Determination of Strong Ligand Sites in Sewage Effluent-Impacted Waters by Competitive Ligand Titration with Silver
Bibliographic record
Abstract
A competitive ligand titration, employing Ag+, is used to determine the binding capacity of the small amounts of strong ligands (SL) in natural water samples. Strong ligands are defined here as high-affinity binding sites for group 11 and 12 metals such as Cu(I), Hg(II), and Ag(I). In addition, the conditional binding strength (log K') is determined for Ag- and SL. Diethyldithiocarbamate (DEDC) is the competitive ligand employed. The system is set at constant pH (8.1), ionic strength (0.1 M), and excess-fixed DEDC (10 microM) to determine SLs with log K' for Ag+ of >10. Silver was chosen as the titrant metal because it binds predominantly with S(-II) versus other ligands and reduced sulfur is thought to comprise the majority of SLs in natural waters. A two-phase system, water and 1,2-dichloroethane (DCE), is required due to the insolubility of Ag-DEDC in water. Added silver partitions into Ag+ and Ag-SL in the aqueous phase and into Ag-DEDC in the DCE phase. An automated system is used to add aliquots of silver and measure Ag-DEDC by UV absorbance in the DCE phase and [Ag+] by specific ion electrode in the aqueous phase. Excess addition of silver and a "Gran's" analysis gives the binding capacity of SL. The stability constant can also be determined for each addition of silver for an overall one-site SL assumption. Cysteine was used to test the method, and urban waters revealed SL capacities from about 50 to 150 nM and log K'(Ag) of 11-12. An independent analysis of chromium-reducible sulfide correlates well with the SL capacity.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".