High Carboxyl Content Cellulose Nanofibers from Banana Peel via One-Pot Nitro-Oxidative Fabrication
Bibliographic record
Abstract
Banana peels (BP) are the outer covering of a banana fruit and are usually discarded as abundant and underutilized waste from banana production and consumption.This work investigated the bioresource for one-pot preparation of carboxyl cellulose nanofibers (CCNF) using the nitro-oxidation method.Briefly, BP was added to a mixture of HNO3 and NaNO2 at 30-50 o C for 6 h.To characterize the obtained material, different methods, including X-ray diffraction, TEM images, FTIR spectroscopy, EDX spectroscopy, and acid-base titration, were used.TEM images reveal that the nanofibers were mainly formed with a diameter of roughly 20-50 nm.Moreover, the results of FTIR, EDX, and titration proved the formation of carboxylic acid (-COOH) groups in CCNF with an amount of 4.9 mmol/g, or 22 wt%.This high carboxyl content makes CCNF an ionexchange material, thereby enabling the development of new applications.Compared with traditional multistep processes for the preparation of cellulose nanomaterials, the current approach is simpler, uses fewer chemicals, and consumes less energy.Overall, these successful results not only valorize potential banana peel waste but also introduce an advanced material for further use.
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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) | 0.001 | 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".