Drastic-yet-distinct alterations in rarefied gas transport of CO2 and propane in nanochannels by finely-tuning surface characteristics
Bibliographic record
Abstract
• Under ambient conditions in nanochannels, CO 2 self-diffusivity is only half of that of propane, against the Knudsen theory. • Finely-tuning surface properties boosts CO 2 self-diffusivities by ∼ 290 % versus ∼ 35 % for propane. • The distinct alteration of transport is achieved by prohibiting CO 2 penetration and consequently limiting its rotations. • CO 2 individual molecular behaviors are more sensitive to the surface characteristics compared to propane. Rarefied gas transports with similar molecular weights (such as CO 2 and propane) render similar Knudsen diffusivity in nanochannels. Nevertheless, by using molecular dynamics (MD) simulations, we find that the Knudsen theory breaks down for rarefied CO 2 and propane transport in β-cristobalite nanochannels with width of 5 nm under ambient conditions (298 K and 1 atm): CO 2 self-diffusivity is only half of that of propane. The drastic differences in their self-diffusivity are due to the penetration of CO 2 into the three-dimensional hexagonal ring structures on β-cristobalite surface, resulting in substantial CO 2 rotations and curved topological accessible plane, which are detrimental to its diffusion. In contrast, propane cannot penetrate into pore surface. On the other hand, by finely-tuning surface properties (the size of surface oxygen atoms), we observe drastic-yet-distinct alterations in their self-diffusivities: the enhancement in CO 2 self-diffusivities is more than 8-fold of that for propane (290 % v.s. 35 %). This is achieved by prohibiting CO 2 penetration and consequently limiting its rotations, thereby largely promoting its transport. On the other hand, the bending structure of propane, coupled with its larger size, always prevents its penetration into regular or tuned (pseudo) surface. Our study indicates that the collective effects of fluid and surface characteristics are instrumental to rarefied gas transport in nanochannels which are largely overlooked in conventional diffusion models and previous experimental as well as simulation studies. This work offers novel insights into rarefied gas transport mechanisms and the development and optimization of advanced materials for gas capture and separation.
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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 | 0.000 |
| 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".