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Record W2610215044 · doi:10.1002/bit.26325

Periodic harvesting of microalgae from calcium alginate hydrogels for sustained high‐density production

2017· article· en· W2610215044 on OpenAlexafffund
Scott C. Pierobon, Jason Riordon, Brian Nguyen, Matthew D. Ooms, David Sinton

Bibliographic record

VenueBiotechnology and Bioengineering · 2017
Typearticle
Languageen
FieldEnergy
TopicAlgal biology and biofuel production
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsPhotobioreactorBiomass (ecology)Calcium alginateCalciumBiofilmPulp and paper industryProductivityBioreactorChemistryCalcium carbonatePenetration (warfare)BotanyChemical engineeringBiophysicsEnvironmental scienceBiologyAgronomyBacteria

Abstract

fetched live from OpenAlex

ABSTRACT High‐density biomass production is currently only realized in biofilm‐based photobioreactors. Harvest yields of whole biofilms are self‐limited by daughter‐upon‐parent cell growth that hinders light and leads to respiratory biomass losses. In this work, we demonstrate a sustainable multi‐harvest approach for prolonged generation of high‐density biomass. Calcium‐alginate hydrogel cultures loaded with Synechococcus elongatus PCC 7942 achieved production densities comparable to that of biofilms (10 9 cells/mL) and optimal total productivity in harvest periods of 2 or 3 days that allowed high‐density surface growth without self‐limiting cell buildup or surface death. Cross‐linking calcium concentration had a strong influence on surface growth and harvest yields, especially in the first harvests. Subsequent harvests achieved more uniform biomass yields and distributions, unaffected by bulk respiration or light penetration. Collectively, these results demonstrate the feasibility of sustained, high‐density biomass production by periodic harvesting within microalgal hydrogel cultures. Biotechnol. Bioeng. 2017;114: 2023–2031. © 2017 Wiley Periodicals, Inc.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.630

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 integrity0.0010.000
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 head0.015
GPT teacher head0.230
Teacher spread0.215 · 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

Citations16
Published2017
Admission routes2
Has abstractyes

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