Sustainable Polyhydroxyalkanoate Production from Food Waste via Bacillus mycoides ICRI89: Enhanced 3D Printing with Poly (Methyl Methacrylate) Blend
Bibliographic record
Abstract
In view of implementing green technologies for bioplastic turning polices, novel durable feedstock for Bacillus mycoides ICRI89 used for efficient polyhydroxybutyrate (PHB) generation is proposed herein. First, two food waste (FW) pretreatment methods were compared, where the ultrasonication approach for 7 min was effective in easing the following enzymatic action. After treatment with a mixture of cellulase/amylases, an impressive 25.3 ± 0.22 g/L of glucose was liberated per 50 g of FW. Furthermore, a notable 2.11 ± 0.06 g/L PHB and 3.56 ± 0.11 g/L cell dry eight (CDW) over 120 h were generated, representing a productivity percentage of 59.3 wt% using 25% FW hydrolysate. The blend of polyhydroxybutyrate/poly (methyl methacrylate) (PHB/PMMA = 1:2) possessed the most satisfactory mechanical properties. For the first time, PHB was chemically crosslinked with PMMA using dicumyl peroxide (DCP), where a concentration of 0.3 wt% had a considerable effect on increasing the mechanical stability of the blend. FTIR analysis confirmed the molecular interaction between PHB and PMMA showing a modest expansion of the C=O stretching vibration at 1725 cm−1. The DCP-PHB/PMMA blend had significant thermal stability and biodegradation profiles comparable to those of the main constituent polymers. More importantly, a 3-Dimetional (3D) filament was successfully extruded with a diameter of 1.75 mm, where no blockages or air bubbles were noticed via SEM. A new PHB/PMMA “key of life” 3D model has been printed with a filling percentage of 60% and a short printing time of 19.2 min. To conclude, high-performance polymeric 3D models have been fabricated to meet the pressing demands for future applications of sustainable polymers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".