New approaches to calculate the transfer function of particle mass analyzers
Bibliographic record
Abstract
This article provides an overview of methods to evaluate transfer functions for the Couette centrifugal particle mass analyzer (CPMA) and aerosol particle mass analyzer (APM). The work first considers finite difference approaches to solving the partial differential equation governing particle motion, which represents an accurate but computationally-demanding approach to evaluating the transfer function. This is used as a baseline to compare to particle tracking methods, which have been shown to yield closed form expressions for the transfer function. In this work, we extend on previous treatments by presenting a generalized framework that allows us to consider a range of representations of the particle migration velocity. As a result, we derive new closed form expressions for the exact representation of the particle migration velocity under APM conditions and provide significant improvements in the accuracy of the transfer function for CPMA conditions. In the latter case, for a CPMA, particle migration effects dominate, which makes the transfer function easier to approximate. We also show that Taylor series approximations to the particle migration velocity should be taken about the centerline radius rather than the equilibrium radius as was done previously. We end by extending the particle tracking approach and derive new closed form expressions for the transfer function that include diffusion.Copyright © 2019 American Association for Aerosol Research
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".