Super‐Assembled Hierarchical CoO Nanosheets‐Cu Foam Composites as Multi‐Level Hosts for High‐Performance Lithium Metal Anodes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Achieving uniform lithium (Li) deposition is the key to tackle uncontrollable dendrite growth, which hinders the application of Li metal anodes. In this study, molten Li is thermally injected into a 3D framework by growing lithiophilic CoO nanosheets on Cu foam (CF). The CoO layer grown on the CF surface physically adsorbs molten Li, which makes it possible to spontaneously wet the framework. The morphology of CoO nanosheets does not change during the Li injection process and formed a multi‐level structure with the CF, which is difficult to be achieved previously, as most lithiophilic oxides undergo serious chemical changes due to chemical reaction with Li and cannot provide a stable submicron structure for the subsequent Li stripping/plating process. The super‐assembled multi‐level structure provides abundant Li nucleation sites and electrolyte/electrode contact areas for rapid charge transfer in the composite anode. Therefore, the prolonged lifespan of symmetrical cells for 300 cycles at 10 and 10 mAh cm −2 with lower polarization is achieved, which further renders the LiFePO 4 and Li 4 Ti 5 O 12 based full cells with improved capacity retention up to 87.3% and 80.1% after 500 cycles at 1 C. These results suggest that the composite anode has a great application prospect.
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.
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) | 0.000 | 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) | 0.000 | 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 it