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Record W4319864262 · doi:10.5267/j.ccl.2022.12.009

Cationic surfactant influence on ternary complexes of CoII, NiII, CuII and ZnII ions with aspartic and citric acids

2023· article· en· W4319864262 on OpenAlexvenueno aff
Srinivasa Rao Pedada, Nageswara Rao Gollapalli

Bibliographic record

VenueCurrent Chemistry Letters · 2023
Typearticle
Languageen
FieldChemistry
TopicSurfactants and Colloidal Systems
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryInorganic chemistryPulmonary surfactantStability constants of complexesPotentiometric titrationIonic strengthHydrogen bondCationic polymerizationTitrationCitric acidTernary operationAqueous solutionOrganic chemistryMoleculeIon

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.818

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.235
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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