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Record W1992669310 · doi:10.1039/b615298h

The permeation liquid membrane as a sensor for free nickel in aqueous samples

2007· article· en· W1992669310 on OpenAlexaff
Stéphane Bayen, Kevin J. Wilkinson, Jacques Buffle

Bibliographic record

VenueThe Analyst · 2007
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsUniversité de Montréal
FundersMerck Sharp and DohmeEuropean Commission
KeywordsChemistryAqueous solutionPermeationNickelMembranePhosphoric acidHydrolysisAqueous two-phase systemCrown etherInorganic chemistryEtherSolubilityNuclear chemistryChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

There are currently a limited number of techniques to study nickel speciation in aqueous samples. This work reports on the use of the permeation liquid membrane (PLM) for that objective. In this paper, the composition of the organic phase was studied to maximize the Ni flux (thus the sensitivity of the device) over a wide Ni2+ concentration range (50 nM to 100 microM) in order to verify its ability to determine free Ni2+ in the presence of Ni complexes. A mixture containing 1,10-didecyl-1,10-diaza-18-crown-6 ether (22DD) and di(2-ethylhexyl)phosphoric acid (D2EHPA) in toluene/phenylhexane was selected as the optimized organic phase for the PLM. The PLM was shown to be a reliable tool to measure free nickel concentrations down to 10(-7) M. The effect of pH on Ni transport was also studied. Fluxes below pH 6 were reduced significantly, i.e. an order of magnitude smaller than fluxes above pH 7.8. Finally, as part of a broader study examining the ability of trace metals to induce antibiotic resistance in bacteria, we used the PLM to quantify the formation, at pH = 7.2, of a weak complex between Ni and Imipenem (a member of the carbapenem class of beta-lactam antibiotics) or its hydrolysis product(s).

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 score0.053
Threshold uncertainty score0.224

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 head0.013
GPT teacher head0.268
Teacher spread0.255 · 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

Citations22
Published2007
Admission routes1
Has abstractyes

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