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Record W4389540963 · doi:10.17118/11143/20970

Flow behavior of active fluids in a bifurcating microchannel

2023· article· en· W4389540963 on OpenAlexaff
Zahra Samadi, Reza Saifi, Malihe Mehdizadeh-Allaf, Christopher T. DeGroot, Hassan Peerhossaini

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsMicrochannelFlow (mathematics)MechanicsComputer scienceMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Abstract: Suspensions of photosynthetic microorganisms as “living”, or “active” fluids play a crucial role in many biological, medical, and engineering applications. The properties of active fluids are fundamentally different from passive fluids. In passive fluids, the gradients of pressure, velocity, or temperature are driving forces for the flow, while in active fluids, such as bacterial suspensions, chemicals and/or light trigger cellular molecular motors to accomplish metabolic functions and navigate in the media, leading to a directed movement. Therefore, microorganisms in active fluids can create complicated and spontaneous fluid motions in the absence of external gradients. However, the presence of an external gradient may alter these spontaneous motions, a topic that requires further investigation. Therefore, in this study, Synechocystis sp., a unicellular species of cyanobacteria, was suspended in a fluid medium (BG11) to constitute the working active fluid. In order to investigate various delicate flow features of this active fluid, a bifurcating microchannel with a rectangular cross-section was designed and fabricated using Polydimethylsiloxane (PDMS). A micro-pump was used to drive the fluid at a specific flow rate into the microchannel. A high-magnification inverted optical video microscope captured the cells’ behavior at a channel plane. The recorded video images were processed using a particle-imagevelocimetry algorithm available in PIVlab to obtain flow patterns for both fluids: the live and dead Synechocystis sp.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.289

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 head0.025
GPT teacher head0.269
Teacher spread0.243 · 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 designSimulation or modeling
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

Citations0
Published2023
Admission routes1
Has abstractyes

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