Feasible synthesis of sulphonated hydrochar from guava peel <i>via</i> hydrothermal carbonisation: modelling and statistical optimisation for methylene blue dye removal
Bibliographic record
Abstract
This investigation aims to propose an alternative approach of converting guava peel waste (GP) into sulphonated hydrochar (GP-SHC) via hydrothermal carbonisation process assisted with 1 M sulphuric acid activation. The synthesised GP-SHC was utilised as a potential adsorbent to eliminate a cationic model dye (Methylene blue (MB)) from an aqueous environment. Multiple analysis was conducted to investigate the unique features of GP-HTC including BET, SEM-EDX, XRD, FTIR, and pHpzc. Moreover, three key input parameters such as GP-SHC dosage (A: 0.02–01 g/0.1 L), pH (B: 4–10), and contact time (C: 20–180 min) were statistically optimised using a Response Surface Methodology (RSM) with Box Behnken Design (BBD). The desirability function approach indicates the best MB removal 98.2% can be obtained using 0.07 g/0.1 L of GP-HTC dose, pH of 9.9 with a contact time of 175 min. The accuracy and acceptability of the BBD model were confirmed by the ANOVA analysis finding with the significant F-value = 183.59 and p-value < 0.0001. Langmuir isotherm model and pseudo-second order (PSO) kinetic model were well explained the adsorption data. Thus, the adsorption of MB by GP-HTC occurred via chemosorption as a monolayer system. The Langmuir isotherm model confirms the adsorption capacity of GP-HTC for MB dye to be 308.3 mg/g. The findings of this research demonstrate the double benefits of alternative guava peel solid waste management and the preparation of a functionalised renewable adsorbent for cationic dye removal.
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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) | 0.002 | 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".