Production of Engineered Carbons from Secondary Waste Streams for Environmental Applications
Bibliographic record
Abstract
Pyrolysis and activation were used in this thesis to produce biochar and activated carbon from industrial digestate and hydrochar materials. In the first part of thesis, digestate derived biochars were obtained by varying the pyrolysis heating rate (10, 80, 100, 200, >1000 °C·min-1) and temperature (300 to 600 °C) to modify their soil-related characteristics (i.e., nutrient leachability, carbon stability, and heavy metal adsorptive capacity). The biochar produced at a temperature of 500 °C and heating rate of 10 °C·min-1 was also activated under using CO2 at 800 °C with different holding times (0.5-2 h). Results indicated a dominant effect of the pyrolysis temperature when compared to the heating rate on the biochar properties: when the temperature was increased, leachability dropped, and carbon stability increased. The desired biochar characteristics were also improved following activation, though increasing the activation holding time negatively impacted these improvements. In the second part of the thesis, activated carbons were produced from hydrochar through pyrolysis, physical activation, and a HNO3 post-treatment. The carbons were optimized based on the removal of methylene blue and phenol, model compounds of organic contaminants from water, by varying the activation gases (N2, CO2, and steam), temperatures (400 to 900 °C) and holding times (1, 2, or 3 hours). Results indicated that (i) a temperature of 800 °C and a 2-hour holding time provided carbons with the best methylene blue and phenol adsorption capacities, (ii) among the activation gases used, steam provided an activated hydrochar with higher surface area and a broader pore size distribution, resulting in the best adsorption performance, (iii) the HNO3 post-treatment of the best performing activated carbons improved methylene blue adsorption, yet had a negative effect on phenol adsorption.
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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".