Machine learning applications for biochar studies: A mini-review

Near-infrared spectroscopy as a green analytical tool for sustainable biomass characterization for biofuels and bioproducts: An overview

Biomass, a widely used renewable energy source, requires characterization to optimize biofuel and bioproduct processes, customize feedstocks, and ensure economic and environmental sustainability. Conventional wet-chemistry methods for biomass analysis are slow, expensive, and require significant reagents and skilled personnel. In contrast, near-infrared (NIR) spectroscopy, a faster, cost-effective, and reagent-free green technology, enables non-destructive biomass analysis with minimal sample preparation. This study provides an overview of the fundamentals of NIR spectroscopy and explores its recent applications for analyzing various biomass properties important to the biofuel and bioproduct industry. The study also critically evaluates the challenges and opportunities of using NIR spectroscopy for biomass analysis. This review aims to guide future research for rapid and high throughput characterization of biomass in the biomass industry, supporting the United Nations' sustainable development goal (SDG) 7: producing affordable and sustainable energy.

Biochar Stability Revealed by FTIR and Machine Learning

Biochar is a carbon-rich and environmentally recalcitrant material, with strong potential for climate change mitigation. There is a need for rapid and accessible estimations of biochar stability, the resistance to biotic and abiotic degradation in soil. This study builds on previous work by integrating Fourier-transform infrared spectroscopy (FTIR) data with predictive modeling to estimate standard stability indicators: H:C and O:C molar ratios. Lignocellulosic feedstocks were pyrolyzed at highest treatment temperatures (HTT) ranging from 150-700 °C, and all samples achieved H:C < 0.7 and O:C < 0.4 at HTT of 400 °C and above. Several statistical and machine learning models were developed using FTIR spectra. The random forest (RF) models, which incorporated full data preprocessing, yielded the highest accuracy (R 2 = 0.96 for both ratios) when tested on an unseen feedstock. Variable importance analysis identified spectral regions linked to aromaticity and inversely correlated to C-O stretches in cellulose and lignin as key predictors. The findings of this study verify that FTIR data can serve as a rapid and accurate tool for estimating biochar stability.

Advancements in Biochar as a Sustainable Adsorbent for Water Pollution Mitigation

Biochar, a carbon-rich material produced from the partial combustion of biomass wastes is often termed "black gold" for its potential in water pollution mitigation and carbon sequestration. By customizing biomass feedstock and optimizing preparation strategies, biochar can be engineered with specific physicochemical properties to enhance its effectiveness in removing contaminants from wastewater. Recent studies demonstrate that biochar can achieve > 90% removal efficiency for heavy metals such as lead and cadmium, > 85% adsorption capacity for organic pollutants such as dyes and phenols, and > 80% reduction in microplastics and nanoplastics. This review explores recent advancements in biochar preparation technologies, such as pyrolysis, carbonization, gasification, torrefaction, and rectification, along with physical, chemical, and biological modifications that are crucial for efficient pollutant removal. The core of this review focuses on biochar's applications in removing a wide range of pollutants from wastewater, detailing mechanisms for organic pollutants, inorganic salts, pharmaceutical contaminants, microplastics, nanoplastics, and volatile organic compounds. In addition, the review introduces machine learning as a key technique for optimizing biochar production and functionality, showcasing its potential in advancing biochar technology. The conclusion provides a comprehensive outlook on biochar's future, emphasizing ongoing research and its role in sustainable environmental management.

Recent advances and factors affecting the adsorption of nano/microplastics by magnetic biochar

The increase in nano/microplastics (NPs/MPs) from various everyday products entering aquatic environments highlights the urgent need to develop mitigation strategies. Biochar (BC), known for its excellent adsorption capabilities, can effectively target various harmful organic and inorganic pollutants. However, traditional methods involving powdered BC necessitate centrifugation and filtration, which can lead to the desorption of pollutants and subsequent secondary pollution. Magnetic biochar (MBC) offers a solution that facilitates straightforward and rapid separation from water through magnetic techniques. This review provides the latest insights into the progress made in MBC applications for the adsorption of NPs/MPs. This review further discusses how external factors such as pH, ionic strength, temperature, competing ions, dissolved organic matter, aging time, and particle size impact the MBC adsorption efficiency of MPs. The use of machine learning (ML) for optimizing the design and properties of BC materials is also briefly addressed. Finally, this review addresses existing challenges and future research directions aimed at improving the large-scale application of MBC for NPs/MPs removal.

Synergistic activation of peroxymonosulfate by 3D CoNiO2/Co core-shell structure biochar catalyst for sulfamethoxazole degradation

In this study, a 3D CoNiO2/Co core-shell structure biochar catalyst derived from walnut shell was synthesized by hydrothermal and ion etching methods. The prepared BC@CoNi-600 catalyst exhibited exceptional peroxymonosulfate (PMS) activation. The system achieved 100 % degradation of sulfamethoxazole (SMX). The reactive oxygen species in the BC@CoNi-600/PMS system included SO4-, OH, and O2-. Density functional theory calculations explored the synergistic effects between nickel-cobalt bimetallic and carbon matrix during PMS activation. The unique 3D core-shell structure of BC@CoNi-600 features an outer nickel-cobalt bimetallic layer with exceptional PMS adsorption capacity, while protecting the zero-valence Co of the inner layer from oxidation. Based on the experimental-data, machine learning modeling mechanism, and information theory, a nonlinear modeling method was proposed. This study utilizes a machine learning approach to investigate the degradation of SMX in complex aquatic environments. This study synthesized a novel biochar-based catalyst for activated PMS and provided unique insights into its environmental applications.

Predicting maturity and identifying key factors in organic waste composting using machine learning models

The measurement of germination index (GI) in composting is a time-consuming and laborious process. This study employed four machine learning (ML) models, namely Random Forest (RF), Artificial Neural Network (ANN), Support Vector Regression (SVR), and Decision Tree (DT), to predict GI based on key composting parameters. The prediction results showed that the coefficient of determination (R2) for RF (>0.9) and ANN (>0.9) was higher than SVR (<0.6) and DT (<0.8), suggesting that RF and ANN displayed superior predictive performance for GI. The SHapley additive exPlanations value result indicated that composting time, temperature, and pH were the important features contributing to GI. Composting time was found to have the most significant impact on GI. Overall, RF and ANN were suggested as effective tools for predicting GI in composting. This study offers the reliable approach of accurately predicting GI in composting processes, thereby enabling intelligent composting practices.