NASA-JSC antenna near-field measurement system

Work was completed on the near-field range control software. The capabilities of the data processing software were expanded with the addition of probe compensation. In addition, the user can process the measured data from the same computer terminal used for range control. The design of the laser metrology system was completed. It provides precise measruement of probe location during near-field measurements as well as position data for control of the translation beam and probe cart. A near-field range measurement system was designed, fabricated, and tested

Molecular origin of the anisotropic dye orientation in emissive layers of organic light emitting diodes

Molecular orientation anisotropy of the emitter molecules used in organic light emitting diodes (OLEDs) can give rise to an enhanced light-outcoupling efficiency, when their transition dipole moments are oriented preferentially parallel to the substrate, and to a modified internal quantum efficiency, when their static dipole moments give rise to a locally modified internal electric field. Here, the orientation anisotropy of state-of-the-art phosphorescent dye molecules is investigated using a simulation approach which mimics the physical vapor deposition process of amorphous thin films. The simulations reveal for all studied systems significant orientation anisotropy. Various types are found, including a preference of the static dipole moments to a certain direction or axis. However, only few systems show an improved outcoupling efficiency. The outcoupling efficiency predicted by the simulations agrees with experimentally reported values. The simulations reveal in some cases a significant effect of the host molecules, and suggest that the driving force of molecular orientation lies in the molecule-specific van der Waals interactions of the dye molecule within the thin film surface. The electrostatic dipole-dipole interaction slightly reduces the anisotropy. These findings can be used for the future design of improved dye molecules.</p

Scientific intuition inspired by machine learning-generated hypotheses

Machine learning with application to questions in the physical sciences has become a widely used tool, successfully applied to classification, regression and optimization tasks in many areas. Research focus mostly lies in improving the accuracy of the machine learning models in numerical predictions, while scientific understanding is still almost exclusively generated by human researchers analysing numerical results and drawing conclusions. In this work, we shift the focus on the insights and the knowledge obtained by the machine learning models themselves. In particular, we study how it can be extracted and used to inspire human scientists to increase their intuitions and understanding of natural systems. We apply gradient boosting in decision trees to extract human-interpretable insights from big data sets from chemistry and physics. In chemistry, we not only rediscover widely know rules of thumb but also find new interesting motifs that tell us how to control solubility and energy levels of organic molecules. At the same time, in quantum physics, we gain new understanding on experiments for quantum entanglement. The ability to go beyond numerics and to enter the realm of scientific insight and hypothesis generation opens the door to use machine learning to accelerate the discovery of conceptual understanding in some of the most challenging domains of science

2-kW Average Power CW Phase-Conjugate Solid-State Laser

We have demonstrated stable operation of a 2-kW Yb:YAG phase-conjugate master oscillator power amplifier (PC-MOPA) laser system with a loop phase-conjugate mirror (LPCM). This is the first demonstration of a continuous wave (CW)-input LPCM MOPA operating at a power greater than 1 kW with a nearly diffraction-limited output beam. The single-pass beam quality incident on the LPCM varied with the specific operating conditions, but it was typically ${sim}20$ times diffraction-limited (XDL). The measured beam quality with an MOPA output power of 1.65 kW was 1.3 XDL

Potentially Diagnostic Electron Paramagnetic Resonance Spectra Elucidate the Underlying Mechanism of Mitochondrial Dysfunction in the Deoxyguanosine Kinase Deficient Rat Model of a Genetic Mitochondrial DNA Depletion Syndrome

A novel rat model for a well-characterized human mitochondrial disease, mitochondrial DNA depletion syndrome with associated deoxyguanosine kinase (DGUOK) deficiency, is described. The rat model recapitulates the pathologic and biochemical signatures of the human disease. The application of electron paramagnetic (spin) resonance (EPR) spectroscopy to the identification and characterization of respiratory chain abnormalities in the mitochondria from freshly frozen tissue of the mitochondrial disease model rat is introduced. EPR is shown to be a sensitive technique for detecting mitochondrial functional abnormalities in situ and, here, is particularly useful in characterizing the redox state changes and oxidative stress that can result from depressed expression and/or diminished specific activity of the distinct respiratory chain complexes. As EPR requires no sample preparation or non-physiological reagents, it provides information on the status of the mitochondrion as it was in the functioning state. On its own, this information is of use in identifying respiratory chain dysfunction; in conjunction with other techniques, the information from EPR shows how the respiratory chain is affected at the molecular level by the dysfunction. It is proposed that EPR has a role in mechanistic pathophysiological studies of mitochondrial disease and could be used to study the impact of new treatment modalities or as an additional diagnostic tool

A subsurface particle maximum layer and enhanced microbial activity in the secondary nitrite maximum of the northeastern tropical Pacific Ocean

Profiles of light transmission, dissolved oxygen, dissolved nutrients, electron transport system (ETS) activity, temperature and salinity were made in the northeastern tropical Pacific Ocean. A particle maximum at 150–300 m within the oxygen minimum and secondary nitrite maximum was associated with the salinity maximum of Subtropical Subsurface Water. A subsurface maximum in ETS activity was also found to be associated with the secondary nitrite maximum and the particle maximum. Persistence of these features at a constant depth and their location within a minimum in vertical static stability suggest an advective and/or in situ origin for the particles and an in situ development of the associated chemical and biochemical extremes

Toward Design of Novel Materials for Organic Electronics

Materials for organic electronics are presently used in prominent applications, such as displays in mobile devices, while being intensely researched for other purposes, such as organic photovoltaics, large-area devices, and thin-film transistors. Many of the challenges to improve and optimize these applications are material related and there is a nearly infinite chemical space that needs to be explored to identify the most suitable material candidates. Established experimental approaches struggle with the size and complexity of this chemical space. Herein, the development of simulation methods is addressed, with a particular emphasis on predictive multiscale protocols, to complement experimental research in the identification of novel materials and illustrate the potential of these methods with a few prominent recent applications. Finally, the potential of machine learning and methods based on artificial intelligence is discussed to further accelerate the search for new materials