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Microalgal TAG production strategies: why batch beats repeated-batch

Overview of attention for article published in Biotechnology for Biofuels and Bioproducts, March 2016
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Title
Microalgal TAG production strategies: why batch beats repeated-batch
Published in
Biotechnology for Biofuels and Bioproducts, March 2016
DOI 10.1186/s13068-016-0475-4
Pubmed ID
Authors

Giulia Benvenuti, Packo P. Lamers, Guido Breuer, Rouke Bosma, Ana Cerar, René H. Wijffels, Maria J. Barbosa

Abstract

For a commercially feasible microalgal triglyceride (TAG) production, high TAG productivities are required. The operational strategy affects TAG productivity but a systematic comparison between different strategies is lacking. For this, physiological responses of Nannochloropsis sp. to nitrogen (N) starvation and N-rich medium replenishment were studied in lab-scale batch and repeated-batch (part of the culture is periodically harvested and N-rich medium is re-supplied) cultivations under continuous light, and condensed into a mechanistic model. The model, which successfully described both strategies, was used to identify potential improvements for both batch and repeated-batch and compare the two strategies on optimized TAG yields on light (amount of TAGs produced per mol of supplied PAR photons). TAG yields on light, for batch, from 0.12 (base case at high light) to 0.49 g molph (-1) (at low light and with improved strain) and, for repeated-batch, from 0.07 (base case at high light) to 0.39 g molph (-1) (at low light with improved strain and optimized repeated-batch settings). The base case yields are in line with the yields observed in current state-of-the-art outdoor TAG production. For continuous light, an optimized batch process will always result in higher TAG yield on light compared to an optimized repeated-batch process. This is mainly because repeated-batch cycles start with N-starved cells. Their reduced photosynthetic capacity leads to inefficient light use during the regrowth phase which results in lower overall TAG yields compared to a batch process.

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Geographical breakdown

Country Count As %
Netherlands 1 2%
Italy 1 2%
Austria 1 2%
India 1 2%
China 1 2%
Unknown 60 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 18%
Researcher 11 17%
Student > Master 11 17%
Student > Bachelor 8 12%
Other 4 6%
Other 12 18%
Unknown 7 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 38%
Biochemistry, Genetics and Molecular Biology 10 15%
Chemical Engineering 7 11%
Environmental Science 3 5%
Immunology and Microbiology 3 5%
Other 7 11%
Unknown 10 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 March 2016.
All research outputs
#19,944,994
of 25,374,647 outputs
Outputs from Biotechnology for Biofuels and Bioproducts
#1,254
of 1,578 outputs
Outputs of similar age
#218,989
of 314,938 outputs
Outputs of similar age from Biotechnology for Biofuels and Bioproducts
#41
of 50 outputs
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So far Altmetric has tracked 1,578 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
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