Ratiometric Nanothermometer Based on a Radical Excimer for In Vivo Sensing (Small 32/2023)

Ratiometric fluorescent nanothermometers with near-infrared emission play an important role in in vivo sensing since they can be used as intracellular thermal sensing probes with high spatial resolution and high sensitivity, to investigate cellular functions of interest in diagnosis and therapy, where current approaches are not effective. Herein, the temperature-dependent fluorescence of organic nanoparticles is designed, synthesized, and studied based on the dual emission, generated by monomer and excimer species, of the tris(2,4,6-trichlorophenyl)methyl radical (TTM) doping organic nanoparticles (TTMd-ONPs), made of optically neutral tris(2,4,6-trichlorophenyl)methane (TTM-αH), acting as a matrix. The excimer emission intensity of TTMd-ONPs decreases with increasing temperatures whereas the monomer emission is almost independent and can be used as an internal reference. TTMd-ONPs show a great temperature sensitivity (3.4% K⁻¹ at 328 K) and a wide temperature response at ambient conditions with excellent reversibility and high colloidal stability. In addition, TTMd-ONPs are not cytotoxic and their ratiometric outputs are unaffected by changes in the environment. Individual TTMd-ONPs are able to sense temperature changes at the nano-microscale. In vivo thermometry experiments in Caenorhabditis elegans (C. elegans) worms show that TTMd-ONPs can locally monitor internal body temperature changes with spatio-temporal resolution and high sensitivity, offering multiple applications in the biological nanothermometry field.     

Ockham’s index of citation impact

Scientometrics - We demonstrate that by using a triple of simple numerical summaries: an author’s productivity, their overall impact, and a single other bibliometric index that aims to...     

Investigation on the effect of NiO content on spray deposited ZnO for selective ammonia detection

Journal of Materials Science: Materials in Electronics - Zinc Oxide/Nickel Oxide (ZnO/NiO) heterostructures were deposited using the spray pyrolysis technique by varying NiO content. The X-ray...     

Accelerated multiobjective design of miniaturized microwave components by means of nested kriging surrogates

Design of microwave components is an inherently multiobjective task. Often, the objectives are at least partially conflicting and the designer has to work out a suitable compromise. In practice, generating the best possible trade‐off designs requires multiobjective optimization, which is a computationally demanding task. If the structure of interest is evaluated through full‐wave electromagnetic (EM) analysis, the employment of widely used population‐based metaheuristics algorithms may become prohibitive in computational terms. This is a common situation for miniaturized components, where considerable cross‐coupling effects make traditional representations (eg, network equivalents) grossly inaccurate. This article presents a framework for accelerated EM‐driven multiobjective design of compact microwave devices. It adopts a recently reported nested kriging methodology to identify the parameter space region containing the Pareto front and to render a fast surrogate, subsequently used to find the first approximation of the Pareto set. The final trade‐off designs are produced in a separate, surrogate‐assisted refinement process. Our approach is demonstrated using a three‐section impedance matching transformer designed for the best matching and the minimum footprint area. The Pareto set is generated at the cost of only a few hundred of high‐fidelity EM simulations of the transformer circuit despite a large number of geometry parameters involved.     

Surrogate modeling of impedance matching transformers by means of variable‐fidelity electromagnetic simulations and nested cokriging

Accurate performance evaluation of microwave components can be carried out using full‐wave electromagnetic (EM) simulation tools, routinely employed for circuit verification but also in the design process itself. Unfortunately, the computational cost of EM‐driven design may be high. This is especially pertinent to tasks entailing considerable number of simulations (eg, parametric optimization, statistical analysis). A possible way of alleviating these difficulties is utilization of fast replacement models, also referred to as surrogates. Notwithstanding, conventional modeling methods exhibit serious limitations when it comes to handling microwave components. The principal challenges include large number of geometry and material parameters, highly nonlinear characteristics, as well as the necessity of covering wide ranges of operating conditions. The latter is mandatory from the point of view of the surrogate model utility. This article presents a novel modeling approach that incorporates variable‐fidelity EM simulations into the recently reported nested kriging framework. A combination of domain confinement due to nested kriging, and low‐/high‐fidelity EM data blending through cokriging, enables the construction of reliable surrogates at a fraction of cost required by single‐fidelity nested kriging. Our technique is validated using a three‐section miniaturized impedance matching transformer with its surrogate model rendered over wide range of operating frequencies. Comprehensive benchmarking demonstrates superiority of the proposed method over both conventional models and nested kriging.     

Multi-objective shape optimization of a hydraulic turbine runner using efficiency,strength and weight criteria

An approach for multi-discipline automatic optimization of the hydraulic turbine runner shape is presented. The approach accounts hydraulic efficiency, mechanical strength and the weight of the runner. In order to effectively control the strength and weight of the runner, a new parameterization of the blade thickness function is suggested. Turbine efficiency is evaluated through numerical solution of Reynolds-averaged Navier-Stokes equations, while the finite element method is used to evaluate the von Mises stress in the runner. An objective function, being the weighted sum of maximal stress and the blade volume, is suggested to account for both the strength and weight of the runner. Multi-objective genetic algorithm is used to solve the optimization problem. The suggested approach has been applied to automatic design of a Francis turbine runner. Series of three-objective optimization runs have been carried out. The obtained results clearly indicate that simultaneous account of stress and weight objectives accompanied by thickness variation allows obtaining high efficiency, light and durable turbine runners.     

The Advantage of Nanowire Configuration in Band Structure Determination

Earth-abundant and environmentally friendly semiconductors offer a promising path toward low-cost mass production of solar cells. A critical aspect in exploring new semiconducting materials and demonstrating their enhanced functionality consists in disentangling them from the artifacts of defects. Nanowires are diameter-tailored filamentary structures that tend to be defect-free and thus ideal model systems for a given material. Here, an additional advantage is demostrated, which is the determination of the band structure, by performing high energy and spatial resolution electron energy-loss spectroscopy in aloof and inner beam geometry in a scanning transmission electron microscope. The experimental results are complemented by spectroscopic ellipsometry and are excellently correlated with first principles calculations. This study opens the path for characterizing the band structure of new compounds in a non-destructive and prompt manner, strengthening the route of new materials discovery.     

Guest Editorial: Special issue on computer vision and machine learning for healthcare applications

Pattern Analysis and Applications -