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Record W2962783569 · doi:10.1038/s41598-019-47096-6

Investigating fouling at the pore-scale using a microfluidic membrane mimic filtration system

2019· article· en· W2962783569 on OpenAlexafffund
Nandini Debnath, Aloke Kumar, Thomas Thundat, Mohtada Sadrzadeh

Bibliographic record

VenueScientific Reports · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaDepartment of Science and Technology, Ministry of Science and Technology, India
KeywordsFoulingMembrane foulingFiltration (mathematics)MembraneChemical engineeringColloidMicrofluidicsPolystyreneMaterials scienceParticle (ecology)MicrofiltrationChromatographyPolymerChemistryNanotechnologyComposite material

Abstract

fetched live from OpenAlex

The work investigates fouling in a microfluidic membrane mimic (MMM) filtration system for foulants such as polystyrene particles and large polymeric molecules. Our MMM device consists of a staggered arrangement of pillars which enables real-time visualization and analysis of pore-scale phenomena. Different fouling scenarios are investigated by conducting constant-pressure experiments. Fouling experiments are performed with three different types of foulants: polystyrene particle solution (colloidal fouling), polyacrylamide polymer solution (organic fouling) and a mixture of these two solutions (combined fouling). Four major categories of microscopic fouling are observed: cake filtration (upstream), pore blocking (inside the pores), colloidal aggregation (downstream) and colloidal streamer fouling (downstream). Our microfluidic experiments show that downstream colloidal aggregation and streamer fouling have a significant effect on overall membrane fouling which were not studied before.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.019
GPT teacher head0.235
Teacher spread0.217 · 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

Citations35
Published2019
Admission routes2
Has abstractyes

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