Wood sawdust and sewage sludge pyrolysis chars for CO<sub>2</sub> adsorption using a magnetic suspension balance
Bibliographic record
Abstract
Abstract In view of global warming caused by the uncontrolled emissions of CO 2 to the environment, there is a great need to develop methods able to eliminate this pollutant. Apart from establishing new techniques, the exploration of new materials with high separation performance and low cost are of importance. This work aims to treat carbon‐metals‐rich chars originated from the fast pyrolysis of sewage sludge and wood sawdust to be used as CO 2 adsorbents. The samples were washed with ethanol and dichloromethane before their physical and chemical activation to remove impregnated bio‐oil. For the chemical activation, potassium hydroxide and hydrochloric acid were used as agents. The samples were analyzed via BET, TGA, XRD, and XRF. The results were used to investigate the role played by ethanol or dichloromethane and the effect of the activation on the properties of the materials such as specific surface area, total specific pore volume, and average pore size. CO 2 isotherms were obtained experimentally at 25 °C using a magnetic suspension balance and the data was used to estimate Langmuir and Freundlich isotherm parameters. After chemical treatment, the sewage sludge char specific surface area increased 11 times and had an adsorptive capacity of 1.32 × 10 −3 mol/g. The wood sawdust char specific surface area increased 90 times and had an adsorptive capacity of 3.73 × 10 −3 mol/g. Desorption was carried out from 5 × 10 5 to 1 × 10 5 Pa at 37 °C and the efficiencies were 89.0 and 84.4 % for the sewage sludge and wood sawdust, respectively.
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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) | 0.000 | 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) | 0.000 | 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".