Production and characterization of magnetic Biochar derived from pyrolysis of waste areca nut husk for removal of methylene blue dye from wastewater
Bibliographic record
Abstract
Abstract The textile industry causes lots of pollution due to its discharge of untreated coloured effluents into water bodies, impacting the environment. The present study includes a slow pyrolysis technique to produce magnetic biochar derived from waste areca nut husk (ANH)) biomass to adsorb methylene blue dye. The biochar and biomass were characterised via proximate analysis, ultimate analysis, bulk density, heating value, extractive content, biochemical analysis, thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy (FTIR), SEM, BET surface area, pH, water holding capacity (WHC) and X-ray diffraction (XRD). A semi-batch reactor was used to produce biochar (ANHB) at 600 and 800 oC at 10 oC min− 1 heating rate and 45 min holding time in an inert atmosphere. The produced biochar was magnetised by blending aqueous biochar suspensions with aqueous Fe3+/Fe2+ solutions. Further, magnetised biochar is employed to eliminate methylene blue (MB) dyes at different pHs, contact times, temperatures, dosages and concentrations. Biochar derived at 800 oC (ANHB800) gave increased carbon content (62.93%), heating value (33.02 MJ/kg), and BET surface area (112 m2/g) over biochar derived at 600 oC. The results of the acid treatment biochar (ANHBA800) demonstrated that 5M H2SO4 causes a BET surface area increase (265 m2/g) and a ash content decrease (9.96%). However, when magnetic biochar was produced at 800 oC it shows an additional increase in BET surface area upto 385 m2/g. The MB dye absorption analysis confirmed 85.47% adsorption at 0.3 g/l dosage, 100 ppm concentration, 30 oC, 60 min contact time, and pH 7. The adsorption capacity was 785.34 mg/g when fit by the Langmuir isotherm model. Magnetic nanoparticles enhance active sites, electrostatic interactions, and recovery, improving efficiency, cost-effectiveness, and sustainability in dye removal. The adsorption kinetics results suggested that the pseudo-second-order model best explains the experimental data with an R2 value of 0.994. Additionally, the adsorption isotherm studies were best fitted by the Langmuir model adsorption conforming monolayer adsorption of MB on biochar surface.
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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) | 0.000 | 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 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".