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Record W2106803106 · doi:10.1155/2013/719179

Interference of Lithium in Measuring Magnesium by Complexometry: Discussions of the Mechanism

2013· article· en· W2106803106 on OpenAlexaff
Jie Gao, Yafei Guo, Shiqiang Wang, Tianlong Deng, Yuwei Chen, Nelson Belzile

Bibliographic record

VenueJournal of Chemistry · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNMR spectroscopy and applications
Canadian institutionsLaurentian University
FundersTianjin University of Science and TechnologyNational Natural Science Foundation of China
KeywordsChemistryMagnesiumEthylenediaminetetraacetic acidAnhydrousInorganic chemistryBrineComplexometric titrationSalt (chemistry)TitrationAlcoholChelationOrganic chemistry

Abstract

fetched live from OpenAlex

There is usually a large concentration of magnesium in the brine of salt lakes and as a consequence it is difficult to measure Mg2+ accurately with the complexometric titration by ethylenediaminetetraacetic acid disodium salt (EDTA) because of the large levels of Li+ also coexisting in brine samples. In this study, a relationship between the relative error related to the number of drops of the indicator and the NMR spectra of anhydrous ethanol and n‐butanol, with or without LiCl, was analyzed and compared. It was then possible to suggest a mechanism to explain the interference of Li+ when measuring Mg2+ and to control the interference in the alcohol mixture because Li+ can then complex with the alcohols and mainly form a complex with n‐butanol.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.003
Open science0.0030.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.009
GPT teacher head0.265
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2013
Admission routes1
Has abstractyes

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