Greener synthesis of functionalized graphene oxide for the adsorption of hydrogen sulfide present in natural gas
Bibliographic record
Abstract
Graphene oxide (GO) was synthesized through the thermal decomposition (TD) of citric acid (CA) through an alternative and more environmentally friendly route than the conventional method. Functionalization with amine (diethylenetriamine) was performed (GOF-DETA) to impart basic characteristics to GO, and impregnated copper (GOF-DETA+Cu) was used to improve the selectivity. The functionalization and impregnation were performed via ultrasonication, a green methodology facilitating homogenization and GO sheet formation. FTIR analysis confirmed the formation of the materials. XRD revealed an increase in the interplanar distance of 0.49 nm for GO and 0.57 nm for GOF-DETA and GOF-DETA+Cu. BET analysis demonstrated a significant increase in surface area, with GO having 18 m 2 ·g -1 and GOF-DETA exhibiting 456 m 2 ·g -1 . In the adsorption tests, GOF-DETA and GOF-DETA+Cu exhibited H 2 S adsorption capacities of 158 and 395 mg·g -1 , respectively. Consequently, the synthesis of GO through the TD of AC offers an alternative, efficient, and environmentally friendly route, and amine functionalization using ultrasound further enhances its properties. Among the produced and tested materials, GOF-DETA+Cu showed superior results, making it the most promising adsorbent for natural gas desulfurization in this study. • Green synthesis of GO from citric acid via thermal decomposition. • Copper-dosed amine-functionalized GO boosts selectivity and adsorption efficiency. • Sonication contributes to eco-friendly process by imparting synergistic effects. • Greener GOF-DETA+Cu shows competitive H2S adsorption against advanced adsorbents.
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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) | 0.000 | 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 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".