Incorporation of Fe<sub>2</sub>O<sub>3</sub> Spacer Molecules in Microwave‐Exfoliated Graphene Oxide as Efficient Electrodes for Simultaneous Detection of Cd<sup>2+</sup>, Pb<sup>2+</sup>, and Hg<sup>2+</sup> in Water
Bibliographic record
Abstract
Toxic adverse effects to human beings caused by heavy metal ions resemble a serious threat to mankind and often appear in the journal headlines. However, simultaneous detection of heavy metal ions using analytical tools is challenging. In this regard, simultaneous electrochemical detection of Cd2+, Pb2+, and Hg2+ ions in water is presented using iron oxide (Fe2O3) nanostructures as spacers incorporated between microwave‐exfoliated graphene oxide (MEGO). First, Fe2O3 nanostructures are synthesized using ferric nitrate in presence of poly(vinylpyrrolidone) and followed by their in‐situ incorporation into expanded graphene oxide (GO). Exfoliated GO accommodates large amount of Fe2O3 nanoparticles via microwave‐assisted method, minimizing the restacking of GO sheets. Consequently, Fe2O3‐incorporated MEGO (Fe2O3‐MEGO) fabricated on screen‐printed electrodes (SPE) demonstrate well‐separated anodic peak potentials at −0.65, −0.45, and +0.27 V for Cd2+, Pb2+, and Hg2+ ions. Moreover, Fe2O3‐MEGO/SPE electrode exhibits wide linear range (0.4 to 74.78 μM), high sensitivities (8.11, 9.59, and 3.01 μA μM−1 cm−2) with low detection limits (0.2, 0.17, and 0.25 μM) for Cd2+, Pb2+, and Hg2+ ions, respectively. Therefore, this kind of incorporating nanomaterials as spacer molecules between GO allows for the design of alternative pathways to minimize restacking of GO and to increase sensitivity toward multiple targeted species.
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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) | 0.000 | 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 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".