Optimization of the Electrochemical Nature of Ni/Co MOF Assisted by Zn Metal Centres for Better Electrode Performance in Hybrid Devices
Bibliographic record
Abstract
Owing to the exceptional porous coordination chemistry and expanded redox culture of mixed metal organic framework (MMOF) materials are now presenting a hot matter for their potential usage in battery-supercapacitor electrodes. Here, we synthesized Ni/Co-MOF and its based Zn x -(Ni/Co)y-MOF nanoparticles containing different transition metals ratio; x: y = 0.25:0.75, 0.50:0.50, and 0.75:0.25 by hydrothermal process and named as ZMOF1, ZMOF2 and ZMOF3. Their electrochemical profile was carried out by CV, GCD and EIS characterization in three electrode setup. Among the MOFs nanoparticles, partially Zn enriched ZMOF1 shows prominent specific capacity of 177.23 and 181.12 C g−1 in 1.0 and 3.0 M KOH electrolyte solution at current density of 0.3 A g−1 along with good rate capability performance. Meanwhile, it retains brilliant specific capacity ∼ 86% of its original value compared to other displayed by ZMOF2 and ZMOF3 (80% and 69%) after charging-discharging for 3000 cycles at j = 4.0 A g−1. Moreover, modified power law was utilized to estimate the battery-type charge storage worth of ZMOF1 from CV cycle (at 5 mV s−1) in 1.0 and 3.0 M electrolytes medium thereby found the contribution 91.58% and 94.32%, respectively. These features of ZMOF1 attributed to particle diverse morphology, enriched redox sites and admiring electrical conductivity.
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".