MétaCan
Menu
Back to cohort
Record W4389921982 · doi:10.3389/frsfm.2023.1329058

Effect of sorbitan ester structure on the separation between tetrahydrofuran and water

2023· article· en· W4389921982 on OpenAlexaff
Bibiana Bartokova, Alejandro G. Marangoni, Thamara Laredo, Erica Pensini

Bibliographic record

VenueFrontiers in Soft Matter · 2023
Typearticle
Languageen
FieldChemistry
TopicZeolite Catalysis and Synthesis
Canadian institutionsLakehead UniversityUniversity of Guelph
Fundersnot available
KeywordsTetrahydrofuranSpan (engineering)ChemistryHydrogen bondSmall-angle X-ray scatteringAbsorbanceMoleculeCrystallographyAnalytical Chemistry (journal)ScatteringOrganic chemistryChromatographyOpticsSolvent

Abstract

fetched live from OpenAlex

This study separates tetrahydrofuran (THF)-water mixtures containing varying THF percentages, using sorbitan esters (Spans) with different tail characteristics. We probe the separation mechanisms using attenuated total reflectance-Fourier transform infrared spectroscopy and small angle X ray scattering (SAXS). THF and water are miscible and interact through hydrogen bonds. Water splits the COC absorbance band of THF into a peak at ≈1,070 cm −1 (crystalline THF) and a dominant peak at ≈1,050 cm −1 (glassy THF), indicating disorder. Depending on the Span, separation occurs for mixtures containing up to 70% THF (v/v, relative to water). Spans with unsaturated tails separate the lowest THF percentages. Tail length and number of Span tails enhances ordering of THF, and the crystalline THF peak at ≈1,070 cm −1 dominates. Spans interact with THF through hydrogen bonds, as reflected in the splitting of the COC band of THF. Furthermore, C-H…O hydrogen bonds cause a blueshift in the ν as (CH 2 ) band of Spans with increasing THF. This effect is most significant in Span 40 and 60, indicating that they interact with THF more strongly than Span 20, Span 80 and Span 85. In contrast, they interact with water less strongly than Span 20, Span 80 and Span 85, as indicated by their flocculation at low THF percentages. Therefore, we propose that separation between THF and water occurs primarily through two mechanisms: 1) Span 20, Span 80 and Span 85 compete against THF for interactions with water through their hydrophilic head, and 2) Span 40 and Span 60 preferentially interact with THF through their tails. Nonetheless, water also interacts with the heads of Span 40 and Span 60, as indicated by SAXS. SAXS shows that in THF Spans self-assemble into micelles, which aggregate into either surface fractals or mass fractals. There are two persistence lengths because of the limited order in THF. Water orders self-assembled structures, likely by favoring the formation of micelles which host water in their interior. Therefore, we identify a single persistence length (≈25 Å), representative of the distance between the micelle centers.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.888
Threshold uncertainty score0.925

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)0.0010.000

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 head0.006
GPT teacher head0.233
Teacher spread0.227 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in Soft MatterSame topicZeolite Catalysis and SynthesisFrench-language works237,207