Bulk Phase Dominates Sulfur Dioxide Hydrolysis over Interfacial Processes
Bibliographic record
Abstract
Sulfur dioxide (SO2) hydrolysis is a critical step in secondary sulfate formation, which significantly affects air quality and climate change. Since the 1980s, debate has persisted over whether this reaction occurs mainly at the air–water interface or in the bulk phase. In this study, we investigate SO2 hydrolysis in heterogeneous systems using molecular dynamics simulations that are driven by a deep neural network potential with ab initio accuracy. In previous studies, rapid interfacial reactions have been proposed to account for the unexpectedly high SO2 uptake coefficients. In contrast, our results reproduce the observed uptake coefficients but show that interfacial hydrolysis contributes only 1%. We find that hydrolysis is accelerated in the bulk phase, where the denser hydrogen-bond network enhances SO2 electrophilicity and lowers the reaction barrier. The theoretical simulations in this work help to improve the understanding of aqueous sulfate aerosol formation and microdroplet chemistry. Sulfur dioxide hydrolysis is a major atmospheric removal pathway. Here the authors use neural-network-potential driven molecular dynamics simulations to show that this process is accelerated in the bulk phase rather than at air-water interfaces.
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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".