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Record W2765205265 · doi:10.1093/femsle/fnx218

Biofilm-based photobioreactors: their design and improving productivity through efficient supply of dissolved inorganic carbon

2017· review· en· W2765205265 on OpenAlexaff
Tong Li, Marc Strous, Michael Melkonian

Bibliographic record

VenueFEMS Microbiology Letters · 2017
Typereview
Languageen
FieldEnergy
TopicAlgal biology and biofuel production
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBiofilmPhotobioreactorBiomass (ecology)Suspension (topology)Dissolved organic carbonBiochemical engineeringSuspension cultureCarbon fibersChemistryEnvironmental scienceChemical engineeringProcess engineeringComputer scienceEnvironmental chemistryBacteriaCell cultureEcologyBiologyEngineeringMathematics

Abstract

fetched live from OpenAlex

The potential of biofilm-based photobioreactors (PBRs) for various applications has long been recognized, and various types of biofilm-based PBRs have been developed for different applications. Compared to suspension-based PBR reactors, biofilm-based systems offer several advantages, including a significantly higher biomass concentration. However, due to the immobilization of the cells, in contrast to suspension-based systems, dissolved inorganic carbon (DIC) has to be transferred into the biofilm for consumption. Thus, to ensure efficient operation of these systems under a given lighting scheme (e.g. depending on geographical location), availability of DIC should be optimized. To achieve this, the dynamics of DIC inside the various biofilm-based PBRs, as well as the operational principles of these PBRs, need to be understood. The mini-review summarizes the designs of existing biofilm-based PBRs and reviews previous studies on DIC dynamics in various biofilms. Strategies to enhance DIC availability for the immobilized cells in biofilm-based PBRs are also discussed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.798
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.043
GPT teacher head0.265
Teacher spread0.222 · 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
GenreReview

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

Citations55
Published2017
Admission routes1
Has abstractyes

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