Self-supported hollow Co(OH)2/NiCo sulfide hybrid nanotube arrays as efficient electrocatalysts for overall water splitting

Journal of Solid State Electrochemistry - Developing low-cost and earth-abundant electrocatalysts with high activity for both hydrogen evolution reaction (HER) and oxygen evolution reaction (OER)...     

Syntheses,characterization of and studies on the electrochemical behaviour of ferrocenyl dithiophosphonates and 4-methoxyphenyl dithiophosphonates

Some 1,3-dithiadiphosphetane 2,4-disulfides (X₂P₂S₄, X: Fc, FcLR; X: CH₃O?C₆H₄?, LR) were allowed to react with alcohols to obtain dithiophosphonic acids (X(OR)PS₂H). These were converted to the corresponding ammonium salts. The salts were of the structures [Fc(OR)PS₂]^?[NH₄]⁺, R: 3-methyl-1-butyl- for I; 1-phenyl-1-propyl- for II; 3-pentyl- for III; 3-phenyl-1-propyl- for IV and [CH₃O?C₆H₄(OR)PS₂]^?[NH₄]⁺, R: 3-methyl-1-butyl- for V and 1-phenyl-1-propyl- for VI. To the best of our knowledge, all the compounds except V were prepared for the first time.

The compounds synthesized were characterized by elemental analysis, NMR (¹H, ¹³C, ³¹P), MS, FTIR, and Raman spectroscopies. Electrochemical behaviors of I–VI at disposable pencil graphite electrode (PGE) were investigated by using cyclic voltammetry (CV) and square-wave voltammetry (SWV). Adsorption and diffusion patterns of all the compounds on the PGE were also studied.

Two electroactive groups were identified in the compounds I–IV and only one in V and VI. The ferrocenyl groups of I-IV were oxidized at around 0.4 V. The same compounds display a second, more intense CV band at 0.8 V. The corresponding band for the compounds V–VI appears at around 0.6 V with a much weaker intensity. It is suggested that the ferrocenyl group introduced into the structures stabilizes the radical species formed as the product of the oxidation of the dithiophosphonato group.     

Effects of Dielectric Barrier Discharge Plasma on Physicochemical Characteristics,Mechanical Properties and Biocompatibility of Silk/PVA Nanofibers

Plasma Chemistry and Plasma Processing - This work reports an investigation of the discharge characteristics of atmospheric dielectric barrier discharge (DBD) plasma in terms of I-V curves and...     

Artificial neural network‐based modeling of snow properties using field data and hyperspectral imagery

This study attempts to model snow wetness and snow density of Himalayan snow cover using a combination of Hyperspectral image processing and Artificial Neural Network (ANN). Initially, a total of 300 spectral signature measurements, synchronized with snow wetness and snow density, were collected in the field. The spectral reflectance of snow was then modeled as a function of snow properties using ANN. Four snow wetness and three snow density models were developed. A strong correlation was observed in near‐infrared and shortwave‐infrared region. The correlation analysis of ANN modeled snow density and snow wetness showed a strong linear relationship with field‐based data values ranging from 0.87–0.90 and 0.88–0.91, respectively. Our results indicate that an Artificial Intelligence (AI) approach, using a combination of Hyperspectral image processing and ANN, can be efficiently used to predict snow properties (wetness and density) in the Himalayan region. Recommendations for resource managers

• Snow properties, such as snow wetness and snow density are mainly investigated through field‐based survey but rugged terrains, difficult weather conditions, and logistics management issues establish remote sensing as an efficient alternative to monitor snow properties, especially in the mountain environment.
• Although Hyperspectral remote sensing is a powerful tool to conduct the quantitative analysis of the physical properties of snow, only a few studies have used hyperspectral data for the estimation of snow density and wetness in the Himalayan region. This could be because of the lack of synchronized snow properties data with field‐based spectral acquisitions.
• In combination with Hyperspectral image processing, Artificial Neural Network (ANN) can be a useful tool for effective snow modeling because of its ability to capture and represent complex input‐output relationships.
• Further research into understanding the applicability of neural networks to determine snow properties is required to obtain results from large snow cover areas of the Himalayan region.

     

Exact Solution of a(2+1)-Dimensional Anisotropic Star in Finch and Skea Spacetime

We provide a new class of interior solution of a(2+1)-dimensional anisotropic star in Finch and Skea spacetime corresponding to the BTZ black hole. We develop the model by considering the MIT bag model EOS and a particular ansatz for the metric function grrproposed by Finch and Skea [M.R. Finch and J.E.F. Skea, Class. Quantum.Grav. 6(1989) 467]. Our model is free from central singularity and satisfies all the physical requirements for the acceptability of the model.     

Thermoelectric properties of stannite-phase CuZn2AS4 (CZAS; A=Al,Ga and In) nanocrystals for solar energy conversion applications

The current study aimed to comprehensively investigate structural, electronic, optical and transport properties of quaternary semiconductor CuZn₂AS₄ (CZAS; A=Al, Ga and In) nanocrystals (NCs). Based on energy considerations, the stannite structure (I-42m; No. 121) is found to be more stable than the kesterite (I-4; No.82) and wurtzite (P6₃mc; No.186) type structures. By means of hybrid functional calculations, these nanocrystals have direct band gap of 0.81–1.71 eV with a high absorption coefficient of >10⁴ cm^?1, which are well-suited for use in solar energy-conversion applications. Some of the latest advances in applications of these nanocrystals in thermoelectric applications are also highlighted in the current study. It is observed that transport coefficients of these materials are found to be nearly direction independent and isotropic. All three samples are p-type conductors at room temperature. Especially, the Seebeck coefficient of CuZn₂AlS₄ is even larger than that of CuZn₂GaS₄ and CuZn₂InS₄ under the studied carrier concentration and temperature region. The maximum figure of merit (ZT) reaches 0.982 (0.977), 0.984 (0.974) and 0.53 (0.955) for p-type (n-type) CuZn₂AlS₄, CuZn₂GaS₄, and CuZn₂InS₄, respectively, at 300 K. The high Seebeck coefficients, high figure of merit and low thermal conductivities make these systems good candidates for high-efficiency thermoelectric conversion applications.     

Exact interior solutions in 2+1-dimensional spacetime

An exact formulation of two-dimensional chiral hydrodynamics with diffeomorphism and conformal anomalies is provided. The constitutive relation involving the stress tensor is computed. It reveals a one parameter class of solutions which is a new result. For a particular value of this parameter, the results found in the gradient expansion scheme are reproduced. Moreover, the constitutive relation is analogous to the corresponding relation for an ideal fluid, appropriately modified to include the chirality property, which has also been derived here.     

Chiral Thirring–Wess model with Faddeevian regularization

Replacing vector type of interaction of the Thirring–Wess model by the chiral type a new model is presented which is termed here as chiral Thirring–Wess model. Ambiguity parameters of regularization are so chosen that the model falls into the Faddeevian class. The resulting Faddeevian class of model in general does not possess Lorentz invariance. However we can exploit the arbitrariness admissible in the ambiguity parameters to relate the quantum mechanically generated ambiguity parameters with the classical parameter involved in the masslike term of the gauge field which helps to maintain physical Lorentz invariance instead of the absence of manifestly Lorentz covariance of the model. The phase space structure and the theoretical spectrum of this class of model have been determined through Dirac’s method of quantization of constraint system.