Hybrid Supercapacitor Electrode Materials via Molecular Imprinting of Nitrogenous Ligands and Iron Complexes into Carbon Support
Bibliographic record
Abstract
Novel hybrid supercapacitor materials were made by the covalent immobilization of nitrogenous ligands onto the surface of commercial carbon support (Vulcan XC-72), then coordinated to iron. The covalent attachment of the nitrogenous ligands allows for the controlled deposition of nitrogen functionalities on the surface of the carbon. The supercapacitor tests in acidic media showed significant growth of the capacitance as a result of the nitrogenous ligands on the support. Notably, the increase of the capacitance values directly correlates with the molecular loading on the surface. Following coordination of iron to the ligands on the surface further elevated the capacitance via Faradaic reactions of the metal center. Remarkably, the overall capacitance of materials significantly increased after the course of long-term cycling tests (ca. 110% or higher). At the beginning of durability studies, a small decline in capacitance was observed, due to some extent of molecular decomposition on the surface of the electrode. However, the intense cycling further propagates a steady growth of the overall capacitance of the materials. This could be attributed to the process of polymerization of physisorbed molecules/ radicals that result in the formation of a 3D network structure that eventually boosts the overall capacitance and the charge storage of the electrode.
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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".