Electrocatalytic Activity of Functionalized Carbon Paper Electrodes and Their Correlation to the Fermi Level Derived from Raman Spectra
Bibliographic record
Abstract
Carbon paper electrodes are employed for different electrochemical applications such as flow batteries and fuel cells. However, redox reactions such as VO 2+ /VO 2 + in a vanadium redox flow battery have been found to possess relatively slow kinetics, resulting in significant activation losses during operation. In this work, we demonstrate a facile and scalable method for nitrogen doping of carbon paper electrodes, leading to superior electrocatalytic activity. The effects of pyrolytic pretreatments under different conditions on the performance of carbon paper were also studied to elucidate their electrocatalytic activity from a material physics perspective, using Raman spectroscopy. The 2D Raman signature, a specific feature of the carbon structures, was employed to understand the effect of different pretreatments on the Fermi level of the carbon papers, which could help us elucidate their intrinsic electron transfer kinetics. The full wave half-maximum of the 2D Raman band and the intensity ratio I 2D / I G were used to indicate changes in the Fermi level relative to the untreated carbon paper, and hence the electrocatalytic properties, which were confirmed using voltammetric techniques. Although heating of carbon paper in air at around 500 °C (a widely used method for activating carbon paper electrodes) increases the surface area by about 16 times compared to untreated and nitrogen-doped carbon paper, the latter exhibits superior electrocatalytic property for VO 2+ /VO 2 +, [Fe(CN) 6 ] 3–/4–, and the oxygen reduction reaction. This study provides greater physical insights into different pretreatments in terms of the energy barrier at the interface, which will aid the pursuit for better carbon-based electrode materials and provide mechanistic details about charge transfer processes at the interface.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".