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Record W3191054057 · doi:10.1115/1.4052053

Rheological and Biophysical Properties of Living Fluids Under Shear: Active Suspensions of<i>Synechocystis</i>sp. CPCC 534

2021· article· en· W3191054057 on OpenAlexaff
Malihe Mehdizadeh Allaf, Zahra Habib, John R. de Bruyn, Christopher T. DeGroot, Hassan Peerhossaini

Bibliographic record

VenueJournal of Fluids Engineering · 2021
Typearticle
Languageen
FieldEnergy
TopicAlgal biology and biofuel production
Canadian institutionsWestern University
Fundersnot available
KeywordsRheologySynechocystisSuspension (topology)BioreactorShear stressShear (geology)ViscosityChemical engineeringMaterials scienceNewtonian fluidCyanobacteriaChemistryComposite materialThermodynamicsGeologyOrganic chemistryBacteria

Abstract

fetched live from OpenAlex

Abstract In this study, we focus on the response of biological, rheological, and physical properties of dilute suspensions of cyanobacterium Synechocystis sp. CPCC 534 to shear induced by stirring. Experiments were carried out at three different stirring rates in well-controlled conditions, and the results are compared with stationary conditions where only molecular diffusion and cell motility govern the transport phenomena and cell growth. Our results show that the growth, biomass, total chlorophyll, and carotenoid production of Synechocystis sp. under various shear conditions were improved significantly, and the yield was nearly doubled. The viscosity of Synechocystis suspensions, subjected to different shear rates, was also measured. The data showed Newtonian behavior for suspensions at different cell concentrations. Cell concentration showed a noticeable increase in the viscosity of suspensions. However, we observed that this increase was smaller than the one predicted for a suspension of hard spheres. Addition of shear to the cyanobacterium Synechocystis sp. culture demonstrated a positive impact on the production of value-added products from the micro-organism. The obtained results can be used to improve the bioreactor design for better productivity.

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)0.001
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 score0.020
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.016
GPT teacher head0.213
Teacher spread0.197 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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