Graphene Based Materials for Arsenic Sensing and Removal from Contaminated Water
Bibliographic record
Abstract
Environmental pollution especially toxic gases, heavy metal ions and organic pollutants in air and water, caused by industry and agricultural activities, severely threaten ecological balance and human health, and have received extensive attention worldwide. Arsenic contamination of groundwater is a critical problem that affects millions of people across the world and results in severe diseases such as skin or lung cancer and bladder cancer. Widespread arsenic contamination of groundwater has led to a massive epidemic of arsenic poisoning in Asia and America, especially such as India, Bangladesh, Vietnam, Cambodia, Thailand, southwest USA, Canada, Chile, and neighboring countries. Graphene has become a promising material for many different applications, such as nanoelectronic devices, physical, chemical and biochemical sensors, transparent conductive films, clean energy devices, or nanocomposite formulation. Sensing applications using graphene sheets as transducers have experienced a surge in the last few years, especially for gas sensing platforms, or electrochemical sensors, because of the high electrical conductivity of graphene. In this work, we investigate the synthesis of graphene by methane decomposition at 1000 °C onto free standing electrodeposited substrates, studying the effects of the electrochemical synthesis onto graphene quality. The growth of good quality graphene layers is also discussed in terms of the role played by grain boundaries and diffusion at the grain boundaries. As an advance of graphene studies, 2D graphene nanomaterials have been applied to arsenic sensing and removal from contaminated water. The work will discuss our latest studies and progresses on the application of graphene-based materials in the environmental protection and detection for arsenic contaminated waters. The work will present the use of graphene based electrodes for electrochemical sensing of arsenic; a similar approach will be discuss for arsenic removal, discussing kinetics and performances of the process.
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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".