一种可直接使用的磁性光热纳米流体

本文通过"两步法"制备了一种具有高光热转换效率的Fe304-H20磁性纳米流体。该纳米流体可以通过磁分离技术实现纳米颗粒与基液的分离,使得纳米流体基液得以直接使用,同时具有优异的可重复利用性能。实验结果表明,在1200 s内其光热转化效率高达70.2%,并且经过60次的循环使用后无明显的衰减。此研究结果为实现纳米流体基液的直接应用提供了可行性的方案。     

A theoretical study: Green phosphorescent iridium(III) complexes with low-efficiency roll-off

A series of heterocyclic Ir(III) complexes used in organic light-emitting diode (OLED) materials with low-efficiency roll-off performance have been studied theoretically. Their electronic structures, spectral properties, and their application value in OLEDs are discussed. The geometries, electronic structures, lowest-lying singlet absorptions, and triplet emissions of (dmdppr-dmp)₂Ir(dibm), and the theoretically designed models of (dmdppr-dmp)₂Ir(acac), (dmdppr-dmp)₂Ir(tpip), (dmdppr-Fdmp)₂Ir(dibm), (dmdppr-Fdmp)₂Ir(acac), and (dmdppr-Fdmp)₂Ir(tpip) were investigated with density-functional-theory-based approaches, where dibm denotes 2,6-dimethy-3,5-heptanedionato-κ₂-O,O′, acac denotes acetylacetonate, and tpip denotes tetraphenylimido-diphosphinate.     

Advantage of populous countries in the trends of innovation efficiency

A flurry of studies indicates that population size has a positive effect on innovation, however, cross-country empirical evidence remains sparse. In this paper, we add to the literature by investigating the relationship between population size and innovation efficiency at the country level through constructing three relative indexes based on the datasets of patent applications and Research and Development (R&D) investment. Different from previous studies based on absolute innovation indicators, the relative indexes can reflect the core innovation efficiency of economies by excluding the impact from the difference of economic development level, with a view putting all economies into a comparable standard framework. For all of the three relative indexes, their long-term trends show significant correlations with population size, and the economy with a larger population usually has better and stable performance on the trends of innovation efficiency. In addition, we find that there is a critical population size, over which the economy would be more likely to have a spontaneous improvement on innovation efficiency. This study provides direct evidence in supporting the population size advantage on the trends of innovation efficiency at the economy level and provides new insight to understand the rapid development of innovation in a few populous countries.     

Controlling water temperature for efficient coal/gangue recognition

Coal and gangue (black-gray solid wastes in coal) recognition is vital to avoid waste of resources and pollution of the environment during the coal production. Considering their color/temperature is very close to each other, the traditional visible image and infrared image analyzing method is hard to obtain satisfied recognition efficiency. Therefore, a new idea of the ‘liquid intervention + infrared monitoring’ method was proposed to improve the recognition efficiency. In this article, the coal/gangue recognition experiments with different water temperatures were conducted, and the infrared thermal imager was used to record temperature variation after water intervention. The results show that when the water temperature is lower than the ambient temperature, the temperature difference between coal and gangue reaches the maximum value within 10 s, which is five times that without water intervention. The mean value of temperature difference between coal and gangue shows an approximate linear downward trend with the increasing of water temperature. The results indicate that under the condition of water intervention, it is recommended to choose water with a temperature below the ambient air, which may be a new approach to improve the coal/gangue recognition efficiency under different complex environments in underground coal mines.     

Portrait Segmentation Using Ensemble of Heterogeneous Deep-Learning Models

Image segmentation plays a central role in a broad range of applications, such as medical image analysis, autonomous vehicles, video surveillance and augmented reality. Portrait segmentation, which is a subset of semantic image segmentation, is widely used as a preprocessing step in multiple applications such as security systems, entertainment applications, video conferences, etc. A substantial amount of deep learning-based portrait segmentation approaches have been developed, since the performance and accuracy of semantic image segmentation have improved significantly due to the recent introduction of deep learning technology. However, these approaches are limited to a single portrait segmentation model. In this paper, we propose a novel approach using an ensemble method by combining multiple heterogeneous deep-learning based portrait segmentation models to improve the segmentation performance. The Two-Models ensemble and Three-Models ensemble, using a simple soft voting method and weighted soft voting method, were experimented. Intersection over Union (IoU) metric, IoU standard deviation and false prediction rate were used to evaluate the performance. Cost efficiency was calculated to analyze the efficiency of segmentation. The experiment results show that the proposed ensemble approach can perform with higher accuracy and lower errors than single deep-learning-based portrait segmentation models. The results also show that the ensemble of deep-learning models typically increases the use of memory and computing power, although it also shows that the ensemble of deep-learning models can perform more efficiently than a single model with higher accuracy using less memory and less computing power.     

