Specifically designed amine functional group doped sludge biochar for inorganic and organic arsenic removal
Bibliographic record
Abstract
Abstract Usages of hospital sludge as a biochar adsorbent for wastewater treatment plants were investigated. Microwave carbonization was used to carbonize the sludge and then chemically activated with ZnCl 2 to increase surface area and porosity. A newly designed amine functional group’s doped Sludge Biochar Carbon (SBC) presents effective inorganic arsenic (As (III)) and organic arsenic (Dimethylarsinic Acid, DMA) adsorption in water. The pore volume, pore size distribution and specific surface area were determined by performing nitrogen adsorption-desorption measurements. The Fourier Transform Infrared of the SBC was recorded to study the functional groups at room temperature. The composition of SBC was further determined by X-ray Photoelectron Spectroscopy. In order to understand the effect of amine functional complexes on arsenic adsorption, the adsorption mechanism of As (III) and DMA on SBC surfaces modified with amine functional complexes was studied using Density Functional Theory (DFT). DFT results showed that both physical and chemical adsorption of As (III) and DMA on SBC surfaces occurred. The participation of amine functional complexes greatly promoted the surface activity of SBC surface and its adsorption capacity on arsenic. The physical adsorption energies of As (III) and DMA on SBC surface with amine functional complexes were − 38.8 and − 32.4 kJ mol − 1 , respectively. The chemical adsorption energies of As (III) and DMA on SBC surface with amine functional complexes were − 92.9 and − 98.5 kJ mol − 1 , 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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".