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Record W3199460513 · doi:10.1021/ic060517x

Potentiometric Titration of Metal Ions in Ethanol

2006· article· fr· W3199460513 on OpenAlexaff
Graham T. T. Gibson, Mark F. Mohamed, Alexei A. Neverov, R. S. Brown

Bibliographic record

VenueInorganic Chemistry · 2006
Typearticle
Languagefr
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsQueen's University
Fundersnot available
KeywordsPotentiometric titrationChemistryTitrationTitration curveDeprotonationGran plotInorganic chemistryStability constants of complexesConductometryMetal ions in aqueous solutionMetalPhysical chemistryIonOrganic chemistry

Abstract

fetched live from OpenAlex

The potentiometric titrations of Zn2+, Cu2+ and 12 Ln3+ metal ions were obtained in ethanol to determine the titration constants (defined as the at which the [-OEt]/[Mx+]t ratios are 0.5, 1.5, and 2.5) and in two cases (La3+ and Zn2+) a complete speciation diagram. Several simple monobasic acids and aminium ions were also titrated to test the validity of experimental titration measurements and to establish new constants in this medium that will be useful for the preparation of buffers and standard solutions. The dependence of the titration constants on the concentration and type of metal ion and specific counterion effects is discussed. In selected cases, the titration profiles were analyzed using a commercially available fitting program to obtain information about the species present in solution, including La3+ for which a dimer model is proposed. The fitting provides the microscopic values for deprotonation of one to four metal-bound ethanol molecules. Kinetics for the La3+-catalyzed ethanolysis of paraoxon as a function of are presented and analyzed in terms of La3+ speciation as determined by the analysis of potentiometric titration curves. The stability constants for the formation of Zn2+ and Cu2+ complexes with 1,5,9-triazacyclododecane as determined by potentiometric titration are presented.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.220
Teacher spread0.214 · 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.

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

Citations27
Published2006
Admission routes1
Has abstractyes

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