Identifying barriers to scaled-up production and commercialization of chitin and chitosan using green technologies: A review and quantitative green chemistry assessment
Bibliographic record
Abstract
Chitosan (CHT) production from Chitin (CH) is a billion-dollar industry but is constrained by multi-step chemical extractions that are energy and wastewater-intensive. Numerous green recovery technologies (GRT)s have paved the path for sustainable extraction, however, these have not been adopted for scale-up or mainstream commercialization. Therefore, this review critically evaluates the chemical, biological, combined biological-chemical and GRTs for CH/CHT recovery on commercially important criteria such as yields, molecular properties, cost/gram, water & energy use and wastewater & GHG emissions to identify barriers that hinder (i) the scaled-up, cost-effective commodity production of CH/CHT using GRTs (ii) the preparation of CH/CHT standards and (iii) the successful pathway from CH/CHT recovery to commercialization of chitosan-based products, supporting United Nations Sustainable Development Goals (UN SDG)s, particularly SDG 12. To arrive at the data-driven assessment, techno-economic and green chemistry metrics such as PMI and E-factor were calculated. The industry-developed quantitative green chemistry evaluator DOZN™ was used to assess resource & energy efficiency and human & environmental health hazards for CHT production. Mechanochemistry was identified as a viable GRT based on the limited literature available for quantitative assessment, and increasing the yield from GRT processes was identified as key to improving economic performance while also reducing environmental impacts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 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".