Periodic harvesting of microalgae from calcium alginate hydrogels for sustained high‐density production
Bibliographic record
Abstract
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 (109 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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".