Synthesis of Industrial Raw Material from Cellulosic Agricultural Wastes: Focus on Carboxymethyl Cellulose
Bibliographic record
Abstract
Agricultural wastes such as sugarcane bagasses, maize cob, palm kernel cake, palm oil empty fruit bunches, banana pseudo stem and orange mesocarp have been known to be potential sources of cellulose. From these cellulose sources sodium carboxymethylcellulose (NaCMC), a water soluble cellulose derivative and an essential raw material in the food, cosmetic, pharmaceutical and detergent industries could be synthesized. Importantly, orange mesocarp generated from orange peel is an abundant agricultural by-product which consists of about 62.5% cellulose. It is significantly considered as one of the alternative secondary resources for cellulose. In this work, cellulose was extracted from orange mesocarp and then converted to NaCMC. The orange mesocarp was dried and ground to pass 20 mesh screen. Cellulose was extracted using 8% NaOH at 100oC for 3.5 hrs and bleached using 3.85% NaOCl at 30oC for 3 hrs. Carboxymethylcellulose (CMC) was consequently synthesized from the extracted cellulose by alkalization followed by etherification. The physicochemical properties of the NaCMC were determined in terms of the degree of substitution, viscosity and with the use of FTIR spectroscopy. The NaCMC resulted from this work has a viscosity of 14.0cP at 29.8oC and DS 1.02 and therefore was categorized as technical grade with medium viscosity. After optimization and scaling up of the production process the NaCMC synthesized will be a useful and cheap raw material for the industries. K eywords: Agricultural wastes, industrial raw material, orange mesocarp, sodium carboxymethylcellulose.
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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 | 0.001 |
| 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 | 0.001 |
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 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".