Enhanced Electrochemical Performance in Supercapacitors through KCu−Cy Based Metal‐Organic Framework Electrodes
Bibliographic record
Abstract
In the realm of electronics and electric energy storage, the convergence of organic and metallic materials has yielded promising outcomes. In this study, we introduce a novel metal-organic polymer synthesized from Cyamelurate and copper (KCu-Cy) and explore its application as an electrode for a supercapacitor. This material was pressed onto a stainless-steel grid as a thin film and synthesized on nickel foam. Comprehensive characterization was carried out to confirm the synthesis, ensure phase purity, and investigate atomic interactions. Single Crystal X-ray Diffraction (SCXRD) and Powder X-ray Diffraction (PXRD) analyses verified the synthesis and phase purity, shedding light on atomic arrangements. Fourier Transform Infrared Spectroscopy (FTIR) analyses provided insights into characteristic peaks within the material. Thermal Gravimetric Analysis (TGA) gauged stability and durability. Electrochemical performance was assessed through cyclic voltammetry. Notably, the nickel-supported electrodes, devoid of binders, exhibited exceptional specific capacity, reaching 1210.89 F/g at a scan rate of 5 mV/s, in contrast to 363.73 F/g for the pressed thin film on the stainless-steel grid, which incorporated a conductive agent and binder. Cu-Cy displayed impressive cyclization resistance, with a capacity retention of 90 % even after 11000 cycles. These findings underline the promise of Cu-Cy as a high-performance electrode material for supercapacitors, particularly in binder-free configurations, and suggest its potential in advanced energy storage applications.
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".