A Simple Approach to Prepare Metal Oxides Supra-Structures for LIBs
Bibliographic record
Abstract
A variety of materials have been investigated as potential electrode materials for LIBs. Electrodes including Fe, Co, Ni or Cu have more Lithium ion storage capacity (more than 600 mAh/g) comparing to graphite (about 372 mAh/g). Recently, much effort has been focused toward achieving 3-dimensional hollow spheres with high surface area and porous for better capacity performance. In this study a simple spray drying approach has been introduced to synthesize porous CuO and Co3O4 microspheres. The results revealed that uniform structures of the nanoparticles microspheres were achieved. Then their cyclic performance were analyzed and compared to their commercial counterparts. The porous CuO microspheres and Co3O4 microspheres exhibited high capacity retention (86.2% of the discharge capacity of the second cycle after 60 cycles) and (89.8% of the discharge capacity of the second cycle after 40 cycles) at a current density of 400 mA/g, respectively. The excellent electrochemical properties could be attributed to their unique porous structures. The electrochemical results showed that microspheric electrode materials are able to manifest superior electrochemical properties compared to their commercial counterparts.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".