Trace level determination of beryllium in natural and flavored mineral waters after pre-concentration using activated carbon

The concentrations of beryllium (Be) in natural and flavored mineral water samples were determined by flame atomic absorption spectrophotometer (FAAS) after pre-concentration based on the complexation of Be(+2) with a mixture of acetylacetone (pentane-2,4-dione) plus morin (3,5,7,2',4'-pentaoxyflavone) and adsorption on activated carbon. The adsorbed complex was eluted with 1.5 ml of 2.0 M HNO(3) and evaporated to dryness. After adding 1.5 ml of 2 M HNO(3) and centrifuging, Be in acid solution was determined by FAAS. To remove a number of metals present in water, EDTA was used as a chelating agent. Beryllium in mineral water samples was pre-concentrated by 500-fold, taking 750 ml as initial sample and 1.5 ml as the final volume. The relative standard deviations were sufficiently low for practical purposes and recoveries were up to 85%. Spiking experiments were performed in real samples to establish accuracy and recoveries. The limits of detection and quantification were 0.01 and 0.03 ng ml(-1), respectively. Twenty samples were analyzed for their beryllium content using optimum parameters. The highest concentration of beryllium was found to be 0.94 ± 0.15 ng ml(-1) in a natural mineral water, while beryllium was not detected in five samples.     

An Intelligent Methodology for Railways Monitoring Using Ultrasonic Guided Waves

It is far from trivial to inspect railways for defections. In particular, for the foot area of the rail non destructive testing methods are known to be difficult to apply. In this paper, an ultrasonic guided wave method is considered along with classification methods for automated rail foot defect detection. In effect, given a set of gathered ultrasonic signals, multiple features are extracted from time-, frequency- and time–frequency domains. Next, a robust feature selection method is performed, to collect a small set of complementary features. The classification task is accomplished by means of a kernel-based support vector machine. To demonstrate the performance capabilities of our approach, an extensive experimental setup is designed under representative environmental and operational conditions. The sensitivity and the resolution of the proposed defect detection system are reported. A study on the influence of rail fastening on the proposed method is also reported where robust defect detection rates, greater than 93 %, are achieved assuming that a compact feature subset is considered. However, it is evident in experiments that even in the case of large defects, changes in the environmental conditions (temperature and humidity) increase the interpretation of the acquired signals, thus making the detection task more difficult.     

Lateral Ordering, Position, and Number Control of Self-Organized Quantum Dots: The Key to Future Functional Nanophotonic Devices

Lateral ordering, position, and number control of self-organized epitaxial semiconductor quantum dots (QDs) are demonstrated. Straight linear InAs QD arrays are formed by self- organized anisotropic strain engineering of an InGaAsP/InP (10 0) superlattice template in chemical beam epitaxy. The QD emission wavelength at room temperature is tuned into the important 1.55 mum telecom wavelength region through the insertion of ultrathin GaAs interlayers. Guided self-organized anisotropic strain engineering is demonstrated on shallow- and deep-patterned GaAs (3 1 1)B substrates by molecular beam epitaxy for the formation of complex InGaAs QD arrays. Lateral positioning and number control of InAs QDs, down to a single QD, are demonstrated on truncated InP (100) pyramids by selective-area metal-organic vapor phase epitaxy. Sharp emission around 1.55 mum is observed well above liquid nitrogen temperatures. The regrowth of a passive waveguide structure establishes submicrometer-scale active- passive integration. The demonstrated control over QD formation is the key to future functional nanophotonic devices and paves the way toward the ultimates of photonic-integrated circuits operating at the single and multiple electron and photon level with control of the quantum mechanical and electromagnetic interactions.     

Learning and Predicting Photonic Responses of Plasmonic Nanoparticle Assemblies via Dual Variational Autoencoders

The application of machine learning is demonstrated for rapid and accurate extraction of plasmonic particles cluster geometries from hyperspectral image data via a dual variational autoencoder (dual-VAE). In this approach, the information is shared between the latent spaces of two VAEs acting on the particle shape data and spectral data, respectively, but enforcing a common encoding on the shape-spectra pairs. It is shown that this approach can establish the relationship between the geometric characteristics of nanoparticles and their far-field photonic responses, demonstrating that hyperspectral darkfield microscopy can be used to accurately predict the geometry (number of particles, arrangement) of a multiparticle assemblies below the diffraction limit in an automated fashion with high fidelity (for monomers (0.96), dimers (0.86), and trimers (0.58). This approach of building structure-property relationships via shared encoding is universal and should have applications to a broader range of materials science and physics problems in imaging of both molecular and nanomaterial systems.     

Efficient and Stable Inverted Wide-Bandgap Perovskite Solar Cells and Modules Enabled by Hybrid Evaporation-Solution Method

Wide-bandgap perovskite solar cells (WBG-PSCs), when partnered with Si bottom cells in tandem configuration, can provide efficiencies up to 44%; yet, the development of stable, efficient, and scalable WBG-PSCs is required. Here, the utility of the hybrid evaporation-solution method (HESM) is investigated to meet these demanding requirements via its unique advantages including ease of control and reproducibility. A PbI₂/CsBr layer is co-evaporated followed by coating of organic-halide solutions in a green solvent. Bandgaps between 1.55–1.67 eV are systematically screened by varying CsBr and MABr content. Champion efficiencies of 21.06% and 20.35% in cells and 19.83% and 18.73% in mini-modules (16 cm²) for perovskites with 1.64 and 1.67 eV bandgaps are achieved, respectively. Additionally, 18.51%-efficient semi-transparent WBG-PSCs are implemented in 4T perovskite/bifacial silicon configuration, reaching a projected power output of 30.61 mW cm⁻² based on PD IEC TS 60904-1-2 (BiFi200) protocol. Despite similar bandgaps achieved by incorporating Br via MABr solution and/or CsBr evaporation, PSCs having a perovskite layer without MABr addition show significantly higher thermal and moisture stability. This study proves scalable, high-performance, and stable WBG-PSCs are enabled by HESM, hence their use in tandems and in emerging applications such as indoor photovoltaics are now within reach.