Combination of a Copper-Ion Selective Electrode and Fluorometric Titration for the Determination of Copper(II) Ion Conditional Stability Constants of Humic Substances
Bibliographic record
Abstract
A fluorescence quenching model using copper(II) ion (Cu(2+)) ion selective electrode (Cu-ISE) is developed. It uses parallel factor analysis (PARAFAC) to model fluorescence excitation-emission matrices (EEMs) of humic acid (HA) samples titrated with Cu(2+) to resolve fluorescence response of fluorescent components to Cu(2+) titration. Meanwhile, Cu-ISE is employed to monitor free Cu(2+) concentration ([Cu]) at each titration step. The fluorescence response of each component is fit individually to a nonlinear function of [Cu] to find the Cu(2+) conditional stability constant for that component. This approach differs from other fluorescence quenching models, including the most up-to-date multi-response model that has a problematic assumption on Cu(2+) speciation, i.e., an assumption that total Cu(2+) present in samples is a sum of [Cu] and those bound by fluorescent components without taking into consideration the contribution of non-fluorescent organic ligands and inorganic ligands to speciation of Cu(2+). This paper employs the new approach to investigate Cu(2+) binding by Pahokee peat HA (PPHA) at pH values of 6.0, 7.0, and 8.0 buffered by phosphate or without buffer. Two fluorescent components (C1 and C2) were identified by PARAFAC. For the new quenching model, the conditional stability constants (logK1 and logK2) of the two components all increased with increasing pH. In buffered solutions, the new quenching model reported logK1 = 7.11, 7.89, 8.04 for C1 and logK2 = 7.04, 7.64, 8.11 for C2 at pH 6.0, 7.0, and 8.0, respectively, nearly two log units higher than the results of the multi-response model. Without buffer, logK1 and logK2 decreased but were still high (>7) at pH 8.0 (logK1 = 7.54, logK2 = 7.95), and all the values were at least 0.5 log unit higher than those (4.83 ~ 5.55) of the multi-response model. These observations indicate that the new quenching model is more intrinsically sensitive than the multi-response model in revealing strong fluorescent binding sites of PPHA in different experimental conditions. The new model was validated by testing it with a mixture of two fluorescing Cu(2+) chelating organic compounds, i.e., l-tryptophan and salicylic acid mixed with one non-fluorescent binding compound oxalic acid titrated with Cu(2+) at pH 5.0.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".