2,270 research outputs found

    Organic lasers: recent developments on materials, device geometries, and fabrication techniques

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    MCG acknowledges financial support through the ERC Starting Grant ABLASE (640012) and the European Union Marie Curie Career Integration Grant (PCIG12-GA-2012-334407). AJCK acknowledges financial support by the German Federal Ministry for Education and Research through a NanoMatFutur research group (BMBF grant no. 13N13522).Organic dyes have been used as gain medium for lasers since the 1960s, long before the advent of today’s organic electronic devices. Organic gain materials are highly attractive for lasing due to their chemical tunability and large stimulated emission cross section. While the traditional dye laser has been largely replaced by solid-state lasers, a number of new and miniaturized organic lasers have emerged that hold great potential for lab-on-chip applications, biointegration, low-cost sensing and related areas, which benefit from the unique properties of organic gain materials. On the fundamental level, these include high exciton binding energy, low refractive index (compared to inorganic semiconductors), and ease of spectral and chemical tuning. On a technological level, mechanical flexibility and compatibility with simple processing techniques such as printing, roll-to-roll, self-assembly, and soft-lithography are most relevant. Here, the authors provide a comprehensive review of the developments in the field over the past decade, discussing recent advances in organic gain materials, which are today often based on solid-state organic semiconductors, as well as optical feedback structures, and device fabrication. Recent efforts toward continuous wave operation and electrical pumping of solid-state organic lasers are reviewed, and new device concepts and emerging applications are summarized.PostprintPeer reviewe

    Loading an Equidistant Ion Chain in a Ring Shaped Surface Trap and Anomalous Heating Studies with a High Optical Access Trap

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    Microfabricated segmented surface ion traps are one viable avenue to scalable quantum information processing. At Sandia National Laboratories we design, fabricate, and characterize such traps. Our unique fabrication capabilities allow us to design traps that facilitate tasks beyond quantum information processing. The design and performance of a trap with a target capability of storing hundreds of equally spaced ions on a ring is described. Such a device could aid experimental studies of phenom- ena as diverse as Hawking radiation, quantum phase transitions, and the Aharonov - Bohm effect. The fabricated device is demonstrated to hold a 3c 400 ion circular crystal, with 9 μm average spacing between ions. The task is accomplished by first characterizing undesired electric fields in the trapping volume and then designing and applying an electric field that substantially reduces the undesired fields. In addition, experimental efforts are described to reduce the motional heating rates in a surface trap by low energy in situ argon plasma treatment that reduces the amount of surface contaminants. The experiment explores the premise that carbonaceous compounds present on the surface contribute to the anomalous heating of secular motion modes in surface traps. This is a research area of fundamental interest to the ion trapping community, as heating adversely affects coherence and thus gate fidelity. The de- vice used provides high optical laser access, substantially reducing scatter from the surface, and thus charging that may lead to excess micromotion. Heating rates for different axial mode frequencies are compared before and after plasma treatment. The presence of a carbon source near the plasma prevents making a conclusion on the observed absence of change in heating rates

    Improved 3D MR Image Acquisition and Processing in Congenital Heart Disease

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    Congenital heart disease (CHD) is the most common type of birth defect, affecting about 1% of the population. MRI is an essential tool in the assessment of CHD, including diagnosis, intervention planning and follow-up. Three-dimensional MRI can provide particularly rich visualization and information. However, it is often complicated by long scan times, cardiorespiratory motion, injection of contrast agents, and complex and time-consuming postprocessing. This thesis comprises four pieces of work that attempt to respond to some of these challenges. The first piece of work aims to enable fast acquisition of 3D time-resolved cardiac imaging during free breathing. Rapid imaging was achieved using an efficient spiral sequence and a sparse parallel imaging reconstruction. The feasibility of this approach was demonstrated on a population of 10 patients with CHD, and areas of improvement were identified. The second piece of work is an integrated software tool designed to simplify and accelerate the development of machine learning (ML) applications in MRI research. It also exploits the strengths of recently developed ML libraries for efficient MR image reconstruction and processing. The third piece of work aims to reduce contrast dose in contrast-enhanced MR angiography (MRA). This would reduce risks and costs associated with contrast agents. A deep learning-based contrast enhancement technique was developed and shown to improve image quality in real low-dose MRA in a population of 40 children and adults with CHD. The fourth and final piece of work aims to simplify the creation of computational models for hemodynamic assessment of the great arteries. A deep learning technique for 3D segmentation of the aorta and the pulmonary arteries was developed and shown to enable accurate calculation of clinically relevant biomarkers in a population of 10 patients with CHD

    Sound Localization by Echolocating Bats

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    Echolocating bats emit ultrasonic vocalizations and listen to echoes reflected back from objects in the path of the sound beam to build a spatial representation of their surroundings. Important to understanding the representation of space through echolocation are detailed studies of the cues used for localization, the sonar emission patterns and how this information is assembled. This thesis includes three studies, one on the directional properties of the sonar receiver, one on the directional properties of the sonar transmitter, and a model that demonstrates the role of action in building a representation of auditory space. The general importance of this work to a broader understanding of spatial localization is discussed. Investigations of the directional properties of the sonar receiver reveal that interaural level difference and monaural spectral notch cues are both dependent on sound source azimuth and elevation. This redundancy allows flexibility that an echolocating bat may need when coping with complex computational demands for sound localization. Using a novel method to measure bat sonar emission patterns from freely behaving bats, I show that the sonar beam shape varies between vocalizations. Consequently, the auditory system of a bat may need to adapt its computations to accurately localize objects using changing acoustic inputs. Extra-auditory signals that carry information about pinna position and beam shape are required for auditory localization of sound sources. The auditory system must learn associations between extra-auditory signals and acoustic spatial cues. Furthermore, the auditory system must adapt to changes in acoustic input that occur with changes in pinna position and vocalization parameters. These demands on the nervous system suggest that sound localization is achieved through the interaction of behavioral control and acoustic inputs. A sensorimotor model demonstrates how an organism can learn space through auditory-motor contingencies. The model also reveals how different aspects of sound localization, such as experience-dependent acquisition, adaptation, and extra-auditory influences, can be brought together under a comprehensive framework. This thesis presents a foundation for understanding the representation of auditory space that builds upon acoustic cues, motor control, and learning dynamic associations between action and auditory inputs
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