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Record W2780970939 · doi:10.1186/s13068-017-0987-6

Bioflocculants’ production in a biomass-degrading bacterium using untreated corn stover as carbon source and use of bioflocculants for microalgae harvest

2017· article· en· W2780970939 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBiotechnology for Biofuels · 2017
Typearticle
Languageen
FieldEnergy
TopicAlgal biology and biofuel production
Canadian institutionsLakehead University
FundersHubei UniversityChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaHubei University of TechnologyUniversity of Ottawa
KeywordsCorn stoverBiomass (ecology)Carbon sourceStoverBiotechnologyBioenergyCarbon fibersProduction (economics)Pulp and paper industryAgronomyBiofuelEnvironmental scienceBiologyMathematicsEngineeringField experiment

Abstract

fetched live from OpenAlex

Bioflocculation has been developed as a cost-effective and environment-friendly method to harvest multiple microalgae. However, the high production cost of bioflocculants makes it difficult to scale up. In the current study, low-cost bioflocculants were produced from untreated corn stover by a biomass-degrading bacterium Pseudomonas sp. GO2. Pseudomonas sp. GO2 showed excellent production ability of bioflocculants through directly hydrolyzing various biomasses. The untreated corn stover was selected as carbon source for bioflocculants’ production due to its highest flocculating efficiency compared to that when using other biomasses as carbon source. The effects of fermentation parameters on bioflocculants’ production were optimized via response surface methodology. According to the optimal model, an ideal flocculating efficiency of 99.8% was obtained with the fermentation time of 130.46 h, initial pH of 7.46, and biomass content of 0.64%. The relative importance of carboxymethyl cellulase and xylanase accounted for 51.8% in the process of bioflocculants’ production by boosted regression tree analysis, further indicating that the bioflocculants were mainly from the hydrolysates of biomass. Biochemical analysis showed that it contained 59.0% polysaccharides with uronic acid (34.2%), 32.1% protein, and 6.1% nucleic acid in the bioflocculants, which had an average molecular weight as 1.33 × 106 Da. In addition, the bioflocculants showed the highest flocculating efficiency at a concentration of 12.5 mg L−1 and were stable over broad ranges of pH and temperature. The highest flocculating efficiencies obtained for Chlorella zofingiensis and Neochloris oleoabundans were 77.9 and 88.9%, respectively. The results indicated that Pseudomonas sp. GO2 can directly utilize various untreated lignocellulolytic biomasses to produce low-cost bioflocculants, which showed the high efficiency to harvest two green microalgae in a low GO2 fermentation broth/algal culture ratio.

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.

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 categoriesMeta-epidemiology (narrow), Research integrity
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 score0.044
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.045
GPT teacher head0.277
Teacher spread0.232 · 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