Elemental analysis using micro Laser-induced Breakdown Spectroscopy (μLIBS) in a microfluidic platform
Bibliographic record
Abstract
We present here a non-labeled, elemental analysis detection technique that is suitable for microfluidic chips, and demonstrate its applicability with the sensitive detection of sodium (Na). Spectroscopy performed on small volumes of liquids can be used to provide a true representation of the composition of the isolated fluid. Performing this using low power instrumentation integrated with a microfluidic platform makes it potentially feasible to develop a portable system. For this we present a simple approach to isolating minute amounts of fluid from bulk fluid within a microfluidic chip. The chip itself contains a patterned thin film resistive element that super-heats the sample in tens of microseconds, creating a micro-bubble that extrudes a micro-droplet from the microchip. For simplicity a non-valved microchip is used here as it is highly compatible to a continuous flow-based fluidic system suitable for continuous sampling of the fluid composition. We believe such a nonlabeled detection technique within a microfluidic system has wide applicability in elemental analysis. This is the first demonstration of laser-induced breakdown spectroscopy (LIBS) as a detection technology in conjunction with microfluidics, and represents first steps towards realizing a portable lower power LIBS-based detection system.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".