A new hot-stage microscopy technique for measuring temperature-dependent viscosities of aerosol particles and its application to farnesene secondary organic aerosol
Bibliographic record
Abstract
Abstract. The viscosity of secondary organic aerosol (SOA) is needed to improve predictions of air quality, climate, and atmospheric chemistry. Many techniques have been developed to measure the viscosity of micrometer-sized materials at room temperature; however, few techniques are able to measure viscosity as a function of temperature for these small sample sizes. SOA in the troposphere experience a wide range of temperatures, so measurement of viscosity as a function of temperature is needed. To address this need, a new method was developed based on hot-stage microscopy combined with fluid dynamics simulations. The current method can be used to determine viscosities in the range of roughly 104 to 108 Pa s at temperatures greater than room temperature. Higher viscosities may be measured if experiments are carried out over multiple days. To validate our technique, the viscosities of 1,3,5-tris(1-naphthyl)benzene and phenolphthalein dimethyl ether were measured and compared with values reported in the literature. Good agreement was found between our measurements and literature data. As an application to SOA, the viscosity as a function of temperature for lab-generated farnesene SOA material was measured, giving values ranging from 3.1×106 Pa s at 51 ∘C to 2.6×104 Pa s at 67 ∘C. We fit the temperature-dependent data to the Vogel–Fulcher–Tammann (VFT) equation and obtained a fragility parameter for the material of 7.29±0.03, which is very similar to the fragility parameter of 7 reported for α-pinene SOA by Petters and Kasparoglu (2020). These results demonstrate that the viscosity as a function of temperature can be measured for lab-generated SOA material using our hot-stage microscopy method.
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 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 | 0.001 | 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 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".