Mass transfer enhancement in continually replenished aerophilic surfaces
Bibliographic record
Abstract
Absorption of gas in liquid media is an important step in applications ranging from environmental remediation to bioreactor design, yet it is often limited by slow interfacial mass transport. Here, we show how continually replenished aerophilic surfaces significantly enhance gas uptake across a gas–liquid interface. Similar to superhydrophobic surfaces, they feature hierarchical micro/nanotextures designed to retain a stable trapped gas layer underwater known as the plastron. We fabricate aerophilic surfaces with microcapsule features covered with nanoscopic pinning points and varying inter-feature spacings using laser ablation. Testing for CO2 uptake in a potassium hydroxide solution at near-neutral pH, we find that they attain 80% of the maximum saturation concentration within one hour, while conventional diffusion from an overhead CO2 atmosphere reaches only 20%. Using bromothymol blue colorimetry, we observe a 20-fold enhancement in the mass transfer rate from aerophilic surfaces compared to an equivalent planar gas–liquid interface with the same projected area. Counterintuitively, the enhanced mass transfer rate is not driven by an increase in interfacial area. Instead, it arises from a 24-fold increase in the liquid-side mass transfer coefficient, likely resulting from reduced resistance to mass transfer across the gas–liquid interface in the plastron. Environmental scanning electron microscopy confirms our plastron model of hemispherical gas caps pinned atop microtextures, with interfacial area calculations corroborating findings from colorimetry. These results highlight the potential of dynamically replenished aerophilic surfaces to overcome mass transport limitations in multiphase systems with gases and liquids, with potential applications in carbon capture and sustainable energy.
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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 | 0.001 |
| 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".