A Surface Grafting of Carbon Allotropes with <i>in‐situ</i> Generated 3‐aryl Diazonium Chlorides: Electrochemical Kinetic Studies
Bibliographic record
Abstract
Abstract In this work three different carbon allotropes: multi‐walled carbon nanotubes (MWCNT), graphene and carbon nanohorns (CNH) were covalently bound with in‐situ generated 3‐diazonium aryl chlorides. These species were attached to the carbon surface using an electrochemically‐initiated radical coupling, similar to the Gomberg‐Bachman type reaction. An electrochemical grafting of the extended π‐systems at the carbon surface was performed in aqueous media in a simple 3‐electrode system and its kinetics was investigated by a modified Butler‐Volmer approach. The analysis of Hammett constants demonstrated a strong influence of the type of electron donating‐/withdrawing substituents on the reduction potential peak position and the peak current. Moderately or strong electron withdrawing groups like ‐carboxy or ‐nitro tend to shift reduction potential towards more positive values, which facilitates an electron uptake. The deposition time varies for different carbon allotropes and depends on the carbon graphitization and the surface area. The best surface coverage was obtained at 90–150 sec of the deposition. Although, the surface functionalities are less conductive than the carbon, the electrodes showed a low internal resistance and thus a high rate of electron transfer (high exchange current density and the electron transfer rate constant), with the most promising observed for carbon nanohorns. The best performing carbon revealed also superior mass transport of the redox active species toward the electrode surface, owing to their unique particle shapes and its very porous structure. The Tafel analysis complemented by an impedance spectroscopy allowed selecting the best carbon substrate for the functionalization with a 3‐aminobenzoic acid.
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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".