Binary metallic sulphide‐based nanocomposites with <scp>ZnO</scp> additives: A dual‐functioning electrode material for energy storage and glucose sensing
Bibliographic record
Abstract
Abstract In the contemporary age, there is a growing need for innovative energy storage solutions that integrate the crucial features of supercapacitors and rechargeable batteries to meet evolving energy requirements. Herein, we used the hydrothermal method to synthesize a binary composite electrode material. The addition of ZnO as a doping material significantly enhances the specific capacity of cobalt niobium sulphide (CoNbS) from 538.16 C g −1 to 912.62 C g −1 due to its high conductivity and the presence of redox‐active sites. We have also developed a supercapattery (CoNbS@ZnO//activated carbon [AC]), which has demonstrated outstanding performance with a remarkable specific capacity of 325.92 C g −1 at a current density of 1.5 A g −1 . Furthermore, the supercapattery device shows improved energy and power densities, measuring 35 Wh/kg and 2397 W/kg, respectively. After 5000 galvanostatic charging discharging (GCD) cycles, the device shows columbic efficiency (90%) and capacity retention (83%). Additionally, the CoNbS@ZnO//AC hybrid device is used as an electrochemical sensor for glucose detection. The device showed a high sensitivity against the glucose and detected up to a small value. The synthesis of nanomaterials opens up new opportunities to create high‐performance energy related systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".