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Record W2770432152 · doi:10.1039/c7an01426k

Monolithic capillary columns consisting of poly(glycidyl methacrylate-<i>co</i>-ethylene glycol dimethacrylate) and their diol derivatives with incorporated hydroxyl functionalized multiwalled carbon nanotubes for reversed-phase capillary electrochromatography

2017· article· en· W2770432152 on OpenAlexaff
Nisansala Ganewatta, Ziad El Rassi

Bibliographic record

VenueThe Analyst · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsStillwater (Canada)
Fundersnot available
KeywordsMonolithGlycidyl methacrylateEthylene glycol dimethacrylateEthylene glycolDiolMethacrylateCarbon nanotubeMonolithic HPLC columnChemistryChemical engineeringSonicationMaterials scienceMethacrylic acidPolymer chemistryChromatographyOrganic chemistryPolymerizationCatalysisPolymerHigh-performance liquid chromatographyNanotechnology

Abstract

fetched live from OpenAlex

in order to react some of the OH-MWCNTs with the epoxy rings of the poly(GMA-co-EDMA) monolith. In all cases, the OH-MWCNTs were subjected to high power sonication at an output power of 10 W for 15 min with the aim of better dispersing the incorporated nanotubes into the monoliths under investigation. In fact, high power sonication yielded columns with a relatively higher plate count (∼2 fold increase) when compared to low power sonication. While the incorporation of OH-MWCNTs into the poly(GMA-co-EDMA) monolith acted as an amendment boosting the nonpolar character of the monolith and providing additional π-π interactions, the diol derivative monolith with its polar backbone character acted nearly as a support for the OH-MWCNT stationary phase giving rise to a carbon nanotube sorbent providing hydrophobic and π-π interactions via the incorporated OH-MWCNTs. These two kinds of columns were evaluated using alkylbenzenes, toluene derivatives, aniline compounds, phenols and polyaromatic hydrocarbons.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.041
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.012
GPT teacher head0.243
Teacher spread0.230 · 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

Citations20
Published2017
Admission routes1
Has abstractyes

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