N-Heterocyclic Carbenes Excel at Functionalizing Diverse Carbon Surfaces with Robust Organic Monolayers
Bibliographic record
Abstract
The controlled deposition of organic monolayers on carbon substrates is a well-known challenge, with existing methods yielding partial coverages or non-uniform, uncontrollable multilayers. N-heterocyclic carbenes (NHCs) have recently been shown to form stable self-assembled monolayers (SAMs) on a range of metals. Here, we demonstrate that NHCs with diverse alkylated backbones and a variety of terminal groups can also be reliably deposited as uniform layers at up to monolayer coverages on a wide range of carbon surfaces, including planar carbons and highly surface-defective carbon powders. Tunable surface coverages, strong support for NHC chemisorption, and excellent electrochemical and thermal stability of these monolayers is observed, comparable to NHC or alkylthiol SAMs on metals. We showcase one application of NHCs on carbon, demonstrating an impedimetric sensor for bacterial biomarker detection, achieving performances associated with metal-supported SAMs. This work positions NHCs as transformative tools for carbon surface modification, enabling applications previously restricted only to metal-supported SAMs.
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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".