A Theoretical Perspective of the Photochemical Potential in the Spectral Performance of Photovoltaic Cells

We present a novel theoretical approach to the problem of light energy conversion in thermostated semiconductor junctions. Using the classical model of a two-level atom, we deduced formulas for the spectral response and the quantum efficiency in terms of the input photons’ non-zero chemical potential. We also calculated the spectral entropy production and the global efficiency parameter in the thermodynamic limit. The heat transferred to the thermostat results in a dissipative loss that appreciably controls the spectral quantities’ behavior and, therefore, the cell’s performance. The application of the obtained formulas to data extracted from photovoltaic cells enabled us to accurately interpolate experimental data for the spectral response and the quantum efficiency of cells based on Si-, GaAs, and CdTe, among others.     

Generic security analysis framework for quantum secure direct communication

Quantum secure direct communication provides a direct means of conveying secret information via quantum states among legitimate users. The past two decades have witnessed its great strides both theoretically and experimentally. However, the security analysis of it still stays in its infant. Some practical problems in this field to be solved urgently, such as detector efficiency mismatch, side-channel effect and source imperfection, are propelling the birth of a more impeccable solution. In this paper, we establish a new framework of the security analysis driven by numerics where all the practical problems may be taken into account naturally. We apply this framework to several variations of the DL04 protocol considering real-world experimental conditions. Also, we propose two optimizing methods to process the numerical part of the framework so as to meet different requirements in practice. With these properties considered, we predict the robust framework would open up a broad avenue of the development in the field.     

Calculation of strong-collision dissociation rate constants from NASA thermodynamic polynomials

A new method for calculating low-pressure strong-collision rate constants of dissociation and recombination reactions was proposed. The method is based on determining the density of states of the internal degrees of freedom of the reactant molecule by applying the inverse Laplace transform to the respective partition function, which, in turn, is calculated from the thermodynamic properties in the form of NASA polynomials. The proposed model is universal in the sense that the required NASA polynomials can be calculated using molecular properties obtained by various methods, both theoretical and experimental or a combination thereof. In the present study, the NASA polynomials were taken from the available databases or calculated from thermodynamic functions, either tabulated or determined by statistical mechanics in the rigid-rotor harmonic-oscillator approximation, with a simple anharmonicity correction introduced when necessary. In addition, a model for calculating the rotational factor is developed and tested. It is based on determination of the centrifugal barrier as a function of the dissociating bond length at a given rotational energy through calculating the principal moments of inertia of the molecule at each step of elongation of the bond. The proposed approach is exemplified for the dissociation of the H₂O, HO₂, and H₂O₂ molecules and the corresponding reverse reactions. A comparison with the available experimental data made it possible to estimate the weak-collision efficiency. At high temperatures, for all the reactions studied, the weak collision efficiency appears to be quite reasonable, whereas, at low temperatures, the situation is unsatisfactory, except perhaps for H₂O dissociation. Given that the energy threshold E₀ of dissociation reactions is typically well known, the calculated and measured dissociation rate constants are represented and handled in terms of the preexponential factor A(T) in the expression k(T) = A(T)exp(−E₀/RT). A new formula for fitting A(T) was proposed: log A(T) = a + b(1000/T) + c(1000/T)^p, which turned out to be a good approximation for the preexponential factors of not only the rate constants but also the equilibrium constants.     

Technologies to improve the sensitivity of existing chromatographic methods used for bioanalytical studies

Chromatographic method has long been recognized as the most widely used separation method in bioanalytical research. However, the relatively low sensitivity of existing chromatographic methods remains a significant challenge, as the requirements for experimental procedures become more demanding. This review discusses the main causes for the low sensitivity of chromatographic methods and aims to introduce different technologies for enhancing their sensitivity in the following aspects: (i) different pretreatment methods for improving clean-up efficiency and recovery; (ii) derivatization step for altering the chromatographic behavior of analytes and enhancing MS ionization efficiency; (iii) optimal LC–MS conditions and appropriate separation mechanism; and (iv) applications of other chromatographic methods, including miniaturized LC, 2D-LC, 2D-GC, and supercritical fluid chromatography. Altogether, this review is devoted to summarizing the recent technologies reported in the literature and providing new strategies for the detection of bioanalytes.