Optimization of MnO2, NiO and MnO2@NiO electrodes using graphene oxide for supercapacitor applications
Bibliographic record
Abstract
MnO2@GO, [email protected] and MnO2@[email protected] electrodes were hydrothermally fabricated for applications in supercapacitor energy storage devices. Because of its large surface area and low electrical resistance, graphene oxide (GO) was added to the nanocomposites during electrode fabrication. The mutual co-operation of diverse components and composites with GO increased the performance, longevity, and hardness of electrodes. The greatest specific capacitance measured by cyclic voltammetry (CV) at 10 mV/s scan rates and GCD at 1.0 A/g current density for MnO2@GO, [email protected], and MnO2@[email protected], respectively, was 652, 425, 985 and 773, 487, 1141 F/g for MnO2@GO, [email protected], and MnO2@[email protected] Performances of various electrodes clearly show that composites of two transition metal oxides/GO had better performance when compared to single transition metal oxide/GO and the addition of graphene oxide enhanced the supercapacitive performances of the electrodes.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".