Investigation of Reduced Graphene Oxide Modified Zinc Anodes Using Various Electrochemical Techniques
Bibliographic record
Abstract
Zinc ion batteries are promising battery energy storage systems due to their high capacity, abundancy and low cost, but are held back by competing reactions, passivation, and dendrite formation which all greatly reduce performance. We modify zinc anode by introducing a self-reducing graphene oxide coating. This protects against dendrite formation and reduces side reactions, increasing the performance of the cell. Half-cell experiments are performed to determine whether sodium sulphate, sodium acetate, or a combination of the provides any advantage over the other, as well as to determine the effect the pattern of the reduced graphene oxide coating on performance. Additionally, symmetric zinc coin cells are assembled, and studied by galvanostatic charge/discharge cycles, and chronopotentiometry. The effect of the reduced graphene oxide coating, electrolyte composition, choice of separator (paper/glass) on the cycling stability and over potential of zinc deposition is studied. The partially covered (island) zinc anode greatly reduces the nucleation overpotential to 8.75 mV when assembled in a symmetric coin cell. Excellent cycling stability is observed at a current density of 5 mA cm-2 charged to 5 mAh cm-2 when using a combination background electrolyte of sodium sulphate and sodium acetate using a glass fibre separator.
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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 | 0.000 |
| 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".