Hydrothermal Synthesis and Characterization of Co<sub>2</sub>GeO<sub>4</sub>/Rgo@C Ternary Composite As Negative Electrodes for Li-Ion Batteries
Bibliographic record
Abstract
In this present work, pristine Co2GeO4, binary Co2GeO4/reduced graphene oxide (rGO) composite and ternary composite of Co2GeO4/rGO@C is prepared by single step hydrothermal process followed by calcination. The amount of carbon content and rGO is identified through thermogravimetric analysis (TGA). XRD analysis reveals the compound formation of single phase Co2GeO4. TEM and HRTEM images clearly elucidate the presence of ternary phases of Co2GeO4, r-GO and C in the ternary composite. The Galvanostatic charge-discharge (GCD) curve demonstrates that the initial discharge capacity of pristine Co2GeO4, Co2GeO4/rGO and Co2GeO4/rGO@C composite is 1400, 1284 and 1594 mAh g-1 at 50 mAh g-1, respectively. The observed discharge capacity is higher than the theoretical capacity of Co2GeO4 which is due to reduction of organic electrolyte and the formation of solid electrolyte interphase film (SEI). The cycling stability curve shows the specific capacity of 609, 970 and 1180 mAh g-1 for pristine, Co2GeO4/rGO and Co2GeO4/rGO@C composite respectively over 15 cycles which confirms that Co2GeO4/rGO@C composite exhibits the stable and high specific capacity. The rate capability curve and EIS spectrum is carried out for the prepared samples which indicates that Co2GeO4/rGO@C composite shows the better rate capability and good electronic conductivity.
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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".