High-intensity, Low-frequency Ultrasound Treatment as Sustainable Strategy to Develop Innovative Biomaterials from Agri-food Byproducts and Wastes
Bibliographic record
Abstract
Bioconversion is an important avenue for finding value from biomass waste produced by the agricultural industry. One avenue of conversion is the development of upcycling byproducts and waste from food processing to value-added products. This includes degradable biomaterials which have real potential to reduce waste, improving economic, social and environmental impacts. As such, this research paper was focused on exploring two avenues of bioconversion from waste products of tomato skin, hemp meal and hops vines: identification of phytochemicals and the development of bioplastic. Combined to these researches, the effect of Ultrasound as a green technology was studied in both contexts. It was found that Ultrasound treatment reduced extraction time for saponin and phenolic acid from tomato skin, hemp meal and/or hops vines from 24h to 30 min. However, Ultrasound Assisted Extraction (UAE) was shown to affect the phenolic acid and saponin profiles of certain extracts. Ultrasound treatment was shown to positively impact the overall microscopic structure and qualities of bioplastic such as water activity, percentage moisture, hardness, cohesiveness, resilience, and springiness index. This study suggests that Ultrasound can be used as sustainable non-thermal method for extraction of active saponins and phenolics but also in bioplastic formulation to enhance physico-chemical characteristic.
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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".