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result(s) for
"Davydov, V. V."
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InSe as a case between 3D and 2D layered crystals for excitons
InSe is a promising material in many aspects where the role of excitons is decisive. Here we report the sequential appearance in its luminescence of the exciton, the biexciton, and the P-band of the exciton-exciton scattering while the excitation power increases. The strict energy and momentum conservation rules of the P-band are used to reexamine the exciton binding energy. The new value ≥20 meV is markedly higher than the currently accepted one (14 meV), being however well consistent with the robustness of the excitons up to room temperature. A peak controlled by the Sommerfeld factor is found near the bandgap (~1.36 eV). Our findings supported by theoretical calculations taking into account the anisotropic material parameters question the pure three-dimensional character of the exciton in InSe, assumed up to now. The refined character and parameters of the exciton are of paramount importance for the successful application of InSe in nanophotonics.
The optical properties of layered InSe aren’t fully understood. Here, the authors observe the exciton, biexciton, and P-band of exciton-exciton scattering in the photoluminescence spectrum of InSe, and calculate an exciton binding energy value of ≥20 meV, markedly higher than the currently accepted 14 meV.
Journal Article
On-the-fly closed-loop materials discovery via Bayesian active learning
Active learning—the field of machine learning (ML) dedicated to optimal experiment design—has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. The real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) is implemented at the synchrotron beamline to accelerate the interconnected tasks of phase mapping and property optimization, with each cycle taking seconds to minutes. We also demonstrate an embodiment of human-machine interaction, where human-in-the-loop is called to play a contributing role within each cycle. This work has resulted in the discovery of a novel epitaxial nanocomposite phase-change memory material.
Machine learning driven research holds big promise towards accelerating materials’ discovery. Here the authors demonstrate CAMEO, which integrates active learning Bayesian optimization with practical experiments execution, for the discovery of new phase- change materials using X-ray diffraction experiments.
Journal Article
A new method for monitoring the health condition based on nondestructive signals of laser radiation absorption and scattering
The article describes a new method for processing a pulse wave, which is formed from the recorded absorption and scattering signals of laser radiation on a blood vessel or human tissues. A new method of tuning the optical part of the pulse oximeter has been developed to increase the reliability of the results in the rapid diagnosis of the human condition. Experimental data on studies of various people are presented.
Journal Article
Novel nanocomposite-superlattices for low energy and high stability nanoscale phase-change memory
Data-centric applications are pushing the limits of energy-efficiency in today’s computing systems, including those based on phase-change memory (PCM). This technology must achieve low-power and stable operation at nanoscale dimensions to succeed in high-density memory arrays. Here we use a novel combination of phase-change material superlattices and nanocomposites (based on Ge
4
Sb
6
Te
7
), to achieve record-low power density ≈ 5 MW/cm
2
and ≈ 0.7 V switching voltage (compatible with modern logic processors) in PCM devices with the smallest dimensions to date (≈ 40 nm) for a superlattice technology on a CMOS-compatible substrate. These devices also
simultaneously
exhibit low resistance drift with 8 resistance states, good endurance (≈ 2 × 10
8
cycles), and fast switching (≈ 40 ns). The efficient switching is enabled by strong heat confinement within the superlattice materials and the nanoscale device dimensions. The microstructural properties of the Ge
4
Sb
6
Te
7
nanocomposite and its high crystallization temperature ensure the fast-switching speed and stability in our superlattice PCM devices. These results re-establish PCM technology as one of the frontrunners for energy-efficient data storage and computing.
Data-centric applications benefit from dense, low-power memory. Here the authors use a combination of chalcogenide superlattices and nanocomposites to achieve low switching voltage (0.7 V) and fast speed (40 ns) in 40-nm-scale phase-change memory.
Journal Article
Identifying a High Fraction of the Human Genome to be under Selective Constraint Using GERP
Computational efforts to identify functional elements within genomes leverage comparative sequence information by looking for regions that exhibit evidence of selective constraint. One way of detecting constrained elements is to follow a bottom-up approach by computing constraint scores for individual positions of a multiple alignment and then defining constrained elements as segments of contiguous, highly scoring nucleotide positions. Here we present GERP++, a new tool that uses maximum likelihood evolutionary rate estimation for position-specific scoring and, in contrast to previous bottom-up methods, a novel dynamic programming approach to subsequently define constrained elements. GERP++ evaluates a richer set of candidate element breakpoints and ranks them based on statistical significance, eliminating the need for biased heuristic extension techniques. Using GERP++ we identify over 1.3 million constrained elements spanning over 7% of the human genome. We predict a higher fraction than earlier estimates largely due to the annotation of longer constrained elements, which improves one to one correspondence between predicted elements with known functional sequences. GERP++ is an efficient and effective tool to provide both nucleotide- and element-level constraint scores within deep multiple sequence alignments.
Journal Article
Insight into the performance of multi-color InGaN/GaN nanorod light emitting diodes
We report on the thorough investigation of light emitting diodes (LEDs) made of core-shell nanorods (NRs) with InGaN/GaN quantum wells (QWs) in the outer shell, which are grown on patterned substrates by metal-organic vapor phase epitaxy. The multi-bands emission of the LEDs covers nearly the whole visible region, including UV, blue, green, and orange ranges. The intensity of each emission is strongly dependent on the current density, however the LEDs demonstrate a rather low color saturation. Based on transmission electron microscopy data and comparing them with electroluminescence and photoluminescence spectra measured at different excitation powers and temperatures, we could identify the spatial origination of each of the emission bands. We show that their wavelengths and intensities are governed by different thicknesses of the QWs grown on different crystal facets of the NRs as well as corresponding polarization-induced electric fields. Also the InGaN incorporation strongly varies along the NRs, increasing at their tips and corners, which provides the red shift of emission. With increasing the current, the different QW regions are activated successively from the NR tips to the side-walls, resulting in different LED colors. Our findings can be used as a guideline to design effectively emitting multi-color NR-LEDs.
Journal Article
Features of formation of structure of a nuclear magnetic resonance signal in weak magnetic field
The peculiarities of formation of the line structure of the detected signal of nuclear magnetic resonance in a weak field with use of the modulation technique are considered in the article. Relations are established that are mandatory for the registration of signals in a small-sized nuclear magnetic spectrometer to perform measurements with an error of less than 1%. The results of experimental studies of various media are presented.
Journal Article
Fiber-optical system for transmitting heterodyne signals in active phased antenna arrays of radar stations
For transmitting reference and heterodyne signals of active phased array antenna is developed a new design of a fiber-optic communication system. In article are considered of transmission features of reference and heterodyne signals in is developed by us of new design of a fiber-optic communication system. The data of experimental investigation are presented.
Journal Article
Features of transmission bearing and heterodyne receivers for signals in fiber-optic communication line in active phased array antenna
The article discusses the problems arising from the modernization of active phased antenna arrays (for example, an increase in the number of transceiver active elements, a decrease in the weight and size of the antenna system, etc.). It has been substantiated that the most rational solution of these problems is the use of fiber-optical communication systems for transmitting heterodyne signals. A new design of the transmit-receive module with a fiber-optic transmission system for an active phased antenna array has been developed. The results of experimental investigations are presented.
Journal Article