Advanced graphene oxide synthesis for arsenic removal from groundwater in Mexico and Colombia
Bibliographic record
Abstract
• Optimization of the synthesis of a novel graphene oxide for arsenic removal using oxidizing agents. • Physicochemical characterization and adsorption capacity analysis of the optimized graphene oxide (GOBR) and graphene oxide synthesized via the Hummers method, utilizing techniques such as XRD, FTIR, TGA, Raman spectroscopy, specific surface area analysis, surface charge distribution, and adsorption isotherms. • Evaluation of the adsorption efficiency of GOBR in groundwater from Mexico and Colombia, assessing the presence of ions such as As, F - , CrO 4 ² - , Cl - , CO 3 ² - , and SO 4 ² - . This study presents the optimization of graphene oxide (GO) synthesis for arsenic (As) removal from contaminated groundwater in Mexico and Colombia, using the modified Hummers method. By applying response surface methodology (RSM), the concentrations of NaNO₃ and KMnO₄ were adjusted to maximize the density of oxygenated functional groups, significantly enhancing the adsorption capacity for As(V). Characterization results revealed a reduction in macroporosity and an increase in mesoporosity and microporosity, contributing to the superior adsorption performance. The optimized GO achieved an adsorption capacity of 99.13 mg g⁻¹ at 308 K under competitive conditions with other ions such as F⁻, CrO₄²⁻, Cl⁻, CO₃²⁻, and SO₄²⁻. Additionally, the synthesis process reduced toxic by-products, demonstrating sustainability for industrial-scale applications. These findings represent a significant advancement in the development of efficient and sustainable materials for groundwater remediation.
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.
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 | 0.000 | 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".