Role of flower-like ultrathin Co<sub>3</sub>O<sub>4</sub> nanosheets in water splitting and non-aqueous Li–O<sub>2</sub> batteries
Bibliographic record
Abstract
The oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) are both fundamental and essential processes for various energy conversion and storage systems. The kinetics of ORR and OER play a critical role in their energy efficiency and practicality. Here, flower-like ultrathin Co3O4 nanosheets synthesized through a facile solvothermal technique were studied as a bifunctional catalyst for both water splitting and non-aqueous Li-O2 batteries. Due to the novel structure and highly active {110} and {100} exposed facets, which can effectively facilitate mass transfer and enhance catalytic capability, Co3O4 nanosheets exhibit better stability and higher ORR/OER activity than Co3O4 nanoparticles, Co3O4 bulks, Pt/C, and RuO2 in alkaline solution. More importantly, Li-O2 batteries with ultrathin Co3O4 nanosheets catalyst can enhance the initial discharge capacity from 6400 to 8600 mA h g-1 and improve the cyclability up to 160 cycles at 500 mA g-1. Unexpectedly, XRD and UV/Vis techniques suggest that the main product in Co3O4 nanosheets based cathodes is LiOH, with resulting LiOH also demonstrating reversible formation/decomposition behavior, rather than Li2O2 in pure Super P based cathodes. Further investigation confirms that Co3O4 can also catalyze the electrolyte decomposition responsible for the formation of LiOH, and a reaction mechanism was illustrated. This work highlights that the traditional high-efficiency bifunctional catalyst in aqueous media may not be suitable for non-aqueous Li-O2 batteries, and the effect of catalyst on electrolyte besides the discharge product should also be carefully considered for the design of more stable and practical Li-O2 systems.
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 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) | 0.000 | 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".