A dopant introduction device for atmospheric pressure photoionization with liquid chromatography/mass spectrometry
Bibliographic record
Abstract
This technical note describes in detail the fabrication, operation and characterization of a pneumatically driven dopant introduction device, with a solvent reservoir capacity of 300 mL. Dopant flow rates and stability for this device are governed by the simple regulation of gas pressure rather than the progression of a stepper motor and syringe diameter, as is the case for typical infusion pumps. The device has the potential to provide days or even weeks of continuous, uninterrupted dopant flow at rates commonly adopted for atmospheric pressure photoionization (APPI) experiments without the need to replenish the dopant supply. Although not a refined instrumental design, this device was developed as an alternative cost-effective means of introducing stable dopant flow to an APPI source. The device was designed such that all components would be commercially available and easily procurable from common scientific part vendors. Figures and suggested part numbers are provided to allow those interested to fabricate similar devices to suit their individual experimental needs. Device characterization was performed while monitoring such factors as flow rate calibration, overall flow stability and reproducibility. In addition, a standard mixture of three polycyclic aromatic hydrocarbons was employed as a model sample for a typical reversed-phase liquid chromatography/atmospheric pressure photoionization mass spectrometry (LC/APPI-MS) application in order to demonstrate device performance.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 0.002 |
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".