Noncovalent Interactions Dictate Surface Chemical Functionalization of Carbon Nitrides with Demonstration from Methylation
Bibliographic record
Abstract
Polymeric carbon nitride (CN x ) is promising for removing pollutants from water due to its high surface area and chemical stability. As a photocatalyst, CN x can degrade pollutants under light irradiation. However, its efficiency can be limited by the lack of reactive sites, and modifying its surface is challenging due to its unreactive nature. This study focuses on the postsynthetic methylation of the surface of CN x to convert amino groups into quaternary ammonium groups to impart positive charge and enhance water remediation activity. Through experimental characterization and quantum mechanical calculations, we explore the chemical reactivity of the amino groups of CN x and its analogues within the carbon nitride family (CNF)─melamine, melam, and melem. Our findings demonstrate successful methylation of the CNF members except for bulk CN x . Disrupting noncovalent interactions is shown to enhance amino groups’ reactivity, and forming CN x nanosheets (CNNS) allows successful methylation of the polymer. The effectiveness of the methylation of CNNS to generate positively charged surface groups was demonstrated via a proof-of-concept application in As(V) remediation: methylated CNNS displayed a significant reduction of As(V) concentration in water, in contrast to nonmethylated CNNS and CN x, which showed poor As(V) adsorption capabilities. This study provides valuable insights into surface modification strategies for CN x and CNNS, highlighting the impact of noncovalent interactions on function and offering a promising approach for sustainable water remediation solutions.
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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".