Modelling and Performance of a Hydrogel-Based Photobioreactor
Bibliographic record
Abstract
This work is motivated by the need for in situ food production with respect to future \nspace activities due to the technical and economic in-feasibility of long-term earth-based \nresupply. The unique size constraints of space have prevented conventional food systems \nfrom demonstrating feasibility. Owing to their high growth rates and phototropic activity, \nmicroalgae are a promising candidate to meet the caloric and respiratory needs of astronauts as part of a biological life support systems (BLSS). However, the gravity dependence \nand size of transitional photobioreactors poses a challenged to their utilization in space. \nAs such, a solid-state hydrogel-based photobioreactor (hPBR) is proposed to achieve inherent phase separation allowing for extra-terrestial use. Initially proposed for the Canadian Space Agency (CSA) Deep Space Food Challenge (DSFC) (Design A), this design \nwas further iterated to improve productivity and reactor performance (Design B). Using \nChlorella vulgaris, Design B achieved a biomass productivity of 2.4 and 3.2 g m−2d \n−1 when \nusing physically (pPVA) and chemically (cPVA) crosslinked poly(vinyl) alcohol (PVA) respectively with a water demand of 0.44 kg g−1 biomass. Over 23 days of growth, the lipid \ncontent increased from 18.9% to 56.6% and 13.8% to 43.2% for pPVA and cPVA respectively, and the chlorophyll content also decreased. However, cell viability remained high \nat over 97% and surface coverage analysis showed good coverage within a few days. \nObservations made with the prototype suggested mass transport limitations were impacting growth, and that poor humidity control led to the hydrogels drying out. To this \nend, a continuum model of the hydrogel was proposed to better understand mass transfer \nand to inform future design iterations. Hydrogels are two phase systems where the polymer \nis fixed due to crosslinking leading to a moving boundary with changes in water content. \nThe proposed model did not require any parameter fitting as values were determined with \nindependent experiments. The model enabled the prediction of the transient material response to changing relative humidity. This helped to explain why humidity control was \ncritical in maintaining cell viability. Humidity impacted the water content of the gel’s \nsurface which needed to be high enough to support algae growth. Using the steady-state \nsolution to the model, the solute transport through the system was also modelled. The solute profile suggested that nutrient concentrations throughout the hydrogel were similar to \nthat in the media tank. This suggests nutrient supply was not the cause of the diminishing \nbiomass quality and that other factors such as photo-inhibition, and mechanical stresses \nfrom solid-state cultivation may be issues to address in future work.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".