A vaporization chamber for micro- or nano-sample introduction into a battery-operated microplasma: from rapid prototyping via 3D printing to Computational Fluid Dynamics (CFD) simulations
Bibliographic record
Abstract
Ideally, a chemical analysis instrument should to be brought to the sample for (near) real-time analysis onsite (rather than bringing a sample to a lab for analysis, as is usually done). In this paper, this paradigm shift is addressed using battery-operated microplasmas. But, how does one introduce an initially ambient temperature sample into a low-power (~10 W) gas-phase microplasma? One way is by using an eletrothermal vaporization sample introduction and a vaporization chamber for introduction of micro- (and nano-size) samples into a microplasma. But then, how does one develop an “optimized” vaporization chamber? To reduce cost and time-delays, rapid prototyping (via 3D printing) and smoke experiments were used, as detailed in this paper. In the future, candidate designs will be evaluated using CFD simulations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".