Ulsan National Institute of Science and Technology

Tandem catalysis involves two or more catalysts arranged in proximity within a single reaction vessel, with the aim of synergistically aligning the catalysts’ reaction pathways to maximize overall system performance. This study presents a proof of concept showing the integration of continuum transport modeling with design optimization in a simplified two-dimensional flow reactor setup for electrochemical CO 2 reduction. Ag catalysts provide the CO 2 ⟶ CO reaction capability, and Cu catalysts provide the CO ⟶ high-value products reaction capability. Given a set of input parameters, the optimization algorithm uses adjoint methods to modify the Ag/Cu surface patterning in order to maximize the current density toward high-value products, such as ethylene. The optimized designs yield significant performance enhancement especially at more negative applied voltages (i.e., stronger surface reactions) and for larger numbers of patterning sections. For an applied voltage of −1.7 V vs. SHE, the 12-section optimized design increases the current density toward ethylene by up to 65% compared to the unoptimized 2-section design. For the optimized cases, observed differences in the production and consumption of CO (the key intermediate species) and minimized zones of low CO reactant surface concentration on Cu sections explain the improved reactor performance.

Although coal remains essential for global energy production, the inevitable self-heating within coal piles poses significant economic and safety risks. Existing thermal anomaly detection methods based on low-resolution (LR) infrared (IR) imaging often fail to identify subtle thermal signatures, especially across large storage areas. To address these limitations, we systematically implemented guided depth super-resolution (GDSR), specifically tailored for coal pile monitoring. Due to the scarcity of realistic field data, we developed a novel testbed using gravel piles embedded with controlled heaters to simulate real-world conditions. Multi-modal images were captured using synchronized RGB and IR sensors. We established a framework evaluated on both synthetic testbed datasets and real-world coal-field data to ensure practical applicability. In this study, we propose a novel Edge-Guided Thermal Super-Resolution (EGTSR) network, benchmarked against representative methods such as the efficient Discrete Cosine Transform Network (DCTNet) and the accuracy-oriented Structure-Guided Network (SGNet). Crucially, EGTSR integrates a segmentation-guided alignment module to explicitly correct spatial discrepancies, which enables the network to maximize the utilization of gradient-based structural guidance from RGB images. Experimental results demonstrate that the proposed EGTSR outperforms existing methods, achieving up to a 31% reduction in root mean squared error compared to the baseline. This AI-based framework significantly improves thermal imaging resolution and detection accuracy, offering safer and more efficient coal storage management.