Condensation in One-Dimensional Dead-End Nanochannels
Bibliographic record
Abstract
Phase change at the nanoscale is at the heart of many biological and geological phenomena. The recent emergence and global implications of unconventional oil and gas production from nanoporous shale further necessitate a higher understanding of phase behavior at these scales. Here, we directly observe condensation and condensate growth of a light hydrocarbon (propane) in discrete sub-100 nm (∼70 nm) channels. Two different condensation mechanisms at this nanoscale are distinguished, continuous growth and discontinuous growth due to liquid bridging ahead of the meniscus, both leading to similar net growth rates. The growth rates agree well with those predicted by a suitably defined thermofluid resistance model. In contrast to phase change at larger scales (∼220 and ∼1000 nm cases), the rate of liquid condensate growth in channels of sub-100 nm size is found to be limited mainly by vapor flow resistance (∼70% of the total resistance here), with interface resistance making up the difference. The condensation-induced vapor flow is in the transitional flow regime (Knudsen flow accounting for up to 13% of total resistance here). Collectively, these results demonstrate that with confinement at sub-100 nm scales, such as is commonly found in porous shale and other applications, condensation conditions deviate from the microscale and larger bulk conditions chiefly due to vapor flow and interface resistances.
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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".