Silicon is considered as a promising anode material for the next-generation lithium-ion battery (LIB) due to its high capacity at nanoscale. However, silicon expands up to 300% during lithiation, which induces high stresses and leads to fractures. To design silicon nanostructures that could minimize fracture, it is important to understand and characterize stress states in the silicon nanostructures during lithiation. Synchrotron X-ray microdiffraction has proven to be effective in revealing insights of mechanical stress and other mechanics considerations in small-scale crystalline structures used in many important technological applications, such as microelectronics, nanotechnology, and energy systems. In the present study, an in situ synchrotron X-ray microdiffraction experiment was conducted to elucidate the mechanical stress states during the first electrochemical cycle of lithiation in single-crystalline silicon nanowires (SiNWs) in an LIB test cell. Morphological changes in the SiNWs at different levels of lithiation were also studied using scanning electron microscope (SEM). It was found from SEM observation that lithiation commenced predominantly at the top surface of SiNWs followed by further progression toward the bottom of the SiNWs gradually. The hydrostatic stress of the crystalline core of the SiNWs at different levels of electrochemical lithiation was determined using the in situ synchrotron X-ray microdiffraction technique. We found that the crystalline core of the SiNWs became highly compressive (up to -325.5 MPa) once lithiation started. This finding helps unravel insights about mechanical stress states in the SiNWs during the electrochemical lithiation, which could potentially pave the path toward the fracture-free design of silicon nanostructure anode materials in the next-generation LIB.
We study the realization of an optical transistor (switch and amplifier) and router in multi-order fluorescence (FL) and spontaneous parametric four-wave mixing (SP-FWM). We estimate that the switching speed is about 15 ns. The router action results from the Autler-Townes splitting in spectral or time domain. The switch and amplifier are realized by dressing suppression and enhancement in FL and SP-FWM. The optical transistor and router can be controlled by multi-parameters (i.e., power, detuning, or polarization).
Triclabendazole is an effective medication to treat fascioliasis and paragonimiasis parasitic infections. We implemented a reliable quantum mechanical method which is density functional theory at the level of ωB97XD/6-31G* along with embedded fragments to elucidate stability and phase transition between two forms of triclabendazole. We calculated crystal structure parameters, volumes, Gibbs free energies, and vibrational spectra of two polymorphic forms of triclabendazole under different pressures and temperatures. We confirmed form I was more stable than form II at atmospheric pressure and room temperature. From high-pressure Gibbs free energy computations, we found a pressure-induced phase transformation between form I (triclinic unit cell) and form II (monoclinic unit cell). The phase transition between forms I and II was found at a pressure and temperature of 5.5 GPa and ≈350 K, respectively. In addition, we also studied the high-pressure polymorphic behavior of two forms of triclabendazole. At the pressure of 5.5 GPa and temperature from ≈350 K to 500 K, form II was more stable than form I. However, at temperatures lower than ≈350 K, form I was more stable than form II. We also studied the effects of pressures on volumes and Raman spectra. To the best of our knowledge, no such research has been conducted to determine the presence of phase transformation between two forms of triclabendazole. This is a case study that can be applied to various polymorphic crystals to study their structures, stabilities, spectra, and phase transformations. This research can assist scientists, chemists, and pharmacologists in selecting the desired polymorph and better drug design.
The development of modern civil industry, energy and information technology is inseparable from the rapid explorations of new materials. However, only a small fraction of materials being experimentally/computationally studied in a vast chemical space. Artificial intelligence (AI) is promising to address this gap, but faces many challenges, such as data scarcity and inaccurate material descriptors. Here, we develop an AI platform, AlphaMat, that can complete data preprocessing and downstream AI models. With high efficiency and accuracy, AlphaMat exhibits strong powers to model typical 12 material attributes (formation energy, band gap, ionic conductivity, magnetism, bulk modulus, etc.). AlphaMat’s capabilities are further demonstrated to discover thousands of new materials for use in specific domains. AlphaMat does not require users to have strong programming experience, and its effective use will facilitate the development of materials informatics, which is of great significance for the implementation of AI for Science (AI4S).
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