Nanoimmiscibility: Selective Absorption of Liquid Methanol–Water Mixtures in Carbon Nanotubes
Bibliographic record
Abstract
Despite the continuing research interests in CNT-liquid systems, the microscopic structure and transport behavior of liquid mixtures in carbon nanotubes (CNTs) remain poorly understood. Methanol and water liquids are completely miscible across the entire range of concentration; however, recent research reveals that they are immiscible at a molecular level. In this work, we carried out classical molecular dynamics to study the molecular distribution, structure ordering, clustering and transport behavior of liquid methanol-water mixtures within CNT confinement. We found that CNTs preferentially absorbed methanol over water molecule even though the latter has a smaller molecular size, indicating that chemical effect such as molecular hydrophilicity plays a crucial role in the molecular absorption of CNTs. Due to the selective absorption of CNTs, methanol aqueous solution changes from microscopically immiscible to macroscopically immiscible at nanoscale. This nanoscale immiscibility may be utilized in various applications of CNTs including direct methanol fuel cells, nanosensors, molecular sieves, nanofluidic chips, and capsules for drug delivery.
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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 | 0.001 |
| 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".