Cobalt based solid state phosphate sensor with submicromolar detection range
Bibliographic record
Abstract
Phosphate monitoring is critical in assessing the nutrient pollution in water bodies like lakes and rivers, and in their management. Increase in phosphate concentration can result in algal bloom in the water bodies. Most of the commercial system uses colorimetric methods for phosphate measurements. However, these methods require reagents that may not be suitable for long term field use. Electrochemical methods such as potentiometry offer a good alternative to construct a solid-state sensor. Cobalt is a widely investigated solid-state electrode used for phosphate measurement. But the commercial success of the electrode is limited due to high limit of detection (>10-6M). This study demonstrates a pretreatment protocol to enable cobalt electrodes for phosphate measurements at sub micromolar concentrations (-6M). The two-step current pretreatment enhanced the sensitivity of the sensor from 3.1mV/decade (control) to 15.2mV/decade (with current pretreatment). Except sulphate, no significant interference was observed with common anions (nitrate, chloride, and acetate).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".