Hierarchy in Binary Liquid Phase Exfoliation of Graphitic Carbon Nitride: Dissecting the Dominance of One Solvent
Bibliographic record
Abstract
This study targets an important question in the synthesis of graphitic carbon nitride (g-C 3 N 4 ) nanosheets via liquid-phase exfoliation (LPE): how does a binary solvent mixture perform relative to its components? A machine learning model based on Extra Trees Regressor, when applied to 171 pure solvents and 14,535 binary solvents resulting from their arbitrary combination, reveals an interesting phenomenon where the LPE performance of the binary mixture can be dominated by one of its components. Quantitatively the free energy of exfoliation (ΔG exf ), defined as the free energy to separate a unit area of two stacked nanosheets, can have close values for the binary solvent and one of its components that has either a large ΔG exf (low LPE efficacy) or a small ΔG exf (high LPE efficacy). Such nonstandard performance is validated by umbrella sampling molecular dynamics (MD) simulations, and examined in detail for two representative binary solvents: one where N -Methyl-2-pyrrolidone (NMP) dominates in NMP:Cyclohexane mixture showing good LPE performance, and the other where methanol (MET) dominates in MET:Dichloromethane mixture showing poor LPE performance. Results from this study challenge existing, largely surface tension based, solvent selection criteria for LPE, and emphasize the importance of molecular details in the solvation layers around the nanosheets. Our numerical framework, integrating MD with machine learning, can be used to further explore strategies in the synthesis of 2D materials, such as optimizing solvent mixtures and introducing functional groups to g-C 3 N 4 .
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".