Spray‐Dried Mesoporous Mixed Cu‐Ni Oxide@Graphene Nanocomposite Microspheres for High Power and Durable Li‐Ion Battery Anodes
Bibliographic record
Abstract
Abstract Exfoliated graphene‐wrapped mesoporous Cu‐Ni oxide (CNO) nanocast composites are developed using a straightforward nanostructure engineering strategy. The synergistic effect of hierarchical mesoporous CNO nanobuilding blocks that are homogeneously wrapped by graphene nanosheets (GNSs) using a rapid spray drying technique effectively preserves the electroactive species against the volume changes resulting from the charge/discharge process. Owing to the intriguing structural/morphological features arising from the caging effect of exfoliated graphene sheets, these 3D/2D CNO@GNS nanocomposite microspheres are promising as high‐performance Li‐ion battery anode materials. They exhibit unprecedented electrochemical behavior, such as high reversible specific capacity (initial discharge capacities exceeding 1700 mAh g−1 at low 0.1 mA g−1, stable 850 and 730 mAh g−1 at 1 and 5 mA g−1 after 800 and 1300 cycles, respectively, and higher than 400 mAh g−1 at very high current density of 10 mA g−1 after more than 2000 cycles), excellent coulombic efficiency and long‐term stability (more than 3000 cycles with >55% capacity retention) at high current density that are remarkable compared to most transition metal oxides and nanocomposites prepared by conventional techniques. This simple, yet innovative, material design is inspiring to develop advanced conversion materials for Li‐ion batteries or other energy storage devices.
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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 | 0.000 | 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".