Study on the Performance of the Ionic Liquids [Emim]CH3SO3 and [Emim]PF6 to Prepare the Biosensor of the Detection of Heavy Metals in Seawater
Bibliographic record
Abstract
Electrochemical sensors can detect the heavy metal ions in seawater quickly, conveniently and accurately with the advantages such as the fast detection speed, the simple operability and the low cost. The ionic liquid [Emim]CH3SO3 showed excellent electrochemical performance and could meet the initial application requirements as electrochemical sensors. The characters of the ionic liquids [Emim]CH3SO3 and [Emim]PF6 which include IR, LC-MS, conductivity, electrochemical window and viscosity were detected. The influ-ence of trace impurity on the conductivity of the ionic liquids was investigated. Results suggested that the conductivity of the ionic liquids increased with the concentration of the added organic solvents. In addition, though the conductivity of the ionic liquids increased with temperature, there is no significant difference in the influence of the same impurity at varying temperatures. Muti Walls Carbon Nanotubes (MWCNTs) are appropriate materials which are commonly used materials for electrochemical sensor applications. The effect of theImidaz-olium-based ionic liquids on the performance of the conductivity of the MWCNTs was studied. It was found that Ionic liquid is an excellent extraction agent for metal ions and its presence in the sensor system improves significantly the detection of heavy metal ions.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".