Sustainable carbon nanomaterials solutions: Facile synthesis from heavy metal-rich water hyacinth using CVD method
Bibliographic record
Abstract
A sustainable method for synthesizing carbon nanomaterials (CNMs) using water hyacinth, which accumulates heavy metals from contaminated water, has been developed. This approach eliminates the need for expensive external catalysts. CNMs were synthesized from the roots of water hyacinth cultured in iron-rich artificial wastewater for one week, compared to control plants grown under standard conditions. After treatment, the plants were harvested, and their phytoremediation efficiency was assessed using AAS. Results showed rhizofiltration as the primary mechanism in the roots. The roots were then used as raw material for CNM synthesis via a catalyst-free chemical vapor deposition process at 650 °C, with acetylene as the carbon source. Characterization using SEM, TEM, XRD, Raman spectroscopy, and TGA revealed that the CNMs mainly consisted of bamboo-like carbon nanotubes and carbon nanofibers. The iron content in the treated roots acted as a catalyst for CNM formation, while Si and Al in the control sample facilitated nucleation. Raman spectroscopy confirmed a high degree of crystallization in both samples. • Sustainable carbon nanomaterials innovations. • Easy production using the CVD method from water hyacinth rich in heavy metals. • Use of water hyacinth as a support material in the synthesis of carbon nanotubes. • Converting a noxious plant into valuable products.
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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 | 0.001 |
| 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".