Algal Cell Response to Pulsed Waved Stimulation and Its Application to Increase Algal Lipid Production
Bibliographic record
Abstract
using algae has recently attracted significant research attention, mostly directing towards biological methods such as systems biology, genetic engineering and bio-refining for optimizing algae strains. Other approaches focus on chemical screening to adjust culture conditions or culture media. We report for the first time the physiological changes of algal cells in response to a novel form of mechanical stimulation, or a pulsed wave at the frequency of 1.5 MHz and the duty cycle of 20%. We studied how the pulsed wave can further increase algal lipid production on top of existing biological and chemical methods. Two commonly used algal strains, fresh-water Chlorella vulgaris and seawater Tetraselmis chuii, were selected. We have performed the tests in shake flasks and 1 L spinner-flask bioreactors. Conventional Gravimetric measurements show that up to 20% increase for algal lipid could be achieved after 8 days of stimulation. The total electricity cost needed for the stimulations in a one-liter bioreactor is only one-tenth of a US penny. Gas liquid chromatography shows that the fatty acid composition remains unchanged after pulsed-wave stimulation. Scanning electron microscope results also suggest that pulsed wave stimulation induces shear stress and thus increases algal lipid production.
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".