Cationic surfactant influence on ternary complexes of CoII, NiII, CuII and ZnII ions with aspartic and citric acids
Bibliographic record
Abstract
A potentiometric titration study has been undertaken to determine the cationic surfactant influence on mixed ligand complexes in ternary systems consisting of cobalt (II), nickel (II), copper (II), and zinc (II) with aspartic and citric acids at various concentrations (0.0–2.5% w/v) of the CTAB (Cetyltrimethyl ammonium bromide)-surfactant solution while maintaining an ionic strength of 0.16 mol L-1 (NaCl) at 30 °C. Titrations were performed in the presence of different ratios (M:L:X = 1:2:2, 1:4:2, and 1:2:4) of metal (M) and aspartic acid (L) to citric acid (X) using sodium hydroxide. Model systems were developed based on statistical parameters and residual analysis. For Co(II), Ni(II), Cu(II), and Zn(II), the species detected were MLXH2, MLX2H5, and MLX3. The electrostatic relationship of the ligands' side chains, charge neutralization, chelate effect, stacking interactions, and hydrogen bonding are used to explain the trend in the variation of logarithm of stability constants (log β) values with changing dielectric constant and mole fraction of the surfactant. The variation in species distribution as a function of pH and surfactant composition is also presented and discussed, as are plausible equilibria for the formation of the species and structures of the ternary complexes are also presented and discussed.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".