Biomass waste-derived porous carbons (BWDPCs) are a class of complex materials that are widely used in sustainable waste management and carbon capture. However, their diverse textural properties, the presence of various functional groups, and the varied temperatures and pressures to which they are subjected during CO 2 adsorption make it challenging to understand the underlying mechanism of CO 2 adsorption. Here, we compiled a data set including 527 data points collected from peer-reviewed publications and applied machine learning to systematically map CO 2 adsorption as a function of the textural and compositional properties of BWDPCs and adsorption parameters. Various tree-based models were devised, where the gradient boosting decision trees (GBDTs) had the best predictive performance with R 2 of 0.98 and 0.84 on the training and test data, respectively. Further, the BWDPCs in the compiled data set were classified into regular porous carbons (RPCs) and heteroatom-doped porous carbons (HDPCs), where again the GBDT model had R 2 of 0.99 and 0.98 on the training and 0.86 and 0.79 on the test data for the RPCs and HDPCs, respectively. Feature importance revealed the significance of adsorption parameters, textural properties, and compositional properties in the order of precedence for BWDPC-based CO 2 adsorption, effectively guiding the synthesis of porous carbons for CO 2 adsorption applications.

Carbon dioxide (CO 2 ) is the main anthropogenic greenhouse gas contributing to global 29 warming, causing tremendous impacts on the global ecosystem. Fossil fuel combustion is the 30 main anthropogenic source of CO 2 emissions. Biochar, a porous carbonaceous material 31 produced through the thermochemical conversion of organic materials in oxygen-depleted 32 conditions, is emerging as a cost-effective green sorbent to maintain environmental quality by 33 capturing CO 2 . Currently, the modification of biochar using different physico-chemical 34 processes, as well as the synthesis of biochar composites to enhance the contaminant sorption 35 capacity, has drawn significant interest from the scientific community, which could also be 36 used for capturing CO 2 . This review summarizes and evaluates the potential of using pristine 37 and engineered biochar as CO 2 capturing media, as well as the factors influencing the CO 2 38 adsorption capacity of biochar and issues related to the synthesis of biochar-based CO 2 39 adsorbents. The CO 2 adsorption capacity of biochar is greatly governed by physico-chemical 40 properties of biochar such as specific surface area, microporosity, aromaticity, 41 hydrophobicity and the presence of basic functional groups which are influenced by 42 feedstock type and production conditions of biochar. Micropore area (R 2 = 0.9032, n=32) and 43 micropore volume (R 2 = 0.8793, n=32) showed a significant positive relationship with CO 2 44 adsorption capacity of biochar. These properties of biochar are closely related to the type of 45 feedstock and the thermochemical conditions of biochar production. Engineered biochar 46 significantly increases CO 2 adsorption capacity of pristine biochar due to modification of 47 surface properties. Despite the progress in biochar development, further studies should be 48 conducted to develop cost-effective, sustainable biochar-based composites for use in large-49 scale CO 2 capture.

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Biochar application is a promising strategy for the remediation of contaminated soil, while ensuring sustainable waste management. Biochar remediation of heavy metal (HM)-contaminated soil primarily depends on the properties of the soil, biochar, and HM. The optimum conditions for HM immobilization in biochar-amended soils are site-specific and vary among studies. Therefore, a generalized approach to predict HM immobilization efficiency in biochar-amended soils is required. This study employs machine learning (ML) approaches to predict the HM immobilization efficiency of biochar in biochar-amended soils. The nitrogen content in the biochar (0.3–25.9%) and biochar application rate (0.5–10%) were the two most significant features affecting HM immobilization. Causal analysis showed that the empirical categories for HM immobilization efficiency, in the order of importance, were biochar properties > experimental conditions > soil properties > HM properties. Therefore, this study presents new insights into the effects of biochar properties and soil properties on HM immobilization. This approach can help determine the optimum conditions for enhanced HM immobilization in biochar-amended soils.