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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".