Effects of activated carbon on the growth of Chlorella vulgaris in an aqueous solution
Bibliographic record
Abstract
Algal blooms, if left unmanaged, can negatively impact lake ecosystems. An unexplored method of removing excess nutrients from lakes, and therefore reducing algal blooms, is through the use of biochar. We hypothesize that due to the adsorptive characteristics of pyrolyzed material such as biochar and activated carbon, its presence would reduce the nutrient availability within aqueous solutions, therefore reducing algal growth. This experiment was conducted in an aqueous solution containing COMBO growth medium with and without the presence of activated carbon, studied under four conditions: 5 mg/L, 10 mg/L, 20 mg/L, and 50 mg/L phosphorous. We applied these treatments to an aqueous solution containing algae and measured fluorometer readings of the algae growth over a period of 12 days. An analysis of covariance followed by a Tukey’s HSD test demonstrated a significant difference between the means of samples containing activated carbon compared to samples without (p < 0.0001). Further, nutrient readings taken of each sample demonstrate a lower concentration of both phosphorus and nitrogen in samples containing activated carbon compared to those without. Our study demonstrates that activated carbon has the capacity to be used for the adsorption of phosphorous. This suggests that both activated carbon, as well as its more adsorptive counterpart, biochar, have the potential to be used in mitigating algal blooms and, more importantly, reducing the effects of anthropogenic eutrophication in aqueous environments.
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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 | 0.000 |
| 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".