Emerging investigator series: chemical and physical properties of organic mixtures on indoor surfaces during HOMEChem
Bibliographic record
Abstract
Organic films on indoor surfaces serve as a medium for reactions and for partitioning of semi-volatile organic compounds and thus play an important role in indoor chemistry. However, the chemical and physical properties of these films are poorly characterized. Here, we investigate the chemical composition of an organic film collected during the HOMEChem campaign, over three cumulative weeks in the kitchen, using both Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) and offline Aerosol Mass Spectrometry (AMS). We also characterize the viscosity of this film using a model based on molecular formulas as well as poke-flow measurements. We find that the film contains organic material similar to cooking organic aerosol (COA) measured during the campaign using on-line AMS. However, the average molecular formula observed using FT-ICR MS is ∼C50H90O11, which is larger and more oxidized than fresh COA. Solvent extracted film material is a low viscous semisolid, with a measured viscosity <104 Pa s. This is much lower than the viscosity model predicts, which is parametrized with atmospherically relevant organic molecules, but sensitivity tests demonstrate that including unsaturation can explain the differences. The presence of unsaturation is supported by reactions of film material with ozone. In contrast to the solvent extract, manually removed material appears to be highly viscous, highlighting the need for continued work understanding both viscosity measurements as well as parameterizations for modeled viscosity of indoor organic films.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".