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Record W4388701093 · doi:10.1080/08927014.2023.2280005

Surface properties of membrane materials and their role in cell adhesion and biofilm formation of microalgae

2023· article· en· W4388701093 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBiofouling · 2023
Typearticle
Languageen
FieldEnergy
TopicAlgal biology and biofuel production
Canadian institutionsLakehead University
FundersCanada Foundation for Innovation
KeywordsBiofilmMembraneAdhesionZeta potentialSurface roughnessChemical engineeringSurface energySurface chargeChlorella vulgarisContact angleBiophysicsChemistryBiofoulingMaterials scienceNanotechnologyBiologyBotanyComposite materialBiochemistryAlgaeBacteriaOrganic chemistry

Abstract

fetched live from OpenAlex

In this study, the effects of surface properties of membrane materials on microalgae cell adhesion and biofilm formation were investigated using Chlorella vulgaris and five different types of membrane materials under hydrodynamic conditions. The results suggest that the contact angle (hydrophobicity), surface free energy, and free energy of cohesion of membrane materials alone could not sufficiently elucidate the selectivity of microalgae cell adhesion and biofilm formation on membrane materials surfaces, and membrane surface roughness played a dominant role in controlling biofilm formation rate, under tested hydrodynamic conditions. A lower level of biofilm EPS production was generally associated with a larger amount of biofilm formation. The zeta potential of membrane materials could enhance initial microalgae cell adhesion and biofilm formation through salt bridging or charge neutralization mechanisms.

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.207

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)0.0000.000

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 head0.022
GPT teacher head0.209
Teacher spread0.188 · 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