Specifically designed magnetic biochar from waste wood for arsenic removal
Bibliographic record
Abstract
Abstract Arsenic is a carcinogenic substance, with many cases of poisoning related to arsenic pollution in groundwater. In Taiwan arsenic in groundwater caused the notorious Blackfoot disease. Methods for arsenic removal from water include precipitation, membrane processes, ion exchange, and adsorption, but these processing technologies suffer from high investment costs and complex operations. The traditional adsorption method cannot be used for arsenic removal due to its high operating costs, difficulties in recovery, and low adsorption capacity. To address these issues, this study designed an adsorption material based on biochar for arsenic removal with higher adsorption properties and easy recovery. Biochar sources are readily available from waste wood as a cheap and environmentally friendly material. The efficiency of As (III) removal is also promoted by FeCl 3 and KMnO 4 . The objectives of this research are to obtain optimum operation conditions by assessing the effects of different iron and manganese contents, different doses, different pH and different initial concentration. The adsorption mechanism between As (III) and biochar was studied by adsorption isotherms and the kinetic model. X-ray diffraction, energy-dispersive X-ray spectroscopy and elemental analyzer analysis results show that modified biochar has major elements of Fe and Mn. There is greater magnetism, 40 emu g − 1 , in the modified biochar. The maximum adsorption efficiency of 81% and 0.72 mg g − 1 capacity occurs when the ratio of Mn, Fe and C is 4:1:1. The adsorption capacity is high under higher pH with pristine biochar and 1FeC under lower pH with 1Fe2MnC. The reaction mechanism is divided into four pathways. The first pathway is the attachment of As (III) ions into the pore of biochar via physical adsorption. In the second pathway, biochar can connect with As (III) through hydrogen bonding from the function group -OH in the biochar and the As (III) itself. In the third pathway, they can contact each other by electron force when the biochar surface is filled with a positive charge. In the fourth pathway, the compounds of manganese have strong oxidizability to oxidize As (III) to As(V). The iron ions then act as a bridge connecting the biochar and the As (III), resulting in the formation of new complex compounds.
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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 | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.023 | 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; both teacher heads agree on what is shown here.
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".