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Record W3184640248 · doi:10.3390/pr9081296

CO2-Derived Carbon Capture Using Microalgae and Sodium Bicarbonate in a PhotoBioCREC Unit: Kinetic Modeling

2021· article· en· W3184640248 on OpenAlexafffund
Maureen Cordoba-Perez, Hugo de Lasa

Bibliographic record

VenueProcesses · 2021
Typearticle
Languageen
FieldEnergy
TopicAlgal biology and biofuel production
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaUniversidad de Costa Rica
KeywordsChlorella vulgarisSodium bicarbonateBicarbonateChemistryCarbon fibersTotal inorganic carbonPhotosynthesisSodiumBiofuelBiomass (ecology)Carbon dioxideEnvironmental chemistryInorganic chemistryBotanyOrganic chemistryMaterials scienceAlgaeWaste managementEcologyBiochemistry

Abstract

fetched live from OpenAlex

By converting bicarbonates via Chlorella vulgaris photosynthesis, one can obtain valuable biofuel products and find a route toward carbon-derived fossil fuel conversion into renewable carbon. In this research, experiments were carried out in the PhotoBioCREC prototype under controlled radiation and high mixing conditions. Sodium bicarbonate (NaHCO3) was supplied as the inorganic carbon-containing species, at different concentrations, in the 18 to 60 mM range. Both the NaHCO3 concentrations and the organic carbon concentrations were quantified periodically during microalgae culture, with the pH being readjusted every day to the 7.00 level. It was found that sodium bicarbonate was converted with a selectivity up to 33.0% ± 2.0 by Chlorella vulgaris. It was also observed that the reaction rate constant for inorganic carbon conversion was 0.26 ± 0.09 day−1, while the maximum reaction rate constant for organic carbon formation was achieved with a 28 mM NaHCO3 concentration and displayed a 1.18 ± 0.05 mmole L−1day−1 value.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.032
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.026
GPT teacher head0.242
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2021
Admission routes2
Has abstractyes

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