In the long term, coal will remain a competitive resource in the thermal power sector, primarily due to its abundant global reserves and low costs. Despite numerous factors, including significant environmental concerns, the global share of coal power generation has remained at 40% over the past four decades. Efficient and clean coal combustion is a high priority wherever coal is used as a fuel. An improved low-power boiler design has been proposed to enhance efficiency during fixed-bed coal combustion. This design reduces harmful emissions into the atmosphere by optimizing parameters and operating modes. In this study, mathematical modeling of gas velocity and temperature distribution during fixed-bed coal combustion was conducted for a conventional grate system and an improved grate-free system. Experimental methods were employed to develop descriptive airflow models in the fixed coal layer, considering nozzle diameter and air supply pressure in the furnace chamber without a grate system. Comparative evaluations of fixed-bed coal combustion rates were performed using an experimental laboratory setup with both grate and grate-free stove systems.

The strategic manipulation of the interaction between a central metal atom and its coordinating environment in single-atom catalysts (SACs) is crucial for catalyzing the CO₂ reduction reaction (CO₂RR). However, it remains a major challenge. While density-functional theory calculations serve as a powerful tool for catalyst screening, their time-consuming nature poses limitations. This paper presents a machine learning (ML) model based on easily accessible intrinsic descriptors to enable rapid, cost-effective, and high-throughput screening of efficient SACs in complex systems. Our ML model comprehensively captures the influences of interactions between 3 and 5d metal centers and 8 C, N-based coordination environments on CO₂RR activity and selectivity. We reveal the electronic origin of the different activity trends observed in early and late transition metals during coordination with N atoms. The extreme gradient boosting regression model shows optimal performance in predicting binding energy and limiting potential for both HCOOH and CO production. We confirm that the product of the electronegativity and the valence electron number of metals, the radius of metals, and the average electronegativity of neighboring coordination atoms are the critical intrinsic factors determining CO₂RR activity. Our developed ML models successfully predict several high-performance SACs beyond the existing database, demonstrating their potential applicability to other systems. This work provides insights into the low-cost and rational design of high-performance SACs.

Flexible strain sensors have received tremendous attention because of their potential applications as wearable sensing devices. However, the integration of key functions into a single sensor, such as high stretchability, low hysteresis, self-adhesion, and excellent antifreezing performance, remains an unmet challenge. In this respect, zwitterionic hydrogels have emerged as ideal material candidates for breaking through the above dilemma. The mechanical properties of most reported zwitterionic hydrogels, however, are relatively poor, significantly restricting their use under load-bearing conditions. Traditional improvement approaches often involve complex preparation processes, making large-scale production challenging. Additionally, zwitterionic hydrogels prepared with chemical crosslinkers are typically fragile and prone to irreversible deformation under large strains, resulting in the slow recovery of structure and function. To fundamentally enhance the mechanical properties of pure zwitterionic hydrogels, the most effective approach is the regulation of the chemical structure of zwitterionic monomers through a targeted design strategy. This study employed a novel zwitterionic monomer carboxybetaine urethane acrylate (CBUTA), which contained one urethane group and one carboxybetaine group on its side chain. Through the direct polymerization of ultrahigh concentration monomer solutions without adding any chemical crosslinker, we successfully developed pure zwitterionic supramolecular hydrogels with significantly enhanced mechanical properties, self-adhesive behavior, and antifreezing performance. Most importantly, the resultant zwitterionic hydrogels exhibited high tensile strength and toughness and displayed ultralow hysteresis under strain conditions up to 1100%. This outstanding performance was attributed to the unique liquid–liquid phase separation phenomenon induced by the ultrahigh concentration of CBUTA monomers in an aqueous solution, as well as the enhanced polymer chain entanglement and the strong hydrogen bonds between urethane groups on the side chains. The potential application of hydrogels in strain sensors and high-performance triboelectric nanogenerators was further explored. Overall, this work provides a promising strategy for developing pure zwitterionic hydrogels for flexible strain sensors and self-powered electronic devices.