Effect of static magnetic fields on the growth, photosynthesis and ultrastructure of <i>Chlorella kessleri</i> microalgae
Bibliographic record
Abstract
Microalgal biotechnology could generate substantial amounts of biofuels with minimal environmental impact if the economics can be improved by increasing the rate of biomass production. Chlorella kessleri was grown in a small-scale raceway pond and in flask cultures with the entire volume, 1% (v/v) at any instant, periodically exposed to static magnetic fields to demonstrate increased biomass production and investigate physiological changes, respectively. The growth rate in flasks was maximal at a field strength of 10 mT, increasing from 0.39 ± 0.06 per day for the control to 0.88 ± 0.06 per day. In the raceway pond the 10 mT field increased the growth rate from 0.24 ± 0.03 to 0.45 ± 0.05 per day, final biomass from 0.88 ± 0.11 to 1.56 ± 0.18 g/L per day, and maximum biomass production from 0.11 ± 0.02 to 0.38 ± 0.04 g/L per day. Increased pigment, protein, Ca, and Zn content made the biomass produced with magnetic stimulation nutritionally superior. An increase in oxidative stress was measured indirectly as a decrease in antioxidant capacity from 26 ± 2 to 17 ± 1 µmol antioxidant/g biomass. Net photosynthetic capacity (NPC) and respiratory rate were increased by factors of 2.1 and 3.1, respectively. Loss of NPC enhancement after the removal of magnetic field fit a first-order model well (R(2) = 0.99) with a half-life of 3.3 days. Transmission electron microscopy showed enlarged chloroplasts and decreased thylakoid order with 10 mT treatment. By increasing daily biomass production about fourfold, 10 mT magnetic field exposure could make algal oil cost competitive with other biodiesel feedstocks.
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".