Bio-inspired graphene oxide sponges for enhanced adsorption of legacy and emerging contaminants from water
Bibliographic record
Abstract
This study investigates the adsorption performance of bioinspired, amino acid-modified reduced graphene oxide (rGO) sponges to remove model legacy and emerging contaminants from water. Modified sponges containing L-tryptophan (GOTR) and L-phenylalanine (GOPA) were synthesized and characterized using scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy, Raman spectroscopy, X-Ray Diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and surface area analysis to confirm structural modifications and functional group incorporation. Adsorption experiments were conducted using methylene blue (MB), rhodamine B (RhB), acetaminophen (AC), and diclofenac (DCF) as model legacy and emerging contaminants of concern. The optimized sponges, GOTR 15–20% and GOPA 1.5–2.5% , demonstrated maximum adsorption capacities of 1003 mg/g for DCF, 653 mg/g for MB, 556 mg/g for AC, and 556 mg/g for RhB, as described by the Langmuir isotherm model. The incorporation of amino acids enhanced the surface area and the availability of active functional groups, increasing adsorption efficiency by up to 2-fold compared to unmodified rGO sponges. These findings suggest that amino acid-modified rGO sponges offer an effective, versatile, and green solution for removing diverse legacy and emerging contaminants from water.
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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".