An Automated Microfluidic Analyzer for <i>In Situ</i> Monitoring of Total Alkalinity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We have designed, built, tested, and deployed an autonomous in situ analyzer for seawater total alkalinity. Such analyzers are required to understand the ocean carbon cycle, including anthropogenic carbon dioxide (CO 2 ) uptake and for mitigation efforts via monitoring, reporting, and verification of carbon dioxide removal through ocean alkalinity enhancement. The microfluidic nature of our instrument makes it relatively lightweight, reagent efficient, and amenable for use on platforms that would carry it on long-term deployments. Our analyzer performs a series of onboard closed-cell titrations with three independent stepper-motor driven syringe pumps, providing highly accurate mixing ratios that can be systematically swept through a range of pH values. Temperature effects are characterized over the range 5–25 °C allowing for field use in most ocean environments. Each titration point requires approximately 170 μL of titrant, 830 μL of sample, 460 J of energy, and a total of 105 s for pumping and optical measurement. The analyzer performance is demonstrated through field data acquired at two sites, representing a cumulative 25 days of operation, and is evaluated against laboratory measurements of discrete water samples. Once calibrated against onboard certified reference material, the analyzer showed an accuracy of −0.17 ± 24 μmol kg –1 . We further report a precision of 16 μmol kg –1, evaluated on repeated in situ measurements of the aforementioned certified reference material. The total alkalinity analyzer presented here will allow measurements to take place in remote areas over extended periods of time, facilitating affordable observations of a key parameter of the ocean carbon system with high spatial and temporal resolution.
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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 | 0.000 |
| 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 it