Cobalt–manganese-zinc ternary phosphate for high performance supercapattery devices
Bibliographic record
Abstract
State of the art supercapatteries have received considerable attention for their significant electrochemical performance; however, electrode materials with enhanced charge storage capabilities are desired. Here, we report the synthesis of mixed metal phosphate nanomaterials with different concentrations via a sonochemical approach. Initially, binary metal phosphates based on zinc, cobalt, and manganese were synthesized. Then, the composition of zinc and cobalt was optimized in ternary metal phosphates. Scanning electron microscopy, energy dispersive X-ray spectroscopy and X-ray diffraction techniques were utilized to examine the surface morphology, elemental analysis and crystal structure of as-synthesized nanomaterials. The electrochemical characterizations were performed in a three cell configuration. Zn0.50Co00.50Mn(PO4)2 delivers the optimum performance with a specific capacity of 1022.52 C g-1 (specific capacitance of 1704.21 F g-1) at 1.2 A g-1. This optimized material was further engaged in an asymmetric device (supercapattery) as a positive electrode material to explore the real device performance. The supercapattery device was found to have an impressive specific energy of 45.45 W h kg-1 at 0.5 A g-1 and provide a remarkable specific power of 4250 W kg-1 at 5 A g-1 current density. The device exhibits excellent capacity preservation of 93% examined after 1500 charge discharge cycles. In addition, to scrutinize the supercapattery performance in terms of capacitive and diffusion controlled processes, a simulation approach was adopted. The real device comprises a capacitive contribution of 8.42% at 3 mV s-1 and 66.56% at 100 mV s-1. This novel progress in ternary metal phosphates results in a fine electrode material for high performance supercapattery applications.
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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 | 0.001 | 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".