Nanocomposites of carbon nanosphere and graphene oxide with iron oxide as high-performance adsorbents for arsenic removal
Bibliographic record
Abstract
Arsenic is a widely distributed element in the Earth’s crust with an average \nterrestrial concentration of about 5 g ton-1 \n. Arsenic is a persistent, bio-accumulative, toxic \nelement. The United States Environmental Protection Agency (EPA) has implemented the \ndischarge criterion of 10 µg L \n-1 \nfor arsenic as the maximum acceptable level for ground \nwater. During the past decades, several techniques have been developed for the removal \nof arsenic from the wastewater, including chemical precipitation, adsorption and ion \nexchange, membrane and biological removal processes, and so on. Because of the good \narsenic removal efficiency and the low cost, adsorption is a more popular method. In this \nthesis research, two ranges of iron oxide nanocomposite adsorbents have been developed \nand studied for their performance properties towards arsenic removal. \nNovel iron oxide encapsulated carbon nanospheres (FeOx-CNS) with excellent \narsenic adsorption performance has been successfully synthesized. CO2 activated carbon \nnanospheres material (A-CNS) with high surface area (2271 m²g \n-1 \n) and high pore volume \n(5.18 cm³g \n-1 \n) was selected as the porous matrix. After surface oxidation by ammonium \npersulfate (APS), iron oxide was loaded into the carbon nanospheres as the effective \narsenic adsorbent. Transmission electron microscopy (TEM) and Braunauer–Emmett– \nTeller (BET) results indicate that iron oxide nanoparticles (7-60 wt%) are well-dispersed \nwithin the mesopores. In particular, FeOx-CNS-13 composite shows most optimum performance properties, with high arsenic adsorption capacities achieved for both As(III) \n(416 mg g−1 \n) and As(V) (201 mg g−1 \n). \nAnother range of amorphous iron oxide-graphene oxide (FeOx-GO) \nnanocomposites having different graphene oxide (GO) content (36-80 wt%) was prepared \nby coprecipitation of ferrous sulfate heptahydrate and ferric sulfate hydrate on GO sheets. \nThe composites have been thoroughly characterized and investigated for their \nperformance towards arsenic removal. The optimum composite, FeOx-GO-80 having the \nhighest iron oxide content of 80 wt% shows excellent arsenic adsorption capacities for \nboth As(III) (147 mg g−1 \n) and As(V) (113 mg g−1 \n), which are highest among iron oxideGO \ncomposites reported to date for arsenic removal. The high performance along with \nlow cost and convenience in synthesis makes this range of amorphous iron oxide-GO \nnanocomposites promising for applications.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".