Surface area measurement techniques, and scope of nutrient removal from greenhouse wastewater by improved activated carbon from tomato plant biomass
Bibliographic record
Abstract
Abstract. A noble approach was developed to resolve dual challenges facing Ontario greenhouses (GHs) in handling generated waste biomass and leached GH nutrient feed (GHF) wastewater. Activated carbon (AC) was produced from hydrochar (HC) of tomato plant biomass (TPB) and applied in treatment of GNF with a view to recycle and reuse both wastes. Activation was conducted at lower temperature (700 - 730 °C) and subsequently assessed for surface area by gas and liquid sorption techniques. Morphology evaluation by SEM-EDS revealed rough surface pores in the produced AC which did not support the surface area (263 m2/g) and pore volume (0.12 mL/g) measurement by N2 gas adsorption. Liquid media sorption methods using methylene blue technique provided a reasonable surface area and pore volume of 463 m2/g and 0.17 mL/g, respectively. Nutrients reduction from leached GNF by treating with generated AC was around 26-36 %, which is comparable to some commercial AC (CAC). Fourier Transform Infrared Spectroscopy and Thermogravimetric analysis suggested further improvement opportunities by removing more volatiles (40-50%) and oxygen functionals (-OH, >C=O, --C-O-) from generated AC at temperatures higher than 730 °C. Conversion of waste TPB and treatment of GNF would support GHs in management of both the waste materials to comply with regulations. Implementation of results would not only provide economic gain from recycle and reuse (RRR) of wastes but also supports environmental sustainability.
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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 | 0.001 | 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".