Effect of Pretreatment on Carbon Materials
Bibliographic record
Abstract
Carbon materials find application in numerous fields because of its large specific surface area, good electronic conductivity, inexpensive and inertness under corrosive conditions. These materials on appropriate pre-treatment have shown to give better performance which is attributed to different functional groups on surface. Surface functionalization, apart from showing improved performance also impact structure and charge carrier concentration of carbon materials and as we move towards large scale production there is a need to understand effect of these pretreatments to envision long term effects on device performance to minimize losses. We attempted to study, structural changes because of functionalization of carbon paper. Carbon paper electrodes are used widely for various electrochemical applications such as flow batteries, catalyst support in fuel cells, waste water treatment, Bioelectrochemistry. We employed Raman spectroscopy technique to study defects on carbon paper before and after functionalization and effect of these functionalization on charge carrier concentration. Carbon papers, pretreated employing different methods to study its effect on fibre structure in terms of defects and charge carrier distribution. This study could assist us to gain an important understanding on impact of conventional pretreatment methods being currently followed which is not ideal. This conventional pretreatment was also compared with nitrogen functionalization treatment which showed superior catalytic activity and a promising pretreatment method for large scale application.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".