Sampling and analysis of nanoparticles with cold fibre SPME device
Bibliographic record
Abstract
A new approach is described to capture nano-size aerosols on internally-cooled micro tubing of the solid-phase microextraction (SPME) device followed by convenient introduction of the collected analytes into analytical instrument. Particles were generated using an aerosol formation by homogeneous nucleation of an organic vapor, and subsequent growth to nano-size particles by coagulation of decanedioic acid, bis[2-ethylhexyl] ester (DEHS). The approach was validated by using carbon dioxide-cooled micro tubing to collect the nanosize DEHS particles followed by analyses on GC-flame ionization detector (FID). Particle size ranged from 150 to 590 nm. Temperature difference between the SPME device and DEHS particles mixture created a temperature gradient and resulted in thermophoretic effect that was determining the extraction rate. SPME device was cooled to as low as -75 degrees C, while the DEHS remained close to room temperature. Several aspects of nanoparticle sampling were tested to demonstrate the principle of the sampling approach. These included the effects of thermal gradient, sample flow rate, sampling time, CO(2) delivery mode (constant coolant delivery vs. constant temperature), and particle size. Results were normalized to measure particulate concentrations using direct sampling with PTFE filters. Nanoparticle extractions of DEHS mass were proportional to sampling time. Normalized mass of DEHS extracted increased with increase in temperature gradient and with increase of the cross flow velocity. Preliminary results indicate that the variation of heat transfer boundary layer caused by the variation in the cross flow velocity produce self-compensating effect at constant coolant delivery, indicating that this approach could be used for field determinations including the time-weighted average sampling of nanoparticles. Thus, it may be possible to develop simple device based on this concept for field applications.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".