The Capacitive Behavior of Polyluminol on Carbon Nanotubes Electrodes
Bibliographic record
Abstract
Abstract A polymerized luminol carbon nanotube (CNT) composite electrode was developed via an in situ chemical polymerization (CpLum) process. Density functional theory (DFT) simulation suggested the luminol molecules were preferentially aligned flat on the CNTs. This was further demonstrated through a morphological study, which showed the CpLum wrapped around each CNT homogeneously with an average thickness of 4.5±1.5 nm. The surface chemical analysis by X‐ray photoelectron spectroscopy (XPS) revealed a progressive increase in the nitrogen content and stabilized at 9 %. Deconvolution of the high‐resolution N 1s spectra suggested the presence of secondary and tertiary amine functional groups, which are the signatures of polymerized luminol. The composite electrodes exhibited a pseudocapacitive‐like behavior with 3.5 times increase in charge storage. The contributions from the CpLum coating and CNT substrate were differentiated and were further deconvoluted to quantify the capacitive charge storage of each component. The thin CpLum coating contributed 70 % of the total charge storage through pseudocapacitance. CpLum‐CNT electrodes also showed a high rate capability and good cycling stability, very promising for electrochemical capacitors.
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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".