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Record W2913333983 · doi:10.5539/ijc.v11n1p1

Application of Molar Volumes Obtained From a Solvent Extraction Method for Estimation of Sizes of Metal Complexes With Crown Ethers in Phases

2019· article· en· W2913333983 on OpenAlexvenueno aff
Yoshihiro Kudo

Bibliographic record

VenueInternational Journal of Chemistry · 2019
Typearticle
Languageen
FieldChemistry
TopicCrystallography and molecular interactions
Canadian institutionsnot available
FundersChiba University
KeywordsChemistryPicrate18-Crown-6Metal ions in aqueous solutionIonAnalytical Chemistry (journal)NitrobenzeneCrystallographyStereochemistryChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

Molar volumes (Vj) of ion-pair complexes (MLAz = j; z = 1, 2) and simple complex ions (CdL2+) without a pairing anion A− obtained at 298 K from the plots of the regular solution theory and the geometrical relation Vj = (4π/3)Rj3 were used for an estimation of their apparent radii (Rj) in liquid phases. Here, the cases for Mz+ = Li+-Cs+, Cd2+, and Pb2+ and L = 18-crown-6 ether (18C6) and benzo-18C6 (B18C6) with A− = Cl−-I− and picrate ion (Pic−) were examined. The thus-estimated Rj values were related to h(ML+), λmax, and (r+ + r−) values, where these symbols refer to the number of water molecules attached to the univalent complex ions, ML+, extracted into water-saturated nitrobenzene, absorption maxima of Pic− with ML+ in water-saturated chloroform, and the sum in an effective ionic radius, r+ or r−, between the central Mz+ and the pairing A−, respectively. The data points of j = Li(18C6)Pic, Na(18C6)Pic, M(B18C6)Pic with M = Na-Rb(I), Cd18C62+, Cd(18C6)Cl2, Cd(18C6)Br2, and Cd(18C6)I2 showed larger deviations from the line of Rj = (r+ + r−). While j = M(18C6)Pic with M = K-Cs(I) and M(18C6)Pic2 with Cd(II) and Pb(II) were on the line. It was demonstrated that essentially the former groups are present as water-separated and -shared ion pairs in the phases, while the latter ones as contact pairs.

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 score0.095
Threshold uncertainty score0.370

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 head0.009
GPT teacher head0.312
Teacher spread0.303 · 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
Published2019
Admission routes1
Has abstractyes

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