122 resultados para photonic crystal laser


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Bulk and size-fractionated kaolinites from seven localities in Australia as well as the Clay Minerals Society Source Clays Georgia KGa-1 and KGa-2 have been studied by X-ray diffraction (XRD), laser scattering, and electron microscopy in order to understand the variation of particle characteristics across a range of environments and to correlate specific particle characteristics with intercalation behavior. All kaolinites have been intercalated with N-methyl (NMF) after pretreatment with hydrazine hydrate, and the relative efficiency of intercalation has been determined using XRD. Intercalate yields of kaolinite: NMF are consistently low for bulk samples that have a high proportion of small-sized particles (i.e., <0.5 µm) and for biphased kaolinites with a high percentage (>60%) of low-defect phase. In general, particle size appears to be a more significant controlling factor than defect distribution in determining the relative yield of kaolinite: NMF intercalate.

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We present a method for optical encryption of information, based on the time-dependent dynamics of writing and erasure of refractive index changes in a bulk lithium niobate medium. Information is written into the photorefractive crystal with a spatially amplitude modulated laser beam which when overexposed significantly degrades the stored data making it unrecognizable. We show that the degradation can be reversed and that a one-to-one relationship exists between the degradation and recovery rates. It is shown that this simple relationship can be used to determine the erasure time required for decrypting the scrambled index patterns. In addition, this method could be used as a straightforward general technique for determining characteristic writing and erasure rates in photorefractive media.

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This thesis studied the source of instability in optical phase modulators used in high accuracy laser measurement systems. The nonlinear origin of the amplitude noise helped further reducing this instability in applications that rely on phase modulators to function. This outcome will have positive impacts on the development of new methods in the amplitude noise suppression.

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High-quality YBa2Cu3O7-δ films grown on (001) single-crystal Y-ZrO2 substrates by pulsed laser deposition have been studied as a function of substrate temperature using transmission electron microscopy. A transition from epitaxial films to c-axis oriented polycrystalline films was observed at 740°C. An intermediate, polycrystalline, BaZrO3 layer was formed from a reaction between the film and the substrate. A dominant orientation relationship of [001] YBCO//[001]int. layer//[001]YSZ and [110] YBCO//[110]int. layer//[100]YSZ was observed. The formation of grain boundaries in the films resulted in an increased microwave surface resistance and a decreased critical-current density. The superconducting transition temperature remained fairly constant at about 90 K.

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YBCO thin films were fabricated by laser deposition, in situ on MgO substrates, using both O2 and N2O as process gas. Films with Tc above 90 K and jc of 106 A/cm2 at 77 K were grown in oxygen at a substrate temperature of 765 °C. Using N2O, the optimum substrate temperature was 745 °C, giving a Tc of 87 K. At lower temperatures, the films made in N2O had higher Tc (79 K) than the films made in oxygen (66 K). SEM and STM investigations of the film surfaces showed the films to consist of a comparatively smooth background surface and a distribution of larger particles. Both the particle size and the distribution density depended on the substrate temperature.

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A novel electrochemical route is used to form highly {111}-oriented and size-controlled Au nanoprisms directly onto the electrodes of quartz crystal microbalances (QCMs) which are subsequently used as mercury vapor sensors. The Au nanoprism loaded QCM sensors exhibited excellent response–concentration linearity with a response enhancement of up to ~ 800% over a non-modified sensor at an operating temperature of 28 °C. The increased surface area and atomic-scale features (step/defect sites) introduced during the growth of nanoprisms are thought to play a significant role in enhancing the sensing properties of the Au nanoprisms toward Hg vapor. The sensors are shown to have excellent Hg sensing capabilities in the concentration range of 0.123–1.27 ppmv (1.02–10.55 mg m − 3), with a detection limit of 2.4 ppbv (0.02 mg m − 3) toward Hg vapor when operating at 28 °C, and 17 ppbv (0.15 mg m − 3) at 89 °C, making them potentially useful for air monitoring applications or for monitoring the efficiency of Hg emission control systems in industries such as mining and waste incineration. The developed sensors exhibited excellent reversible behavior (sensor recovery) within 1 h periods, and crucially were also observed to have high selectivity toward Hg vapor in the presence of ethanol, ammonia and humidity, and excellent long-term stability over a 33 day operating period.

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TiO2 nanofibers with different crystal phases have been discovered to be efficient catalysts for the transesterification of alcohols with dimethyl carbonate to produce corresponding methyl carbonates. Advantages of this catalytic system include excellent selectivity (>99%), general suitability to alcohols, reusability and ease of preparation and separation of fibrous catalysts. Activities of TiO2 catalysts were found to correlate with their crystal phases which results in different absorption abilities and activation energies on the catalyst surfaces. The kinetic isotope effect (KIE) investigation identified the rate-determining step, and the isotope labeling of oxygen-18 of benzyl alcohol clearly demonstrated the reaction pathway. Finally, the transesterification mechanism of alcohols with dimethyl carbonate catalyzed by TiO2 nanofibers was proposed, in which the alcohol released the proton to form benzyl alcoholic anion, and subsequently the anion attacks the carbonyl carbon of dimethyl carbonate to produce the target product of benzyl methyl carbonate.

