Two chemistries on a single lab-on-chip: Nitrate and orthophosphate sensing underwater with inlaid microfluidics
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Bibliographic record
Abstract
Autonomous in situ sensors are required to monitor high-frequency nutrient fluctuations in marine environments on a mass-scale. We present a submersible, dual-chemistry sensor that performs multiple colourimetric assays simultaneously on a fluid sample for multi-parameter in situ analysis. Based on a highly configurable architecture that has been successfully deployed for several multi-month periods, the sensor utilizes 10 solenoid valves, 4 syringes, 3 stepper motors, 2 LEDs, 4 photodiodes, and “inlaid” microfluidics to permit optical measurements of microliter fluid volumes. Fluid pathways are machined into a modular two-layer microfluidic lab-on-chip (LOC) fabricated from poly (methyl methacrylate) (PMMA) with two parallel inlaid optical cells of 10.4 mm and 25.4 mm path lengths (1.7 µl and 4 μl, respectively). Different LOC designs can be used to implement a wide variety of colorimetric assays. We demonstrate application of our dual-chemistry sensor towards simultaneous measurement of nitrate and dissolved orthophosphate: two nutrients fundamental to primary production. The performance of the dual-species nitrate and phosphate “NP Sensor” is characterized first in a controlled laboratory environment. Combined nutrient standards containing nitrate and phosphate concentrations ranging from 2.5 µM–100 µM <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:msubsup><mml:mrow><mml:mi mathvariant="normal">N</mml:mi><mml:mi mathvariant="normal">O</mml:mi></mml:mrow><mml:mn>3</mml:mn><mml:mo>−</mml:mo></mml:msubsup></mml:mrow></mml:math> and 0.25 µM–10 µM <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"><mml:mrow><mml:msubsup><mml:mrow><mml:mi mathvariant="normal">P</mml:mi><mml:mi mathvariant="normal">O</mml:mi></mml:mrow><mml:mn>4</mml:mn><mml:mrow><mml:mn>3</mml:mn><mml:mo>−</mml:mo></mml:mrow></mml:msubsup></mml:mrow></mml:math> were analyzed, reporting detection limits of 97 nM <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m3"><mml:mrow><mml:msubsup><mml:mrow><mml:mi mathvariant="normal">N</mml:mi><mml:mi mathvariant="normal">O</mml:mi></mml:mrow><mml:mn>3</mml:mn><mml:mo>−</mml:mo></mml:msubsup></mml:mrow></mml:math> and 15 nM <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m4"><mml:mrow><mml:msubsup><mml:mrow><mml:mi mathvariant="normal">P</mml:mi><mml:mi mathvariant="normal">O</mml:mi></mml:mrow><mml:mn>4</mml:mn><mml:mrow><mml:mn>3</mml:mn><mml:mo>−</mml:mo></mml:mrow></mml:msubsup></mml:mrow></mml:math> . Calibrations were repeated under 3 fixed temperature conditions, T = 5°C, 10°C, 15°C, to determine the temperature-dependent sensitivity relations for both species needed to calculate concentrations during field deployments. Finally, an 8-day field deployment in Fish Hatchery Park, NS, Canada followed, acquiring a total of 592 nitrate and dissolved orthophosphate measurements. An on-board combined nutrient standard was measured periodically to assess the in situ accuracy of the sensor, with an average relative uncertainty of 15% across the deployment. Measured nitrate and dissolved orthophosphate levels in the river reached as high as 10 µM and 3.6 µM, respectively. Fast Fourier transform analysis suggests a strong out-of-phase relationship between measured phosphate and water level, with a shared frequency peak in both data agreeing within a 3.2% difference. This trend is due to conventional mixing at the river mouth to neighboring Bedford Basin. A spike in the measured nitrate to phosphate (N:P) ratio was also observed, synchronized to a precipitation event and indicative of runoff. The novel sensor will enable high-frequency dual-nutrient monitoring in many aquatic environments.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it