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Record W4399386161 · doi:10.1002/marc.202400232

Fabrication of Triblock Elastomer Foams and Gelation Studies for Oil Spill Remediation

2024· article· en· W4399386161 on OpenAlexafffund
Hye-Jin Lee, Binh M. Trinh, Tizazu H. Mekonnen

Bibliographic record

VenueMacromolecular Rapid Communications · 2024
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of Waterloo
FundersOntario Centre of Innovation
KeywordsMaterials sciencePorosityOil spillLeakage (economics)ElastomerPolymerEnvironmental remediationComposite materialPetroleum engineeringFabricationContaminationGeology

Abstract

fetched live from OpenAlex

Polymeric foamed materials are among the most widely utilized technologies for oil spill accidents and releases of oil-contaminated wastewater oil due to their porosity to absorb and separate oil/water effectively. However, a major limitation of traditional polymeric foams is their reliance on an ad/absorption mechanism as the sole method of oil capture, leading to potential oil leakage once their saturation point is exceeded. Tri-block polymer styrene-ethylene-butylene-styrene (SEBS) is a fascinating absorbent material that can bypass this limitation by both capturing oil and providing a sealing mechanism via gelation to prevent oil leakage due to its unique chemical structure. SEBS foams are produced via simultaneous crosslinking and foaming that results in an impressive expansion ratio of up to 15.2 with over 93% porosity. Most importantly, the SEBS foams show great potential as oil absorbents in spill remediation, demonstrating rapid and efficient oil absorption coupled with superhydrophobic properties. Moreover, the unique interaction between the oil and SEBS enables the formation of a physical gel, acting as an effective barrier against oil leakage. These findings indicate the potential for commercializing SEBS foam as a viable option for geotextiles to mitigate oil spill concerns from infrastructures.

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 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 score0.131
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.056
GPT teacher head0.327
Teacher spread0.271 · 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

Citations6
Published2024
Admission routes2
Has abstractyes

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