In the last century, conventional strategies pursued to reduce or convert CO have shown limitations and, consequently, have been pushing the development of innovative routes. Among them, great efforts have been made in the field of heterogeneous electrochemical CO conversion, which boasts the use of mild operative conditions, compatibility with renewable energy sources, and high versatility from an industrial point of view. Indeed, since the pioneering studies of Hori and co-workers, a wide range of electrocatalysts have been designed. Starting from the performances achieved using traditional bulk metal electrodes, advanced nanostructured and multi-phase materials are currently being studied with the main goal of overcoming the high overpotentials usually required for the obtainment of reduction products in substantial amounts. This review reports the most relevant examples of metal-based, nanostructured electrocatalysts proposed in the literature during the last 40 years. Moreover, the benchmark materials are identified and the most promising strategies towards the selective conversion to high-added-value chemicals with superior productivities are highlighted.
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http://dx.doi.org/10.3390/nano13111723 | DOI Listing |
Angew Chem Int Ed Engl
January 2025
Nanjing Tech University, College of Chemical Engineering, Nanjing, CHINA.
The wide application of zeolite Y in petrochemical industry is well known as one of the milestones in zeolite chemistry and heterogeneous catalysis. However, the traditional organic-free synthesis typically produces (hydro)thermally unstable zeolite Y with Si/Al atomic ratio (SAR) less than 2.5.
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January 2025
School of Computer Science and Engineering, Central South University, Changsha 410083, China; Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China.
Exploring the associations between microbes and drugs offers valuable insights into their underlying mechanisms. Traditional wet lab experiments, while reliable, are often time-consuming and labor-intensive, making computational approaches an attractive alternative. Existing similarity-based machine learning models for predicting microbe-drug associations typically rely on integrated similarities as input, neglecting the unique contributions of individual similarities, which can compromise predictive accuracy.
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January 2025
Department of Civil Engineering, Myongji College, Seoul 03656, Republic of Korea.
Conventional approaches for the structural health monitoring of infrastructures often rely on physical sensors or targets attached to structural members, which require considerable preparation, maintenance, and operational effort, including continuous on-site adjustments. This paper presents an image-driven hybrid structural analysis technique that combines digital image processing (DIP) and regression analysis with a continuum point cloud method (CPCM) built on a particle-based strong formulation. Polynomial regressions capture the boundary shape change due to the structural loading and precisely identify the edge and corner coordinates of the deformed structure.
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January 2025
College of Energy and Power Engineering, Xihua University, Chengdu 610039, China.
Artificial intelligence (AI) technologies have been widely applied to the automated detection of pipeline leaks. However, traditional AI methods still face significant challenges in effectively detecting the complete leak process. Furthermore, the deployment cost of such models has increased substantially due to the use of GPU-trained neural networks in recent years.
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January 2025
Department of Electronics and Electrical Engineering, Faculty of Science and Technology, Keio University, 3-14-1, Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan.
Traditional Vision-and-Language Navigation (VLN) tasks require an agent to navigate static environments using natural language instructions. However, real-world road conditions such as vehicle movements, traffic signal fluctuations, pedestrian activity, and weather variations are dynamic and continually changing. These factors significantly impact an agent's decision-making ability, underscoring the limitations of current VLN models, which do not accurately reflect the complexities of real-world navigation.
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