Vishal K. Sharma

Electron–phonon coupling is fundamental to condensed-matter physics, governing various physical phenomena and properties in both conventional and quantum materials. Here we propose and demonstrate two-dimensional electron–phonon coupling spectroscopy that can directly extract the electron–phonon coupling matrix elements for specific phonon modes and different electron energies. Using this technique, we measure the electron energy dependence of the electron–phonon coupling strength for individual phonon modes. It allows us to identify distinct signatures distinguishing non-local Su–Schrieffer–Heeger-type couplings from local Holstein-type couplings. Applying this methodology to a methylammonium lead iodide perovskite, we reveal particularly different properties, for example, temperature dependence or anisotropy, of the electron–phonon couplings of two pronounced phonon modes. Our approach provides insights into the microscopic origin of the electron–phonon coupling and has potential applications in phonon-mediated ultrafast control material properties.

The deployment of wireless sensor networks (WSNs) into the environment can increase the awareness of the environment in most cases but it is usually restricted by limited energy and computing resources in the network. To address this limitation, cloud infrastructure can be used as a platform for location optimization. With effective location optimization, the cloud infrastructure can use its computing capabilities to minimize the number of sensor displacements while maintaining the desired coverage of the WSN. This will allow the WSN nodes to extend the lifetime and make more efficient use of their limited energy and computing resources while still providing the necessary coverage. Location optimization algorithms can be used to determine the optimal locations for the network nodes. These algorithms consider the spatial characteristics of the environment and prioritize the placement of nodes based on their expected utility. Several methods have been proposed for location optimization based on the cloud infrastructure, such as Reinforcement Learning, Differential Evolution, and Swarm Optimization techniques. These techniques were compared in terms of their accuracy and scalability in optimizing a WSN's coverage performance. The results showed that Differential Evolution outperformed the other techniques in terms of accuracy and Swarm Optimization was the best for scalability. The use of the cloud for location optimization for WSNs can help extend the lifetime of the network and provide a viable solution for long-term data collection.