We calculated electron inelastic mean free paths (IMFPs) for liquid water from its optical energy-loss function (ELF) for electron energies from 50 eV to 30 keV. These calculations were made with the relativistic full Penn algorithm (FPA) that has been used for previous IMFP and electron stopping-power calculations for many elemental solids. We also calculated IMFPs of water with three additional algorithms: the relativistic single-pole approximation (SPA), the relativistic simplified SPA, and the relativistic extended Mermin method. These calculations were made using the same optical ELF in order to assess any differences of the IMFPs arising from choice of the algorithm. We found good agreement among the IMFPs from the four algorithms for energies over 300 eV. For energies less than 100 eV, however, large differences became apparent. IMFPs from the relativistic TPP-2M equation for predicting IMFPs were in good agreement with IMFPs from the four algorithms for energies between 300 eV and 30 keV but there was poorer agreement for lower energies. We calculated values of the static structure factor as a function of momentum transfer from the FPA. The resulting values were in good agreement with results from first-principles calculations and with inelastic X-ray scattering spectroscopy experiments. We made comparisons of our IMFPs with earlier calculations from authors who had used different algorithms and different ELF data sets. IMFP differences could then be analyzed in terms of the algorithms and the data sets. Finally, we compared our IMFPs with measurements of IMFPs and of a related quantity, the effective attenuation length (EAL). There were large variations in the measured IMFPs and EALs (as well as their dependence on electron energy). Further measurements are therefore required to establish consistent data sets and for more detailed comparisons with calculated IMFPs.

No abstract

2D transition metal dichalcogenides (TMDs) have received widespread interest by virtue of their excellent electrical, optical, and electrochemical characteristics. Recent studies on TMDs have revealed their versatile utilization as electrocatalysts, supercapacitors, battery materials, and sensors, etc. In this study, MoS2 nanosheets are successfully assembled on the porous VS2 (P‐VS2) scaffold to form a MoS2/VS2 heterostructure. Their gas‐sensing features, such as sensitivity and selectivity, are investigated by using a quartz crystal microbalance (QCM) technique. The QCM results and density functional theory (DFT) calculations reveal the impressive affinity of the MoS2/VS2 heterostructure sensor toward ammonia with a higher adsorption uptake than the pristine MoS2 or P‐VS2 sensor. Furthermore, the adsorption kinetics of the MoS2/VS2 heterostructure sensor toward ammonia follow the pseudo‐first‐order kinetics model. The excellent sensing features of the MoS2/VS2 heterostructure render it attractive for high‐performance ammonia sensors in diverse applications.

We propose an improved method for calculating electron inelastic mean free paths (IMFPs) in solids from experimental energy-loss functions based on the Mermin dielectric function. The "extended Mermin" method employs a nonlimited number of Mermin oscillators and allows negative oscillators to take into account not only electronic transitions, as is common in the traditional approaches, but also infrared transitions and inner shell electron excitations. The use of only Mermin oscillators naturally preserves two important sum rules when extending to infinite momentum transfer. Excellent agreement is found between calculated IMFPs for Cu and experimental measurements from elastic peak electron spectroscopy. Notably improved fits to the IMFPs derived from analyses of x-ray absorption fine structure measurements for Cu and Mo illustrate the importance of the contribution of infrared transitions in IMFP calculations at low energies.

Isoelectronic cation substitution is a potential method to decrease the density of Cu‐Zn anti‐site defects in CZTSSe, thus improving the VOC and performance of CZTSSe solar cells. The proper doping concentration is determined traditionally by the trial and error approach, costing much time, and materials. How to shorten the time to find the proper doping concentration is a big challenge for the development of solar cells. Here, by utilizing the machine learning model, the authors carry out an adaptive design for predicting the optimal doping ratio of Mn2+ ions in CZTSSe solar cells for improved solar cell efficiency. With the help of machine learning prediction, the authors rapidly and efficiently find the optimal doping ratio of Mn2+ in CZTSSe solar cells to be 0.05, achieving a highest solar cell efficiency of 8.9% in experiment. Further experimental characterizations of Mn‐doped CZTSSe show that the defect in CZTSSe after Mn doping is changed from an anti‐site CuZn defect to VCu defect. Our findings suggest that machine learning is a very powerful and efficient approach to aid the development of solar cell materials for its application in the photovoltaic field.