Green synthesis approach: extraction of chitosan from fungus mycelia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Chitosan, copolymer of glucosamine and N-acetyl glucosamine is mainly derived from chitin, which is present in cell walls of crustaceans and some other microorganisms, such as fungi. Chitosan is emerging as an important biopolymer having a broad range of applications in different fields. On a commercial scale, chitosan is mainly obtained from crustacean shells rather than from the fungal sources. The methods used for extraction of chitosan are laden with many disadvantages. Alternative options of producing chitosan from fungal biomass exist, in fact with superior physico-chemical properties. Researchers around the globe are attempting to commercialize chitosan production and extraction from fungal sources. Chitosan extracted from fungal sources has the potential to completely replace crustacean-derived chitosan. In this context, the present review discusses the potential of fungal biomass resulting from various biotechnological industries or grown on negative/low cost agricultural and industrial wastes and their by-products as an inexpensive source of chitosan. Biologically derived fungal chitosan offers promising advantages over the chitosan obtained from crustacean shells with respect to different physico-chemical attributes. The different aspects of fungal chitosan extraction methods and various parameters having an effect on the yield of chitosan are discussed in detail. This review also deals with essential attributes of chitosan for high value-added applications in different fields.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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 it