Use of an ion-selective electrode for free copper measurements in low salinity and low ionic strength matrices
Bibliographic record
Abstract
Environmental context. The toxicity of metals in the environment is controlled by several parameters including total metal concentration, pH and organic and inorganic ligands (type and concentration). The characterisation of different metal pools in natural matrices (e.g. seawater, soil) is important for the evaluation of their toxic impact. The copper ion-selective electrode (Cu-ISE) is a method of choice for the analytical determination of the speciation (i.e. chemical forms) of divalent copper in natural matrices. This paper clarifies several operational parameters in the hope of decreasing variability of results and increasing the application domain of the Cu-ISE. Abstract. The determination of free copper concentrations in natural matrices is critical for the evaluation of copper toxicity. The ISE is one of the few analytical means for determining the direct speciation of free metal species. We have refined the method for low salinity and low ionic strength solutions for application with soil water extracts or fresh waters. Moreover, we have detailed and standardised a method for using a Cu-ISE with an autotitrator. The standardisation shows a good response and allows significant time saving (under 2 h for the calibration). The results obtained using the ISE are compared with those predicted in the presence of different organic ligands or even the lower free Cu2+ activities resulting from the formation of Cu hydroxyl species. The method was validated for the determination of Cu speciation at environmentally relevant free Cu2+ activity, i.e. ranging between 10-14 to 10-4 M. The chemical equilibrium calculations were made using the MINEQL+ software and the results agree well for pH values between 3 and 10. In terms of precision, the standard deviations of the measured values never exceed 0.1 units, and in terms of accuracy, the measured values were very close to the nominal values, within a range of 0.1. Outside the optimal pH range, the electrode yields higher activity than expected.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".