Impact of dimensionality and confinement on reaction dynamics and thermodynamics within 1D and 2D nanostructures
Bibliographic record
Abstract
Confinement has been shown to contribute to the dynamics of small molecules within nanoscale hydrophobic or hydrophilic cavities. Enclosure within a confined space can also influence energy transfer pathways, such as the enhancement of fluorescence over thermal relaxation. In this paper, the effect of confinement on the thermodynamic properties and reaction kinetics of small hydrophobic molecules confined in a soft polymeric template is detailed. A quasi-elastic neutron scattering experiment identified a substantial decrease in translational diffusion of pyrrole after solubilization within a hydrophobic cavity. This decrease in mobility is due to pyrrole's closer packing and increased density under confinement vs the bulk liquid. The decreased mobility and increased density explain the spontaneous polymerization reaction of pyrrole observed within the cavity. The precise characterization of the polymerization kinetics under confinement found that the reaction is independent of pyrrole concentration, consistent with the close packing density. Kinetic data also show that confinement dimensionality finds a thermodynamic expression in the transition state entropy. The dynamics and kinetics experiments reported here offer rare empirical insight into the important influence that cavity geometry places on the reactions they host.
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 | 0.001 |
| 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) | 0.001 | 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".