Effect of Magnesium Site Doping on Electrochemical Properties of Mg-Mn Composite Materials and Application of Carpet-Like Sheets Composite Formed by Cr Doping in Aqueous Zinc-Ion Batteries
Bibliographic record
Abstract
Manganese based compounds have very stable electrochemical properties, thus they are widely applied in aqueous zinc-ion battery. However, it is easy to dissolve manganese in the process of charge and discharge, which leads to the collapse of the cathode material structure. In this paper, the composite materials of manganese carbonate and magnesium carbonate were synthesized by hydrothermal method, and its properties were optimized by doping modification. According to the electrochemical tests, the electrochemical performance of the material synthesized with 5% Cr doping is relatively stable. Moreover, with the increase of Cr content, the charge transfer impedance of the material can be significantly reduced. In the cyclic tests, after 60 cycles of charge and discharge under different current densities, and another several cycles at the current density of 50 mA g−1, the capacity rises to 212.01 mAh g−1, higher than the first discharge capacity. SEM tests are also conducted to survey the micro-structure of the material, besides the cube unit, there are also carpet-like sheets made of nano-sized joined petal-like unit, which greatly increases the specific surface area of the material. According to the data of EDS and XPS, the fact that that the types and valence states of elements in the composites were consistent with those in manganese carbonate and magnesium carbonate are confirmed. Through the infrared and Raman tests, the functional groups in the synthesized material are proven to correspond to the molecular formula. From the XRD pattern, no obvious impurity peaks can be observed. With all the data obtained from physical characterization, it can be concluded that the material is mainly composed of manganese carbonate and magnesium carbonate.
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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".