Performance of TF-VLS Grown InP Photovoltaic Cells

A grand challenge of photovoltaics (PV) is to find materials offering a promising combination of low costs and high efficiencies. While III-V material-based PV cells have set many world records, often their cost is much greater than other commercial cells. To help address this gap, thin-film vapor-liquid-solid (TF-VLS) grown Indium Phosphide (InP) PV cells have recently been developed, which both eliminate a key source of high costs and offer a direct bandgap of 1.34eV with potential to approach maximum theoretical efficiencies. However, the unanticipated phenomenon of open circuit voltage (Voc) degradation has prevented TF-VLS grown InP PV cells from achieving their theoretical efficiencies, which appears to be caused by effective bandgap narrowing in certain portions of the cells. To address this issue, we have developed a 3D model for these PV cells in Xyce, a SPICE-like free circuit modeling software. Our model quantifies lateral variation of TF-VLS grown cells observed in photoluminescence (PL) images with two sets of unit cell parameters. It turns out that the PL intensity correlates to PV cells of different bandgaps (Eg). Based on user-defined cutoffs, we are able to categorize the expected bandgap and reduced bandgap cells. With the addition of an appropriate shunt resistance, it is possible to explain most of current-voltage relationship with this model. Finally, we are building a web-enabled tool to allow users to upload their own heterogeneous PV cell data into our model, using a graphical user interface on nanoHUB.org, an open-access science gateway for cloud-based simulation tools and resources for research and education in nanoscale science and technology

Modal Analysis of Upright Piano Soundboards by Combining Finite Element Analysis and Computer-Aided Design

This study presents a visual model for analyzing the vibration modes of piano soundboards by combining the tools of finite element analysis and computer-aided design. Based on the predicted results from the model, changes of natural frequency and maximum displacement of the soundboard as a function of wood properties, structure, and rib size were discussed. Wood grain direction affected the mode shape of the soundboard. Among the 10 property factors investigated, density presented the greatest impact to the vibration mode of the soundboard followed by Young's modulus, shear modulus, and Poisson's ratio. Increasing the thickness of the resonance board and the use of ribs had positive impacts on the natural frequency of the soundboard. However, the amount of natural frequency was decreased for those that were lower than 100 Hz. Natural frequency increased as the intensity, density, and size of ribs increased. Rib height had a greater effect on the variation of natural frequency than the intensity, density, and rib width. In general, increases in rib intensity, density of wood species, and rib width presented negative effects on the maximum displacement

Analysis of Strength Characteristics and Energy Dissipation of Improved-Subgrade Soil of High-Speed Railway above Mined-Out Areas

To reveal the effect of sand content on the mechanical performance and energy dissipation of cement improved subgrade soil, using universal testing machine and SHPB test device, unconfined compressive strength (UCS) and impact compression strength under different impact load (0.2, 0.3, 0.4, and 0.5 MPa) were carried out for the cement improved subgrade soil with different sand content (0%, 5%, 10%, 15%, and 20%). Results show that the dynamic and static stress-strain curves of the cement improved soil have similar variation trend. With the increase of the sand content, the UCS and impact compressive strength of the cement improved soil both increase first, then decrease later, showing the form of a quadratic function. The strength growth rate and the dynamic increase factor (DIF) reach the maximum values when the sand content is 10%, which is 64.7% and 18.6% larger than that of ordinary improved subgrade soil, respectively. In addition, when the sand content increases from 0% to 20%, the specific dissipation energy increases first, and decreases later. Mixing 10% natural sand is the optimal proportion to obtain better energy dissipation capacity of the sand-cement-improved soil

Degradation Estimation Recurrent Neural Network with Local and Non-Local Priors for Compressive Spectral Imaging

In the Coded Aperture Snapshot Spectral Imaging (CASSI) system, deep unfolding networks (DUNs) have demonstrated excellent performance in recovering 3D hyperspectral images (HSIs) from 2D measurements. However, some noticeable gaps exist between the imaging model used in DUNs and the real CASSI imaging process, such as the sensing error as well as photon and dark current noise, compromising the accuracy of solving the data subproblem and the prior subproblem in DUNs. To address this issue, we propose a Degradation Estimation Network (DEN) to correct the imaging model used in DUNs by simultaneously estimating the sensing error and the noise level, thereby improving the performance of DUNs. Additionally, we propose an efficient Local and Non-local Transformer (LNLT) to solve the prior subproblem, which not only effectively models local and non-local similarities but also reduces the computational cost of the window-based global Multi-head Self-attention (MSA). Furthermore, we transform the DUN into a Recurrent Neural Network (RNN) by sharing parameters of DNNs across stages, which not only allows DNN to be trained more adequately but also significantly reduces the number of parameters. The proposed DERNN-LNLT achieves state-of-the-art (SOTA) performance with fewer parameters on both simulation and real datasets