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 CA 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.
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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) | 0.001 | 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".