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IMAGE: Realization of far-field, label-free super-resolution imaging based on evanescent waves excited by nonlinear four-wave mixing. view more
Credit: Zhou et al., doi 10.1117/1.AP.3.2.025001
The diffraction limit, also known as Abbe diffraction limit in optics, poses a great challenge in many systems that involve wave dynamics, such as imaging, astronomy, and photolithography. For example, the best optical microscope only possesses resolution around 200 nm, but the physical size of the photolithography process with an excimer laser is around tens of nanometers. Meanwhile, physical sizes in current research and applications in biology and the semiconductor industry have scaled down to several nanometers, which is far beyond the ability of optical waves. According to the Abbe theory, subwavelength features are usually associated with evanescent waves, which decay exponentially with distance from the target. In response to this problem, researchers have develope
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Credit: RMIT University
A new study, out this week, could pave the way to revolutionary, transparent electronics.
Such see-through devices could potentially be integrated in glass, in flexible displays and in smart contact lenses, bringing to life futuristic devices that seem like the product of science fiction.
For several decades, researchers have sought a new class of electronics based on semiconducting oxides, whose optical transparency could enable these fully-transparent electronics.
Oxide-based devices could also find use in power electronics and communication technology, reducing the carbon footprint of our utility networks.
A RMIT-led team has now introduced ultrathin beta-tellurite to the two-dimensional (2D) semiconducting material family, providing an answer to this decades-long search for a high mobility p-type oxide.
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IMAGE: Figure 1. Schematic diagram of the M3I3 Flagship Project. This project aims to achieve the seamless integration of the multiscale structure-property and processing-property relationships via materials modeling, imaging, and machine. view more
Credit: KAIST
Developing new materials and novel processes has continued to change the world. The M3I3 Initiative at KAIST has led to new insights into advancing materials development by implementing breakthroughs in materials imaging that have created a paradigm shift in the discovery of materials. The Initiative features the multiscale modeling and imaging of structure and property relationships and materials hierarchies combined with the latest material-processing data.
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IMAGE: Estimating the variance of the number of clusters and the sample size for which it is maximum can give us an estimate of the total number of clusters for the. view more
Credit: Ryo Maezono from JAIST.
Ishikawa, Japan - Any high-performance computing should be able to handle a vast amount of data in a short amount of time an important aspect on which entire fields (data science, Big Data) are based. Usually, the first step to managing a large amount of data is to either classify it based on well-defined attributes or as is typical in machine learning cluster them into groups such that data points in the same group are more similar to one another than to those in another group. However, for an extremely large dataset, which can have trillions of sample points, it is tedious to even group data points into a single cluster without huge memory requirements.