Nanobubble nucleation dynamics in reacting microdroplets: Insights from confocal laser scanning microscopy and molecular dynamics simulations
Bibliographic record
Abstract
Highlights • Nanobubble nucleation in reactive droplets requires surpassing a constant critical gas concentration. • Gas production rate dictates induction times and shifts nucleation sites from interior to interface. • Phase-specific gas solubility modulates nucleation by either elevating thresholds or depleting gas. • Lower water–gas interfacial tension accelerates nucleation and redistributes bubble formation. Gas-evolving interfacial reactions in microdroplets underpin processes in catalysis, energy conversion, and microreactor technologies, yet the principles of nanobubble nucleation remain unclear. Here, we integrate confocal laser scanning microscopy with coarse-grained molecular dynamics simulations to elucidate hydrogen nanobubble formation during base-catalyzed reactions of liquid organic hydrogen carrier (LOHC) droplets with aqueous NaOH. We reveal a competition-controlled nucleation mechanism governed by gas production rate, asymmetric solubility in droplet and surrounding phases, and water–gas interfacial tension. Nucleation occurs only when local gas concentrations exceed a critical threshold that is largely independent of production rate but strongly influenced by gas solubility in two phases. High production rates shorten induction times and shift nucleation toward the LOHC–water boundary, whereas increased solubility in LOHC or water suppresses nucleation, raising the critical threshold or extracting gas from the droplet. Reduced interfacial tension lowers the nucleation barrier, accelerates onset, and favors interfacial nucleation. These findings establish principles for controlling gas evolution in reactive emulsions, offering design guidelines for interfacial microreactors and nanobubble-enabled catalytic systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".