Production and characterisation of activated carbon and carbon nanotubes from potato peel waste and their application in heavy metal removal.
Bibliographic record
Abstract
Abstract Herein, activated carbon (AC) and carbon nanotubes (CNTs) were synthesised from potato peel waste (PPW). Different ACs were synthesised via two activation steps: firstly, with phosphoric acid (designated PP) and then using potassium hydroxide (designated PK). The AC produced after the two activation steps showed a surface area as high as 833 m 2 g −1 with a pore volume of 0.44 cm 3 g −1 , where the raw material of PPW showed a surface area < 4 m 2 g −1 . This can help aid and facilitate the concept of the circular economy by effectively up-cycling and valorising waste lignocellulosic biomass such as potato peel waste to high surface area AC and subsequently, multi-walled carbon nanotubes (MWCNTs). Consequently, MWCNTs were prepared from the produced AC by mixing it with the nitrogen-based material melamine and iron precursor, iron (III) oxalate hexahydrate. This produced hydrophilic multi-wall carbon nanotubes (MWCNTs) with a water contact angle of θ = 14.97 °. Both AC and CNT materials were used in heavy metal removal (HMR) where the maximum lead absorption was observed for sample PK with a 84% removal capacity after the first hour of testing. This result signifies that the synthesis of these up-cycled materials can have applications in areas such as wastewater treatment or other conventional AC/CNT end uses with a rapid cycle time in a two-fold approach to improve the eco-friendly synthesis of such value-added products and the circular economy from a significant waste stream, i.e., PPW.
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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".