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Record W3173898721 · doi:10.1139/cjc-2021-0119

Development of calix[4]arenes modified at their narrow- and wide-rims as potential metal ions sensor layers for microcantilever sensors: further studies

2021· article· en· W3173898721 on OpenAlexaffvenue
Paris E. Georghiou, Shofiur Rahman, Yousif Assiri, Gopi Kishore Valluru, Melita Menelaou, Abdullah N. Alodhayb, Mona Braim, Luc Beaulieu

Bibliographic record

VenueCanadian Journal of Chemistry · 2021
Typearticle
Languageen
FieldChemistry
TopicMolecular Sensors and Ion Detection
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsChemistryCounterionMetal ions in aqueous solutionIonMetalAqueous solutionMonolayerIonic bondingCalixareneAlkoxy groupMoleculeSelf-assembled monolayerInorganic chemistryCombinatorial chemistryNanotechnologyOrganic chemistryAlkylMaterials science

Abstract

fetched live from OpenAlex

The development of a microcantilever (MCL) sensing device capable of simultaneously detecting several metal ionic species in aqueous media with low limits of detection requires a variety of sensing layers that are ion specific. Calix[4]arenes are robust molecules that can be easily modified and have been extensively studied for their ion binding properties. They are also capable of forming self-assembled monolayers (SAMs) on the gold layers of MCLs and are capable of detecting various metal ions with different anionic counterions in aqueous solutions. In this paper, we report on the effect of the alkoxy group in the narrow rim [O-(alkoxycarbonyl)methoxy] substituents of bimodal calix[4]arenes, which have been used as metal ion MCL sensing layers, using classical solution state experimental studies. A DFT computational study to compare the experimental results with several metal ions is also reported herein.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.020
GPT teacher head0.232
Teacher spread0.212 · 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

Citations3
Published2021
Admission routes2
Has abstractyes

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