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Record W3084268486 · doi:10.32393/csme.2020.14

Active Fluids in Environment

2020· article· en· W3084268486 on OpenAlexaff
Zahra Habibi, Malihe Mehdizadeh Allaf, Christopher T. DeGroot, Hassan Peerhossaini

Bibliographic record

VenueProgress in Canadian Mechanical Engineering. Volume 3 · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsWestern University
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Photosynthetic carbon capture by trees and other vegetation is believed to be among the most efficient and environmentally friendly strategies to reduce CO2 concentration in the earth atmosphere. The 2018 IPCC special report suggests that 1 billion hectares of new forest is necessary to limit global warming to 1.5 C by 2050. However, recent studies estimate that only 0.9 billion hectares of land are available on the earth outside of cropland and urban regions for potential forest restoration. Water availability can also be one of limitations of this strategy. Biofixation of carbon dioxide by aqueous suspensions of bacteria and algae (referred to as living or active fluids) is an alternative solution for limitation of CO2. Biofixation is 10-15 times more efficient than trees and produces biomass that has value in producing biofuels, human nutritional supplements, cosmetic products, biofertilizers and animal feed. These microorganisms are cultivated in dedicated reactors commonly referred to as photobioreactors (PBR). The physical phenomena occurring in PBR involve a sophisticated nonlinear interaction between the hydrodynamics of active fluids, biokinetic processes, radiative transfer as well as heat and mass transfer in microorganism suspensions. A high degree of success in the design and optimization of PBR therefore, relies on the proper understanding and modelling of physical properties and rheological behavior of cell suspensions. Active fluids, like suspensions of autonomous fluid particles, can show unexpected manifestations. In a recent study we showed the diffusion coefficient of cell suspensions on solid surfaces reduces with time; this reduction is directly related to the contact history between the cell and the surface. We also showed that this diffusion is a function of surface stiffness (durotaxis) and light intensity and direction (phototaxis). In our most recent study, we have focused on the new measurements of the physical properties, rheological behavior and biomass, lipid and pigment production of Synechocystis, an environmentally important model bacterium, under flow shear stress. This information is of primordial importance in hydrodynamical and radiative design of high performance photobioreactors.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.737
Threshold uncertainty score0.996

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)0.0010.000

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 head0.007
GPT teacher head0.194
Teacher spread0.187 · 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
Published2020
Admission routes1
Has abstractyes

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