Synergetic and anomalous effect of <scp>CNTs</scp> in the sulphide‐based binary composite for an extraordinary and asymmetric supercapacitor device
Bibliographic record
Abstract
Abstract Carbon nanotubes (CNTs) have attained great interest from researchers due to their excellent electrical conductivity, vast surface area, and good chemical stability. In this work, the sulphide‐based composite Ag 2 S@ZnS was synthesized using the hydrothermal method and was doped with CNTs in various weight percentage ratios. The structural and morphological characteristics of the samples were evaluated by employing X‐ray diffractometry (XRD), X‐ray photo spectroscopy (XPS), scanning electron microscopy (SEM), Brunauer–Emmett–Teller (BET) analysis, and thermogravimetric analysis (TGA), while cyclic voltammetry (CV) and galvanostatic charge/discharge (GCD) were also executed for their electrochemical characterization. The performance of the Ag 2 S@ZnS electrode was enhanced after the doping of CNTs because of their synergistic effect. An extraordinary specific capacity ( Q s ) of 946.5 Cg −1 (262.91 mAh g −1 ) was exhibited by Ag 2 S@ZnS with 50% CNTs doping (Ag 2 S@ZnS/CNT‐50%), which is significantly greater than the reference samples. Furthermore, an asymmetric supercapacitor was designed and assessed for its electrochemical properties. The specific capacity of the asymmetric supercapacitor reached 148.62 Cg −1 (41.28 mAh g −1 ). The device showed improved stability and retained the 87% initial capacity after 5000 cycles. The energy and power densities were found to be 33.02 Wh kg −1 at 639.98 W kg −1 , respectively, with a high value of coulombic efficiency of 92%. The device succeeded in acquiring a higher power density of 3200 W kg −1 for an energy density of 4 Wh kg −1 . These astonishing results provide opportunities to design high‐performance electrode materials for extraordinary energy storage devices.
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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".