Efficient Removal of Organic and Inorganic Pollutants from Hospital Wastewater Using Flash Graphene in Batch Experiments
Bibliographic record
Abstract
Hospital wastewater contains a variety of organic, inorganic, and heavy metal contaminants.In this study, wastewater samples were collected from Al-Sader Medical City Hospital in Al-Najaf, Iraq, and the removal efficiency of pollutants was evaluated using flash graphene (FG) as an adsorbent.Flash graphene was synthesized from Iraqi orange peel using a novel carbon-based method known as flash Joule heating (FJH).The data obtained demonstrate the presence and subsequent reduction of various contaminants in hospital wastewater-such as total suspended solids (TSS), phosphate (PO), pH, total dissolved solids (TDS), dissolved oxygen (DO), total organic carbon (TOC), nitrate (NO), chloride (Cl), cobalt (Co), copper (Cu), and chemical oxygen demand (COD)-after treatment with flash graphene.Based on biological and chemical standards, the concentrations of heavy metals such as copper and cobalt exceeded the permissible limits set by Iraqi water quality regulations.In addition, elevated levels of total hardness and chloride were also observed, exceeding national water quality standards.These findings suggest that hospital wastewater is a significant source of environmental pollution and should be carefully considered when formulating strategies to assess environmental and public health risks.Flash graphene, characterized by an average pore diameter of 18.534 nm and a specific surface area of 11.168 m /g, proved to be an effective adsorbent for removing organic pollutants (BOD, COD, TOC), inorganic contaminants (TDS, DO, PO, NO, Cl, Co, Cu), and mixed pollutants such as TSS.This study proposes that adsorption using flash graphene could serve as a more cost-effective alternative to conventional hospital wastewater treatment systems.The volume and composition of toxic substances and liquid waste generated by hospital operations pose significant risks to both human health and the environment.In many developing countries, untreated hospital wastewater is often discharged directly into the environment, including rivers and other water bodies, which exacerbates environmental pollution.
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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".