Functional group chemistry as a determinant of graphitic carbon nitride nanosheet dispersibility: A molecular dynamics study
Bibliographic record
Abstract
• SO 3 H and COOH groups enhance g-C 3 N 4 exfoliation in DMF via reduced free energy. • Functionalization modulates solvent mobility/dipole interactions, affecting exfoliation. • CHO groups hinder exfoliation by increasing free energy and solvent disruption. • COOH-functionalized g-C 3 N 4 shows superior dispersibility in DMF (experimentally validated) • Solvent selection guidelines optimize functionalized g-C 3 N 4 nanosheet production. This study contributes to addressing the challenges in producing high-quality graphitic carbon nitride (g-C 3 N 4 ) nanosheets through liquid-phase exfoliation (LPE), by investigating the impact of chemical functionalization. Utilizing molecular dynamics simulations, functionalization with sulfonic (SO 3 H), carboxyl (COOH), amine (NH 2 ), hydroxyl (OH), and aldehyde (CHO) is explored to assess their impact on LPE efficiency in DMF. Our findings reveal that SO 3 H and COOH functional groups significantly enhance exfoliation efficiency by improving solvent-nanosheet interactions, decreasing solvent mobility, and reducing the free energy required for exfoliation. NH 2 and OH groups also contribute positively, though to a lesser extent, while CHO hinders the process by increasing the free energy of exfoliation and disrupting solvent–solvent interactions. Experimental validation confirms the superior dispersibility of COOH-functionalized g-C 3 N 4 in DMF compared to pristine g-C 3 N 4 , aligning with computational predictions. Based on these insights, practical guidelines are proposed for solvent selection to optimize the production of functionalized g-C 3 N 4 . Molecular-level mechanisms understood from this work can facilitate the development of strategies for advancing the synthesis and utilization of g-C 3 N 4 -based materials.
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 | 0.000 |
| 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) | 0.003 | 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".