Nitrogen Doping on Carbon Paper Electrodes
Bibliographic record
Abstract
Carbon papers are widely used as electrode materials for electrochemical applications, include but not limited to; flow batteries, fuel cells, supercapacitors, sensors, and bioelectrochemical systems. In order to increase the hydrophilicity of carbon paper, and hence improve the electrochemical properties, one of the common approaches is to incorporate oxygen containing functional groups when heating in air at relatively elevated temperature, resulting in increases of the wetting properties of carbon paper and improve the electrochemical performance. As our work demonstrate here, this approach is not ideal, especially when dealing with electrochemical system that contain high acid concentration or are sensitive to hydrogen peroxide production from oxygen reduction. We provided here a simple alternative approach via a pre-physiochemical treatment to successfully prepare a nitrogen-doped carbon paper that reduces the in-situ H2O2 production and at the same time, remarkably increases the electrochemical activity of the electrode. Characterisation using Raman spectroscopy for the prepared N-doped graphite paper indicates higher defects in structure that fit well with XPS and BET analyses and provide explanation of many interfacial charge transfer observations. For example, VO2 +/VO2+ and [Fe(CN)6]4−/3− redox couples show with the N-doped carbon paper a superior catalytic activity compared to other preheated or bare carbon papers could be obtained. Thus, the prepared N-doped carbon paper, can provide alternative promising electrode material which is suitable for higher efficiency for various electrochemical 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".