Efficient decaffeination with recyclable magnetic microporous carbon from renewable sources: Kinetics and isotherm analysis
Bibliographic record
Abstract
Rapid global urbanization and population growth have ignited an alarming surge in emerging contaminants in water bodies, posing health risks, even at trace concentrations. To address this challenge, novel water treatment and reuse technologies are required as current treatment systems are associated with high costs and energy requirements. These drawbacks provide additional incentives for the application of cost-effective and sustainable biomass-derived activated carbon, which possesses high surface area and low toxicity. Herein, we synthesized microporous activated carbon (MAC) and its magnetic derivative (m-MAC) from tannic acid to decaffeinate contaminated aqueous solutions. Detailed characterization using SEM, BET, and PXRD revealed a very high surface area (>1800 m2/g) and a highly porous, amorphous, heterogeneous sponge-like structure. Physicochemical and thermal analyses using XPS, TGA, and EDS confirmed thermal stability, unique surface moieties, and homogeneous elemental distribution. High absorption performance (>96 %) and adsorption capacity (287 and 394 mg/g) were recorded for m-MAC and MAC, respectively. Mechanistic studies showed that the sorption of caffeine is in tandem with multilayer and chemisorptive mechanisms, considering the models’ correlation and error coefficients. π-π stacking and hydrogen bonding were among the interactions that could facilitate MAC-Caffeine and m-MAC-Caffeine bonding interactions. Regeneration and reusability experiments revealed adsorption efficiency ranging from 90.5 to 98.4 % for MAC and 88.6–93.7 % for m-MAC for five cycles. Our findings suggest that MAC and its magnetic derivative are effective for caffeine removal, and potentially other organic contaminants with the possibility of developing commercially viable and cost-effective water polishing tools.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".