Impact of pH on Gas-Particle Partitioning of Semi-Volatile Organics in Multicomponent Aerosol
Bibliographic record
Abstract
The partitioning of semivolatile organic compounds (SVOCs) between the condensed and gas phases can have significant implications for the properties of aerosol particles. In addition to affecting size and composition, this partitioning can alter radiative properties and impact cloud activation processes. We present measurements and model predictions on how activity and pH influence the evaporation of SVOCs from particles to the gas phase, specifically investigating aqueous inorganic particles containing dicarboxylic acids (DCAs). The aerosols are studied at the single-particle level by using optical trapping and cavity-enhanced Raman spectroscopy. Optical resonances in the spectra enable precise size tracking, while vibrational bands allow real-time monitoring of pH. Results are compared to a Maxwell-type model that accounts for volatile and nonvolatile solutes in aqueous droplets that are held at a constant relative humidity. The aerosol inorganic–organic mixture functional group activity coefficients thermodynamic model and Debye–Hückel theory are both used to calculate the activities of the species present in the droplet. For DCAs, we find that the evaporation rate is highly sensitive to the particle pH. For acidity changes of approximately 1.5 pH units, we observe a shift from a volatile system to one that is completely nonvolatile. We also observe that the pH itself is not constant during evaporation; it increases as DCAs evaporate, slowing the rate of evaporation until it eventually ceases. Whether a DCA evaporates or remains a stable component of the droplet is determined by the difference between the lowest p K a of the DCA and the pH of the droplet.
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 | 0.000 |
| 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".