Predicting the corrosion rate for soil-buried steel is significant for assessing the service-life performance of structures in soil environments. However, due to the large amount of variables involved, existing corrosion prediction models have limited accuracy for complex soil environment. The present study employs three machine learning (ML) algorithms, i.e., random forest, support vector regression, and multilayer perception, to predict the corrosion current density of soil-buried steel. Steel specimens were embedded in soil samples collected from different regions of the Wisconsin state. Variables including exposure time, moisture content, pH, electrical resistivity, chloride, sulfate content, and mean total organic carbon were measured through laboratory tests and were used as input variables for the model. The current density of steel was measured through polarization technique, and was employed as the output of the model. Of the various ML algorithms, the random forest (RF) model demonstrates the highest predictability (with an RMSE value of 0.01095 A/m² and an R² value of 0.987). In light of the feature selection method, the electrical resistivity is identified as the most significant feature. The combination of three features (resistivity, exposure time, and mean total organic carbon) is the optimal scenario for predicting the corrosion current density of soil-buried steel.

This research aimed to apply different models on electrochemical impedance spectroscopy to study the influence of grain refinement and straining obtained from surface mechanical attrition treatment on the passivation of carbon steel in an alkaline solution at open circuit and different passivating potentials. Based on the power law model, the graphical presentation of capacitances at high frequencies and the SIMPLEX method at low frequencies were used to evaluate the electrochemical properties of the interface. The point defect model was used to estimate point defects diffusivity of the passive film in the passivating potential range. Results showed the oxide layer formed on a more strained and grain refined surface was thinner but less porous.

The rise in population, urbanization, and industrial developments have led to a substantial increase in waste generation and energy demand, posing significant challenges for waste management as well as energy conservation and production. Bioenergy conversions have been merged as advanced, sustainable, and integrated solutions for these issues, encompassing energy generation and waste upcycling of different types of organic waste. Municipal solid waste (MSW) and agricultural residues (AR) are two main resources for bioenergy conversions. Bioenergy production involves feedstock deconstruction and the conversion of platform chemicals to energy products. This review provides a detailed overview of waste sources, biofuel, and bioelectricity production from fermentation and microbial fuel cell (MFC) technology, and their economic and environmental perspectives. Fermentation plays a critical role in liquid biofuel production, while MFCs demonstrate promising potential for simultaneous production of electricity and hydrogen. Fermentation and MFCs hold a significant potential to be integrated into a single pipeline, enabling the conversion of organic matter, including a variety of waste material and effluent, into diverse forms of bioenergy via microbial cultures under mild conditions. Furthermore, MFCs are deemed a promising technology for pollutant remediation, reducing COD levels while producing bioenergy. Importantly, the consolidated fermentation–MFC system is projected to produce approximately 7.17 trillion L of bioethanol and 6.12 × 10⁴ MW/m² of bioelectricity from MSW and AR annually, contributing over USD 465 billion to the global energy market. Such an integrated system has the potential to initiate a circular economy, foster waste reduction, and improve waste management practices. This advancement could play a crucial role in promoting sustainability across the environmental and energy sectors.

Biomass quality is influenced by water’s abundance, distribution, and status in relation to other chemical species within the polymer matrix. Water interacts with polymers that make up the cell walls, and these interactions govern the physical and chemical changes that occur during the storage and preprocessing of biomass feedstocks. Time-domain nuclear magnetic resonance (TD-NMR) was employed to explore variations in the physical constraints of water within the lignocellulosic microstructure in distinct anatomical fractions of biomass and as a function of biological degradation. The Carr–Purcell–Meiboom–Gill sequence, when combined with knowledge of the chemical composition and physical structure of pine residues and corn stover anatomical fractions, gives an accurate measurement of the bound and free water. In this work, the impacts of storage and biological degradation were investigated to elucidate changes in the status and distribution of water within distinct plant tissues. We also investigate how degradation during storage affects water interactions in different pine residues (e.g., bark, branch, and needle) and corn stover (e.g., cob, leaf, and stalk) anatomical fractions using transverse relaxation times (T₂). As demonstrated herein, TD-NMR provides quantitative data on lignocellulosic biomass–water interactions within anatomical fractions, which can further aid in the investigation of preprocessing effects on feedstock quality. Our findings suggest that biological heating enhances biomass–water interactions at the cellular and macromolecular scale. In addition, analysis of three-dimensional scanning electron microscopy reconstructions indicates that surface roughness wavelengths align with microscale roughness, suggesting that pine forestry residue and corn stover particles have primarily hydrophobic exterior surfaces. This study offers multiscale insights into understanding the microstructure, wettability, and chemical environment that dictate diffusion, enzyme access, and recalcitrance of lignocellulosic biomass.