Tailored Self-Supported Co,Ni/MnO<sub>2</sub> Nanorods@Hierarchical Carbon Spheres Chains as Advanced Electrocatalysts for Rechargeable Zn-Air battery and Self-Driven Water Splitting
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Designing multifunctional electrocatalysts that combine high efficiency, durability, and affordability for energy storage represents a significant challenge. Here, we introduce a novel trifunctional electrocatalyst synthesized by doping self-supported surface electrochemically functionalized carbon sphere chains/MnO 2 nanorods with Co or Ni (Func CSCs-2M/Co 0.25 (or Ni 0.25 ) MnO x ). These electrocatalysts demonstrate exceptional electroactivity for the oxygen evolution reaction, oxygen reduction reaction, and hydrogen evolution reaction, along with durability comparable to that of commercial Pt/C and IrO 2 catalysts. Two Zn-air batteries (ZABs) equipped with Func CSCs-2M/Co 0.25 MnO x cathodes, connected in series, have the capability to power 39 red light-emitting diodes continuously for an impressive duration of 200 h. Moreover, a self-sustaining water splitting system, powered by ZABs, is showcased, utilizing Func CSCs-2M/Co 0.25 MnO x as the exclusive catalyst. This system sustains a consistent voltage for up to 20 h under an applied current density reaching as high as 30 mA cm –2 . This performance rivals that of noble catalyst systems, showcasing its competitive edge. The study emphasizes the cost-effectiveness of materials and the utilization of low-carbon, renewable rechargeable ZAB energy storage systems, combined with water electrolysis. Such integration has the potential to make a substantial impact in addressing long-term energy and environmental challenges, easing the pressure on these critical fronts.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".