Investigation of Environmental Applications of Graphene Oxide in Removal of Synthetic Dyes and Hydrogen Storage
Bibliographic record
Abstract
Synthetic dyes are prevalent organic contaminants in the effluents of industries such as textiles, paper, cosmetics, and pharmaceuticals, pose a significant environmental challenge due to their resistance to conventional treatment methods.The potential toxicity, carcinogenicity, and detrimental effects on both human health and the environment highlight the urgent need for development of technologies to efficiently remove synthetic dyes from water and wastewater.However, this is not the only environmental issue that demands attention.To mitigate the adverse impact of climate change, hydrogen, as a clean and sustainable renewable energy source, holds immense promise for reducing dependence on fossil fuels in the transportation sector.The development of materials with superior hydrogen adsorption capacities is pivotal for realizing the potential energy source to employ in transportation.Graphene oxide (GO) has developed interest as an adsorbent for water treatment and hydrogen storage applications, owing to its unique layered structure, high surface area, and numerous functional groups.This study first explores and evaluates the potential of GO as an effective alternative to commonly used adsorbents such as Granular activated carbon (GAC) and Zeolite NaY (NaY) for the removal of synthetic dyes.The research focuses on two synthetic dyes, Methylene Blue (MB) and Rhodamine B (RhB), varying in molecular size and structure.It assesses adsorbents' removal efficiency under slow-mixing condition to simulate large-scale conditions.Further, this research investigates the effect of surface modifications of GO and reduced GO (rGO) on surface area and hydrogen storage.To accomplish this objective, two different isotherm models Braunauer-Emmett-Teller (BET) and Density Functional Theory (DFT) as well as two different molecular size and polarity gas probe molecules, Nitrogen (N2) and Hydrogen (H2) were employed to measure the surface area of materials.viii5.4 Measurement of Pore Volume of rGO with Hydrogen and Nitrogen .........................
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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".