Most of that sediment then gets dumped right back in the water: either in the Gulf of Mexico or in deeper parts of the same waterway, where the current is expected to carry it downstream and away from the dredged area. By law, the Corps is required to do the most cost-effective thing with the dredged material, which limits the options on its use.
Researchers at Washington State University have created materials that react to temperature and ultraviolet light. It's a look into what mass-produced smart materials could be. Smart materials are a growing area of interest for researchers, with potential for instantly forming casts and self-healing phone screens. But this is the first time memory, light-activated movement, and self-healing have been combined. One of the reasons smart materials aren't widespread yet is their single-use capability.
The long-term persistence of many synthetic materials and the resulting impact on the environment has made clear the importance of developing new routes to creating sustainable materials (1). This is especially true for man-made polymers, for which slow (or lack of) degradation is widely problematic, from plastic wastes generated by commodity packaging to high-tech electronics. Despite threats to human health, wildlife, oceans, and landfills, the fraction of polymeric materials that are recycled remains low. Polymers designed to degrade after their intended use represent a promising, chemistry-driven approach to minimize the impact of persistent, petroleum-derived materials (2). An alternative strategy for preparing sustainable materials is to design polymers that have even longer life spans and, as a result, need to be replaced less frequently.
Artificial Intelligence (AI) could help promote the development of material science and accelerate the invention of new materials, according to Chinese experts. Many key and core technologies that need breakthroughs in China are related to the material science, and AI could help in these areas, Zhao Zhongxian, an academician of the Chinese Academy of Sciences (CAS), who won China's top science award, said at a science forum opened in Dongguan, Guangdong Province, Monday. Traditional methods for material composition analysis are time-consuming and expensive. It takes an average of 10 years for a laboratory to develop new materials and 20 years for mass production. With AI technology, the development and application cycle of new materials is expected to be shortened by more than half.
Scientists from the School of Energy and Power Engineering, Chongqing University, China, have discovered a highly efficient, time saving as well as a reliable machine learning (ML) method for the research and development of novel organic photovoltaic (OPV) materials. During the development of high performing OPV materials, if one can pre-establish the correlation between the structure of the designed material and its photovoltaic property, it becomes highly meaningful and time saving. The research is reported in the journal Science Advances. OPV cells are an easy and highly economical method for transforming the solar energy into electrical energy. Until now, the typical OPV materials-based research has focused on building a relationship between the newly developed OPV molecular material and its organic photovoltaic material properties.