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PURPOSE To investigate the utility of using non-contact laser-scanning confocal microscopy (NC-LSCM), compared with the more conventional contact laser-scanning confocal microscopy (C-LSCM), for examining corneal substructures in vivo. METHODS An attempt was made to capture representative images from the tear film and all layers of the cornea of a healthy, 35 year old female, using both NC-LSCM and C-LSCM, on separate days. RESULTS Using NC-LSCM, good quality images were obtained of the tear film, stroma, and a section of endothelium, but the corneal depth of the images of these various substructures could not be ascertained. Using C-LSCM, good quality, full-field images were obtained of the epithelium, subbasal nerve plexus, stroma, and endothelium, and the corneal depth of each of the captured images could be ascertained. CONCLUSIONS NC-LSCM may find general use for clinical examination of the tear film, stroma and endothelium, with the caveat that the depth of stromal images cannot be determined when using this technique. This technique also facilitates image capture of oblique sections of multiple corneal layers. The inability to clearly and consistently image thin corneal substructures - such as the tear film, subbasal nerve plexus and endothelium - is a key limitation of NC-LSCM.

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This work considers the problem of building high-fidelity 3D representations of the environment from sensor data acquired by mobile robots. Multi-sensor data fusion allows for more complete and accurate representations, and for more reliable perception, especially when different sensing modalities are used. In this paper, we propose a thorough experimental analysis of the performance of 3D surface reconstruction from laser and mm-wave radar data using Gaussian Process Implicit Surfaces (GPIS), in a realistic field robotics scenario. We first analyse the performance of GPIS using raw laser data alone and raw radar data alone, respectively, with different choices of covariance matrices and different resolutions of the input data. We then evaluate and compare the performance of two different GPIS fusion approaches. The first, state-of-the-art approach directly fuses raw data from laser and radar. The alternative approach proposed in this paper first computes an initial estimate of the surface from each single source of data, and then fuses these two estimates. We show that this method outperforms the state of the art, especially in situations where the sensors react differently to the targets they perceive.

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Field robots often rely on laser range finders (LRFs) to detect obstacles and navigate autonomously. Despite recent progress in sensing technology and perception algorithms, adverse environmental conditions, such as the presence of smoke, remain a challenging issue for these robots. In this paper, we investigate the possibility to improve laser-based perception applications by anticipating situations when laser data are affected by smoke, using supervised learning and state-of-the-art visual image quality analysis. We propose to train a k-nearest-neighbour (kNN) classifier to recognise situations where a laser scan is likely to be affected by smoke, based on visual data quality features. This method is evaluated experimentally using a mobile robot equipped with LRFs and a visual camera. The strengths and limitations of the technique are identified and discussed, and we show that the method is beneficial if conservative decisions are the most appropriate.

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This paper presents an approach to promote the integrity of perception systems for outdoor unmanned ground vehicles (UGV) operating in challenging environmental conditions (presence of dust or smoke). The proposed technique automatically evaluates the consistency of the data provided by two sensing modalities: a 2D laser range finder and a millimetre-wave radar, allowing for perceptual failure mitigation. Experimental results, obtained with a UGV operating in rural environments, and an error analysis validate the approach.

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Camera-laser calibration is necessary for many robotics and computer vision applications. However, existing calibration toolboxes still require laborious effort from the operator in order to achieve reliable and accurate results. This paper proposes algorithms that augment two existing trustful calibration methods with an automatic extraction of the calibration object from the sensor data. The result is a complete procedure that allows for automatic camera-laser calibration. The first stage of the procedure is automatic camera calibration which is useful in its own right for many applications. The chessboard extraction algorithm it provides is shown to outperform openly available techniques. The second stage completes the procedure by providing automatic camera-laser calibration. The procedure has been verified by extensive experimental tests with the proposed algorithms providing a major reduction in time required from an operator in comparison to manual methods.

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This work aims to promote integrity in autonomous perceptual systems, with a focus on outdoor unmanned ground vehicles equipped with a camera and a 2D laser range finder. A method to check for inconsistencies between the data provided by these two heterogeneous sensors is proposed and discussed. First, uncertainties in the estimated transformation between the laser and camera frames are evaluated and propagated up to the projection of the laser points onto the image. Then, for each pair of laser scan-camera image acquired, the information at corners of the laser scan is compared with the content of the image, resulting in a likelihood of correspondence. The result of this process is then used to validate segments of the laser scan that are found to be consistent with the image, while inconsistent segments are rejected. Experimental results illustrate how this technique can improve the reliability of perception in challenging environmental conditions, such as in the presence of airborne dust.

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Electrocatalytic processes will undoubtedly be at the heart of energising future transportation and technology with the added importance of being able to create the necessary fuels required to do so in an environmentally friendly and cost effective manner. For this to be successful two almost mutually exclusive surface properties need to be reconciled, namely producing highly active/reactive surface sites that exhibit long term stability. This article reviews the various approaches which have been undertaken to study the elusive nature of these active sites on metal surfaces which are considered as adatoms or clusters of adatoms with low coordination number. This includes the pioneering studies at extended well defined stepped single crystal surfaces using cyclic voltammetry up to the highly sophisticated in situ electrochemical imaging techniques used to study chemically synthesised nanomaterials. By combining the information attained from single crystal surfaces, individual nanoparticles of defined size and shape, density functional theory calculations and new concepts such as mesoporous multimetallic thin films and single atom electrocatalysts new insights into the design and fabrication of materials with highly active but stable active sites can be achieved. The area of electrocatalysis is therefore not only a fascinating and exciting field in terms of realistic technological and economical benefits but also from the fundamental understanding that can be acquired by studying such an array of interesting materials.