Effects of Phase State and Phase Separation on Dimethylamine Uptake of Ammonium Sulfate and Ammonium Sulfate–Sucrose Mixed Particles
Bibliographic record
Abstract
Unexpectedly high amounts of aminium salts have been detected in ambient aerosol particles, prompting investigations into their role in new particle formation and nanoparticle growth. Amine uptake and particle-phase processes, including the effects of phase state and non-ideal mixing, are poorly understood. In this study, we conducted kinetic multi-layer modeling of dimethylamine (DMA) uptake by crystalline and aqueous ammonium sulfate (AS) and mixed ammonium sulfate–sucrose particles based on measurements at different relative humidity (RH) values. The temporal evolution of particle mass increases and the humidity dependence were successfully reproduced by considering the amine/ammonium exchange reaction and formation of hygroscopic dimethylaminium sulfate. Thermodynamic equilibrium predictions suggest that aqueous sucrose and AS mixtures undergo phase separation at RH < 94%. The kinetic model simulations reveal that DMA uptake is limited by diffusion of DMA and AS through a viscous sucrose-rich shell at lower RH, resulting in strong concentration gradients in the particle bulk. The model predicts that the true uptake coefficients would range from 2.0 × 10–5 to 2.6 × 10–3 for initially solid particles at low RH, while they can be as high as 0.70–0.82 in aqueous particles at high RH. Uptake coefficients increase when RH and associated particle water content increase, while they generally decrease when the molar fraction of sucrose increases at a specific value of RH. Using new measurements of ambient amines from the Holistic Interactions of Shallow Clouds, Aerosols and Land Ecosystems (HI-SCALE) field campaign as a reference, the model is extrapolated to particles with a diameter of 30 nm and amine mixing ratios in the ppt range to emulate atmospheric conditions. At 70% RH or higher with particles in the liquid phase, amine uptake can lead to a mass increase of approximately 20–60%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".