Bio-inspired graphene oxide sponges for enhanced adsorption of legacy and emerging contaminants from water
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 it