Design of a Sustainable Polysaccharide‐Lignocellulosic‐Mineral Granular Biocomposite Sorbent for Methylene Blue Removal
Bibliographic record
Abstract
ABSTRACT Four granular biocomposite adsorbents containing spent coffee grounds (SCG), eggshells (ES), and chitosan (Chi) for remediation of waterborne methylene blue (MB) were prepared. The role of epichlorohydrin (ECL) crosslinking and variable NaOH concentration (a = 0.5 M, b = 1.8 M) during the adsorbent synthesis was investigated for its role on the MB adsorption capacity ( Q m ). Materials characterization employed complementary methods ( 13 C solids NMR spectroscopy; thermogravimetry; BET analysis). The Sips isotherm model yielded equilibrium Q m values for non‐crosslinked (SCG80‐a (82 mg/g) and SCG80‐b (119 mg/g)), and crosslinked (SCG80‐ECL‐a (94 mg/g) and SCG80‐ECL‐b (139 mg/g)) at pH 6.5–7 and an adsorbent dosage of ca. 3 mg/mL of solution. Environmental conditions (< 20 mg/L MB) revealed dye removal (RE; %) from 90% to 95% with increased NaOH adsorbent treatment. Spiked river water with both 5 mg/L and 20 mg/L MB led to a decreased RE of 63% (0.5 M NaOH) and 76%–77% (1.8 M NaOH) versus lab water. Decreased RE values of 41%–47% and 49%–55% were observed in saline well water. Methanol regeneration yielded a slight drop in RE (%) after the initial cycle and remained stable after the second cycle up to 6 cycles. Increasing the NaOH concentration during adsorbent synthesis increases the MB adsorption capacity of SCG‐based adsorbents.
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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".