Evaluation of reagentless pH modification for in situ ocean analysis: determination of dissolved inorganic carbon using mass spectrometry
Bibliographic record
Abstract
RATIONALE: In situ analytical techniques that require the storage and delivery of reagents (e.g., acidic or basic solutions) have inherent durability limitations. The reagentless electrolytic technique for pH modification presented here was developed primarily to ease and to extend the longevity of dissolved inorganic carbon (DIC) determinations in seawater, but can also be used for other analytical methods. DIC, a primary carbon dioxide (CO(2)) system variable along with alkalinity, controls seawater pH, carbonate saturation state, and CO(2) fugacity. Determinations of these parameters are central to an understanding of ocean acidification and global climate change. METHODS: Electrodes fabricated with electroactive materials, including manganese(III) oxide (Mn(2)O(3)) and palladium (Pd), were examined for potential use in electrolytic acidification. In-line acidification techniques were evaluated using a bench-top membrane introduction mass spectrometry (MIMS) setup to determine the DIC content of artificial seawater. Linear least-squares (LLSQ) calibrations for DIC concentration determinations over a range between 1650 and 2400 µmol kg(-1) were obtained, using both the novel electrolytic and conventional acid addition techniques. RESULTS: At sample rates of 4.5 mL min(-1), electrodes clad with Mn(2)O(3) and Pd were able to change seawater pH from 7.6 to 2.8 with a power consumption of less than 3 W. Although calibration curves were influenced by sampling rates at a flow of 4.5 mL min(-1), the 1σ measurement precision for DIC was of the order of ±20 µmol kg(-1). CONCLUSIONS: Calibrations obtained with the novel reagentless technique and the in-line addition of strong acid showed similar capabilities for DIC quantification. However, calculations of power savings for the reagentless technique relative to the mechanical delivery of stored acid demonstrated substantial advantages of the electrolytic technique for long-term deployments (>1 year).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".