Lab-on-chip analyser for the in situ determination of dissolved manganese in seawater
Bibliographic record
Abstract
Abstract A spectrophotometric approach for quantification of dissolved manganese (DMn) with 1-(2-pyridylazo)-2-naphthol (PAN) has been adapted for in situ application in coastal and estuarine waters. The analyser uses a submersible microfluidic lab-on-chip device, with low power (~ 1.5 W) and reagent consumption (63 µL per sample). Laboratory characterization showed an absorption coefficient of 40,838 ± 1127 L⋅mol−1⋅cm−1 and a detection limit of 27 nM, determined for a 34.6 mm long optical detection cell. Laboratory tests showed that long-term stability of the PAN reagent was achieved by addition of 4% v/v of a non-ionic surfactant (Triton-X100). To suppress iron (Fe) interferences with the PAN reagent, the Fe(III) masking agents deferoxamine mesylate (DFO-B) or disodium 4,5-dihydroxy-1,3-benzenedisulfonate (Tiron) were added and their Fe masking efficiencies were investigated. The analyser was tested during a deployment over several weeks in Kiel Fjord (Germany), with successful acquisition of 215 in situ data points. The time series was in good agreement with DMn concentrations determined from discretely collected samples analysed via inductively coupled plasma mass spectrometry (ICP-MS), exhibiting a mean accuracy of 87% over the full deployment duration (with an accuracy of > 99% for certain periods) and clear correlations to key hydrographic parameters.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".