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Record W3186221454 · doi:10.1021/acs.langmuir.1c01493

Algae Adhesion onto Silicone is Sensitive to Environment-Induced Surface Restructuring

2021· article· en· W3186221454 on OpenAlexafffund
Zhijing Wan, Md. Shafiul Azam, Shea N. Wyatt, Kaitlyn Ramsay, Jaime L. Korner, Katherine S. Elvira, Rajkumar Padmawar, Diana E. Varela, Dennis K. Hore

Bibliographic record

VenueLangmuir · 2021
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolydimethylsiloxanePolymerSiliconeAdhesionMaterials scienceSurface roughnessChemical engineeringSurface finishAlgaeComposite materialChemistryBotany

Abstract

fetched live from OpenAlex

Resistance to algae contamination is an important characteristic of insulators used in overhead power distribution in coastal environments. It is therefore important to understand the parameters governing algae adhesion onto polymer insulator materials such as silicone. Flow cell-based shear experiments were conducted in order to characterize the adhesion strength of algae onto polydimethylsiloxane surfaces, comparing fresh polymer substrates with those that have been soaked in water and saline solutions for 1 month. Both freshwater algae and seawater species could withstand considerably less drag force and were therefore more easily removed when the polymer was soaked in salt water. The polymer surface was found to be unaltered in terms of its roughness, contact angle, and lack of water uptake; no macroscopic surface characterization was therefore able to account for the differences in cell adhesion strength resulting from the soaking treatment. Surface-specific nonlinear vibrational spectroscopy, however, revealed subtle differences in the orientation of surface methyl groups that resulted from the water and saline exposure.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.028
Threshold uncertainty score0.999

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)0.0020.002

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 head0.026
GPT teacher head0.247
Teacher spread0.221 · 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; both teacher heads agree on what is shown here.

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

Citations10
Published2021
Admission routes2
Has abstractyes

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