 2000 cycles. This approach demonstrates feasibility with LiFePO 4 and extends to other commercial cathodes such as LiCoO 2 , LiNi 1/3 Co 1/3 Mn 1/3 O 2 , and even their blends, offering a groundbreaking solution for lithium‐ion battery recycling.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".