Operation of Laboratory Photobioreactors with Online Growth Measurements and Customizable Light Regimes
Bibliographic record
Abstract
The laboratory study of microalgae can be experimentally challenging. In addition to the cultivation requirements of non-photosynthetic microorganisms, phototrophs also require illumination. Routinely, researchers seek to provide custom light supplies, i.e., vary the light intensity and time over which it is delivered. Such flexibility is difficult with standard benchtop lights. Usually, cultivation studies also require growth comparisons between experimental treatments. Frequently, growth is assessed over an extended duration, e.g., multiple times a day over a week-long trial. Manual measurements can be time-consuming and lack data resolution. Therefore, photobioreactors (PBRs) with automatic growth monitoring and customizable light supply are useful for replicated experiments with multiple treatments. The current work presents the design, construction, and operation of laboratory PBRs. The materials are easily sourced and relatively inexpensive. The design could be duplicated with moderate skill. Each structure has a footprint of ~40 cm2 and hosts three 1 L glass bottles for triplicate replication. Bottles rest upon platforms containing magnetic stirrers and are arranged vertically within a 1 m high and 15 cm diameter polyvinyl chloride (PVC) pipe. The pipe interior is lined with light-emitting diodes (LEDs). These LEDs produce continuous light intensities from 0–2400 µmol photons m-2 s-1 of photosynthetically active radiation (PAR). Users design a custom lighting program. The light intensity can be adjusted each second or held constant for longer durations. Oxygen produced from photosynthesis exits each bottle via a one-way volumetric gas sensor. Software is used to record gas sensor data. The amount of oxygen produced can be correlated to biomass growth. If biomass samples are required, a syringe can be used to extract culture. The method is suited for microalgae grown with bicarbonate as the carbon source. These PBRs are valuable to a laboratory that requires replicated experiments, light regime flexibility, and continuous high-resolution growth data.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".