Impact of pre-charged particles on steady state and pulsed modes of unipolar diffusion chargers
Bibliographic record
Abstract
Fine mode particulate matter (PM) is known to be a major risk for human health as it can cause respiratory and cardiovascular diseases. Whether produced from anthropogenic or natural processes, fine mode PM is often electrically charged with positive, negative and/or bipolar charges. These preexisting charges can, in some cases, significantly affect measurements from sensors which involve a charging stage prior to measurement by, e.g., electrometry. In this work, we demonstrate quantitatively the impact of preexisting charges (q=0,±1,±2) on three different unipolar diffusion charging (DC) configurations using both simulations and experiments. The DC configurations include traditional steady state (SS) DC and pulsed modes of DC, where in all configurations a positive corona charger is used. We show that the impact of preexisting charges on SS and modulated precipitation (MP) DC is comparable. Positively pre-charged particles cause a larger deviation for SS and MP DC, in contrast to modulated diffusion charging (MDC), where negatively pre-charged particles have a greater impact. In addition, we show that the ion concentration in the charging region can be increased to significantly reduce the resulting error caused by the pre-charges for both SS DC and MP DC, in contrast to MDC. These results show that the influence of unipolarly pre-charged particles is very differently pronounced for the different modes of DC. The impact strongly depends on the polarity of the pre-charges, characteristics of the charger and the DC configuration itself. These insights are particularly interesting for the design and the application of DC based sensors.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".