Sustainable Extraction of High-Value Phytochemicals from Spontaneous Flora Biomass: Integrating NADES Solvents and Machine Learning Within a Circular Biorefinery Framework

What if the weeds in your backyard held the key to sustainable medicine? Scientists just unlocked their potential using eco-friendly solvents and artificial intelligence.

Forget harsh chemicals—nature's own solvents are taking center stage. Researchers have crafted Natural Deep Eutectic Solvents (NADES) from betaine and 1,3-propanediol, turning them into green extraction media for Artemisia annua, a plant often dismissed as spontaneous flora. This isn't just a lab experiment; it's a step toward a circular bioeconomy where waste becomes worth.

The team tested two extraction methods—vortex and ultrasound—and tweaked parameters like molar ratio, water content, and temperature. The results were striking: maximum polyphenol yield hit 52.08 mg GAE/mL with ultrasound at 20°C for 15 minutes, while flavonoids peaked at 17.34 mg QE/mL with vortex for 45 minutes. Each phytochemical class demanded its own sweet spot, proving that one-size-fits-all doesn't apply here.

To navigate this complex landscape, they deployed a hybrid AI model—deep neural networks fused with the SHADE algorithm. This machine learning wizard predicted extraction performance across a broad parameter space with high accuracy, eliminating guesswork. The framework showcases how green solvent design and data-driven modeling can join forces, turning underutilized biomass into a resource-efficient goldmine. It's a blueprint for sustainable extraction that could reshape industries, from pharma to nutraceuticals.

Key Points

• NADES from betaine and 1,3-propanediol extract Artemisia annua phytochemicals.
• Ultrasound yields 52.08 mg GAE/mL polyphenols; vortex gives 17.34 mg QE/mL flavonoids.
• Hybrid AI (DNN+SHADE) predicts extraction performance accurately.