Informative, innovative and interesting articles from our favorite blogs
- , Argonne National Laboratory, March 25, 2019, Evaluation Engineering -- A new approach to molecular modeling may accelerate the development of new organic materials for electronics. Organic electronics are more cost-effective and have more versatility than inorganic electronics. For instance, the flexibility of organic electronics could allow companies to print them like paper or incorporate them into clothing to power wearable devices. However, there is some difficulty controlling their electronic structure. To address this, researchers have developed a faster way of creating molecular models by using machine learning. The team’s models accelerate the screening of potential new organic materials. The research focuses on vapor deposition as a means to assemble materials for organic electronics. Scientists evaporate an organic molecule and allow it to condense on a surface, producing a film. Then the team manipulates certain deposition conditions, which enables them to tune the way the molecules pack in the film, like a game of Tetris. To study this effect and to optimize device performance, the team ran detailed computer simulations. For the full article check out Evaluation Engineering.
- , Far Eastern Federal University, March 25, 2019, Wireless Design & Development -- Researchers predict that in the next 15 to 20 years, hypersensitive sensors operating under the magnetoresistive principle will be applied in many innovative areas. In a recently released scientific journal, a team of scientists and experts identified the most promising application areas for magnetoresistive sensors: flexible electronics, biomedicine, position sensors, human-computer interaction, various types of monitoring and navigation, and autonomous transport. The team carried out comprehensive analytical work and drew up development roadmaps for the sensor industry. They also outlined the most probable ways to commercialize the results in this area. Magnetoresistive sensors are highly sensitive, have low costs, low power consumption and are compact. Magnetoresistive technology is especially promising in flexible portable electronic devices like smartphones. Devices operating on such sensors can withstand a large number of extensions and stretching cycles without loss of sensitivity properties. By 2030 the team predicts it will even be possible to print the sensors on paper and textiles. For the full article visit Wireless Design & Development.
- , The Engineer, March 25, 2019, Wonderful Engineering -- Stanford researchers have developed a system that can convert sea water to hydrogen fuel. The concept involving saltwater, electrodes and solar power, shows separation of hydrogen and oxygen gas from seawater by using electricity. Using saltwater can be challenging because it can corrode electrodes during the process, greatly reducing the lifespan of the system. The team countered this by introducing a change in materials. They discovered that if the anode was covered with negative charges, the resulting layer repelled chloride and changed the rate of decay in the material that was underneath it. The researchers developed a nickel foamcore and then made layers of nickel-ion hydroxide and nickel sulfide. The foam served as a conductor and the nickel-ion hydroxide began the electrolysis. The team’s system operated for more than a thousand hours, whereas similar systems only operate for 12 hours. This concept has multiple possibilities; for instance, a deep-sea diver could stay under water for an unlimited amount of time. For more information check out Wonderful Engineering.
- , Stanford University, March 25, 2019, TechXplore -- A new technique could be used to sort manufactured cells and to help new battery designs reach the market more quickly. If manufacturers of cell-phone batteries could tell which cells will last at least two years, then they could sell only those to phone makers and send the rest to makers of less demanding devices. Researchers combined comprehensive experimental data with AI to reveal the key for accurately predicting the useful life of lithium-ion batteries before their capacities start to wane. After the team trained their machine learning model with a few hundred million data point batteries charging and discharging, the algorithm predicted how many more cycles each battery would last. The algorithm categorized batteries as either long or short life expectancy based on just the first five charge/discharge cycles. The predictions were correct 95 percent of the time. For the full article check out TechXplore.
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