Fluorescence-Based Siderophore Biosensor for the Determination of Bioavailable Iron in Oceanic Waters
Bibliographic record
Abstract
With direct evidence that iron is the chemical limitation of phytoplankton growth, particularly in the Southern Ocean, it is increasingly important to develop new tools that provide direct measurement of the bioavailable iron fraction in oceanic waters. Here we report the development of a fluorescence quenching-based siderophore biosensor capable of the in situ measurement of this ultratrace Fe(III) fraction at ambient pH ( approximately 8). Parabactin was extracted from cultures of Paracoccus denitrificans. The purified siderophore was encapsulated within a spin-coated sol-gel thin film, which was subsequently incorporated in a flow cell system. The parabactin biosensor has been fully characterized for the detection of Fe(III) in seawater samples. The biosensor can be regenerated by lowering the pH of the flowing solution, thereby releasing the chelated Fe(III), enabling multiple use. The LOD of the biosensor was determined to be 40 pM, while for an Fe(III) concentration of 1 nM, a reproducibility with a RSD of 6% (n = 10) was obtained. The accuracy of the biosensing system has been determined through analysis of a certified seawater reference sample. Samples from the Atlantic Ocean have been analyzed using the parabactin biosensor providing a concentration vs depth profile for the bioavailable Fe(III) fraction in the 50 pM-1 nM range.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".