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We studied the biochemical and anaerobic degradation characteristics of 29 types of materials to evaluate the effects of a physical composition classification method for degradable solid waste on the computation of anaerobic degradation parameters, including the methane yield potential (L0), anaerobic decay rate (k), and carbon sequestration factor (CSF). Biochemical methane potential tests were conducted to determine the anaerobic degradation parameters of each material. The results indicated that the anaerobic degradation parameters of nut waste were quite different from those of other food waste and nut waste was classified separately. Paper was subdivided into two categories according to its lignin content: degradable paper with lignin content of <0.05 g g VS(-1), and refractory paper with lignin content >0.15 g g VS(-1). The L0, k, and CSF parameters of leaves, a type of garden waste, were similar to those of grass. This classification method for degradable solid waste may provide a theoretical basis that facilitates the more accurate calculation of anaerobic degradation parameters.

Plastics are used extensively and provide great convenience in daily life. However, their stable and nonbiodegradable nature incurs challenging threats to the environment and ecosystems. It is essential that a sustainable method for plastic treatment and utilization be developed. We used low-density polyethylene (LDPE) as a precursor to synthesize a hierarchical porous carbon (HPC) through autogenic pressure carbonization followed by potassium hydroxide (KOH) activation. The noncatalytic carbonization in a closed system obtained 45% carbon residues from LDPE, which would not yield any carbon residues under normal pressure. The following KOH activation developed hierarchical porous structures in the carbon materials, which can be controlled by KOH dosage. The mechanism of carbonization and activation was proposed considering the nanostructure of carbon materials. The obtained HPC exhibited a micrometer-scale carbon sphere morphology with hierarchical pores, a large specific surface area of 3059 m2 g–1, and abundant surface functional groups. By acting as an electrode material for supercapacitors, the HPC displayed excellent electrochemical performance with a specific capacitance of 355 F g–1 at a current density of 0.2 A g–1 in 6 M KOH electrolyte, a high energy density of 9.81 W h kg–1 at a power density of 450 W kg–1, and an outstanding cycling stability. This research develops a sustainable way for plastic waste utilization and a green approach for HPC synthesis.

Engineering robust or specialized microbial ecosystems by regulating the operating temperatures of anaerobic digestion (AD) has not yet received enough attention. Further, the critical temperature range for restructuring the structure and function of microbial community has not been assessed. In this study, batch AD experiments were conducted along a fine-scale 3 °C temperature gradient of 26–65 °C. Stable performance of AD was observed at 26–41 and 50–56 °C. However, sudden performance deterioration was observed at 47 and 59 °C, accompanied by a severe reconstruction of the microbial community and metagenomics-indicated functional dynamics. These effects were particularly observed during methanogenesis qualitatively and quantitatively, suggesting that 47 and 59 °C were the critical temperatures from mesophilic to thermophilic temperatures and from thermophilic to hyperthermophilic temperatures, respectively. Contrary to most prior studies, hydrogenotrophs also dominated at mesophilic temperatures, which suggested that the primary methanogenesis pathway shifted from acetoclastic and hydrogenotrophic to hydrogenotrophic, with the temperature shifting from mesophilic to thermophilic. The present study suggests that modulating operating temperatures to shape the microecosystem of the AD system is an efficient strategy, and avoiding a temperature pitfall at approximately 47 or 59 °C is imperative.

Traditional methods for analyzing the biogenic and fossil carbon shares in solid waste are time-consuming and labor-intensive. A novel approach was developed to directly classify the carbon group and predict carbon content using the hyperspectral imaging (HSI) spectra of solid waste in conjunction with state-of-the-art tree-based machine learning models, including random forest (RF), extreme gradient boost, and light gradient boost machine (LGBM). All of the classifiers and regressors were able to achieve an accuracy above 0.95 and an R 2 of 0.96 in the test set, respectively. In addition, two model interpretation approaches, the Shapley additive explanation and model explainer, were applied. The results showed that the predictions of the developed models were based on a reasonable understanding of the overtone and shake of the functional groups (C–H, N–H, and O–H). Furthermore, the developed models were validated by an external test set, which did not overlap with the data used for model construction. The RF and LGBM showed robust performance with a 0.790 accuracy for carbon group classification and a 0.806 R 2 for carbon content prediction. Overall, the optimal models provided a rapid method for characterizing the biogenic carbon share in solid waste based on raw HSI spectra without preprocessing.