A particle scale micro-CT approach for 3D in-situ visualizing the Pb (II) adsorption in different crop residue-derived chars

A novel micro-CT approach for in situ visualization of the spatial dynamics of mesovoids in aerobic composting piles

The spatial configuration of mesovoids profoundly affects the aerobic composting microenvironment, which governs vital processes such as greenhouse gas production and emission, thermal conduction, and overall composting efficiency. Nondestructive in-situ characterization of the composting spatial structure is crucial to better understand its interaction mechanism with the microenvironment. In this study, a valuable contribution to the field of composting research was made by introducing micro-computed tomography (micro-CT) tool for in situ three-dimensional (3D) visual characterizing the void structure dynamics of straw and manure compost pile units at the mesoscale. Representative samples at different composting stages derived from wheat straw and cow manure were procured by pre-embedding samplers in laboratory-based aerobic composting reactor systems. Based on an advanced Skyscan 1275 micro-CT system, scanning conditions and image processing algorithms were determined, and the void structure and their dynamic changes in the pile unit during composting were in-situ 3D visualized for the first time. The micro-CT images effectively reveal well-developed void structures exhibiting spatiotemporal dynamics during composting, and they exhibit excellent consistency with conventional macrophysical effects and wet chemical analyses. Micro-CT quantification results of the void structure parameters changes in pile unit during composting were as follows: percentage of the total voidage and the connected voidage in pile unit were in the range of 52.34%-58.56%, indicating a very suitable composting spatial structural microenvironment. This new micro-CT method provides a valuable perspective for analyzing and understanding the complex aerobic composting process.

In-situ registration subtraction image segmentation algorithm for spatiotemporal visualization of copper adsorption onto corn stalk-derived pellet biochar by micro-computed tomography

The non-homogeneous structure and high-density ash composition of biochar matrix pose significant challenges in characterizing the dynamic changes of heavy metal adsorption onto biochar with micro-computed tomography (Micro-CT). A novel in-situ registration subtraction image segmentation method (IRS) was developed to enhance micro-CT characterization accuracy. The kinetics of Cu(II) adsorption onto pellet biochar derived from corn stalks were tested. Respectively, the IRS and traditional K-means algorithms were used for image segmentation to the in-situ three-dimensional (3D) visual characterization of the Cu(II) adsorption onto biochar. The results indicated that the IRS algorithm reduced interference from high-density biochar composition, and thus achieved more precise results (R2 = 0.95) than that of K-means (R2 = 0.72). The visualized dynamic migration of Cu(II) from surface adsorption to intraparticle diffusion reflexed the complex mechanism of heavy metal adsorption. The developed Micro-CT method with high generalizability has great potential for studying the process and mechanism of biochar heavy metal adsorption.

Machine learning applications for biochar studies: A mini-review

Biochar is a promising carbon sink whose application can assist in reducing carbon emissions. Development of this technology currently relies on experimental trials, which are time-consuming and labor-intensive. Machine learning (ML) technology presents a potential solution for streamlining this process. This review summarizes the current research on ML's applications in biochar production, characterization, and applications. It briefly explains commonly used machine learning algorithms and discusses prospects and challenges. A hybrid model that combines ML with mechanism-based analysis could be a future trend, addressing the ML's black-box nature. While biochar studies have adopted ML technology, current works mostly use lab-scale data for model training. Further work is needed to develop ML models based on pilot or industrial-scale data to realize the use of ML techniques for the field application of biochar.