Novel banana peel/graphene oxide derived biosorbent for water purification
Bibliographic record
Abstract
More than 100 million tons of banana peels are produced annually, and about 40 million banana peels (40% of total weight) remain greatly unused. Hence, exploring banana peels’ ability to clean contaminated water would bring an additional value to the current “waste” product. One of the most common aspects of water pollution currently is heavy metal contamination, which is particularly dangerous for humans due to its high toxicity. Banana peels contain a high concentration of carbohydrates, the two most abundant being cellulose and starch, which has multiple hydroxyl and carboxyl functional groups. Banana peels are an easily available and cost-effective adsorbent that can adsorb different kinds of heavy metal ions. This research primarily focuses on improving the current efficiency of this technique through the development of a banana peel/graphene oxide hybrid adsorbent. The cross-linking graphene oxide possess numerous hydroxyl, carbonyl, carboxyl, and epoxide functional groups that can be used to induce chemical reactions with banana peel carbohydrates, providing the graphene oxide with additional functional groups. This modification can potentially increase the adsorption capacity of banana peel derived adsorbents. It is evident through FTIR analysis that banana peel powder and graphene oxide have many functional groups of similar types. Thus, reactions can readily occur to combine the two substances. The TGA analysis of both compounds, however, indicates different patterns of thermal decomposition. Further thermal analysis is required for the hybrid adsorbent. After the development and characterization of this hybrid adsorbent, the next step is to complete a water purification analysis. In the future, banana peel/graphene oxide derived adsorbent may serve as a sustainable and efficient solution for water purification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".