The Influence of Monolayer and Multilayer Diazonium Functionalities on the Electrochemical Oxidation of Nanoporous Carbons
Bibliographic record
Abstract
High surface area carbon powders are used in many electrochemical applications, e.g., fuel cells, supercapacitors, and batteries. However, despite their advantages, they are susceptible to oxidation and carbon corrosion when exposed to oxidizing potentials. Our goal has been to use diazonium chemistry to attach surface groups to block corrosion-susceptible sites and alter wettability. In prior work, mesoporous colloid imprinted carbons (CICs) with pores of 12–50 nm and still smaller pore necks hindered access of the diazonium precursors and limit mass transport in electrochemical applications. Here, CIC-85 powders (85 nm pores) were modified with -PhF 5 or PhSO 3 H groups to engender hydrophobicity or hydrophilicity, respectively. Both groups decrease corrosion-induced surface roughening of the CIC-85 by ∼50% in 0.5 M sulfuric acid. The -PhF 5 group decreases irreversible oxidation of carbon to CO 2 by a factor of ∼9, while the -PhSO 3 H group protects the CIC-85 surface by ∼4 times. An analogous free-standing, binder-free 85 nm pore size carbon sheet, exhibiting similar oxidation behavior, was examined by XPS, showing that surface functionalities are fully retained after corrosion. This work offers novel insights on the role, impact, and fate of diazonium-attached surface groups in protecting carbon surfaces during accelerated stress testing in sulfuric 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".