Purple Bacteria for Wastewater Treatment and Resource Recovery
Bibliographic record
Abstract
Wastewater treatment plants are transitioning into resource-recovery centers that reduce pollution from the wastewater by transforming it into valuable resources.Purple phototrophic bacteria (PPB) have been shown to exhibit accumulative properties under anaerobic conditions while utilizing infrared light as their primary energy source, which reduces energy input into the biochemical process.Manipulating their accumulative properties will allow carbon, phosphorus and energy recovery from wastewater in the form of poly-hydroxyalkanoates (PHAs), poly-phosphates (poly-P) and hydrogen gas, respectively.This work first aimed to investigate the viability of treating wastewater using PPB.A 4-L anaerobic photobioreactor was constructed and illuminated with 850 nm infrared light.Steady-state analysis showed satisfactory chemical oxygen demand (COD) removal with soluble COD dropping to less than 30 mg/L.The enriched biomass under this reactor accumulated about 5 mg-P/L of ortho-phosphates corresponding to 6% of the total dry weight of solids.Fluorescence microscopy analysis with the dye 4,6-diamidino-2-phenylindole (DAPI) suggested that a portion of the accumulated P was in the form of poly-P.An enrichment experiment was conducted to investigate the effect of isolating the carbon sources of the synthetic wastewater (normally comprises eight carbon sources) on the microbial community composition.rRNA gene amplicon sequencing revealed the presence of fermenters when fermentable substrates (starch, milk or glycerol) were fed to the reactors, while only a small amount fermenting bacteria were present when reactors were fed acetate.The latter fermenters were likely fermenting microbial metabolites and macromolecules.The PPB accounted for 50% of the sequence reads when the reactors were illuminated with 850 nm, while they were only 30% of the reads with the 940 nm light.Microbial composition of the reactors fed complete wastewater appeared to be more similar to the fermentable substrates than the acetate reactors.A second experiment was done to examine I also want to extend my appreciation to all the members (Alex, Arshath, Claire, Julie, Sampriti, Sarayu and Vini) of the environmental engineering lab for illuminating this beautiful environment with their companionship.I would also like to acknowledge the people who have lent me their hands and facilitated the completion of this project.Namely, our lab technician, John Bartczak who helped with the reactor construction, my summer student
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 teacher head, 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".