machine learning

From Black Box to Precision Tool: The Emerging New Science of Biochar Application
CatBoost AI Model Hits 98.8% (R2) Accuracy in Predicting Water-Cleaning Biochar Performance
An Engineer of the Atomic Scale: Meet Dr. Francisco Martin-Martinez
Optimal 30 t/ha Biochar Rate Boosts Vegetable Growth, Yield Prediction Hits R2=0.94
Machine Learning Boosts Biocomposite Strength: Coconut Biochar Reinforcement Yields 98.77% Prediction Accuracy for Tensile Strength
AI Framework Boosts Biochar Quality by ≈13% Fixed Carbon and ≈22% Lower Volatiles Over Previous Pyrolysis Optimum
Machine Learning Predicts Phosphorus Fate in Biowaste Hydrochar: XGBoost Model Achieves R2=1.0 for Inorganic Liquid Phosphorus
A Natural Solution for a Modern Problem: Biochar from Mexican Logwood Removes 84.5 mg/g of Acetaminophen from Water
XGBoost Model Achieves 99% Predictive Accuracy for Phosphorus Recovery from Swine Manure
Optimized Biochar from Tea Waste Achieves 82.66% Pollutant Removal, With RNN Model Predicting Performance at an R2 of 0.960