Defect tolerant device geometries

The term defect tolerance is widely used in literature to describe materials such as lead-halides which exhibit long non-radiative lifetimes of carriers despite possessing a large concentration of point defects. Studies on defect tolerance of materials mostly look at the properties of the host material and/or the chemical nature of defects that affect the capture coefficients of defects. However, the recombination activity of a defect is not only a function of its capture coefficients alone but are also dependent on the electrostatics and the design of the layer stack of a photovoltaic device. Here we study the influence of device geometry on defect tolerance by combining calculations of capture coefficients with device simulations. We derive generic device design principles which can inhibit recombination inside a photovoltaic device for a given set of capture coefficients based on the idea of slowing down the slower of the two processes (electron and hole capture) even further by modifying electron and hole injection into the absorber layer. We use the material parameters and typical p-i-n device geometry representing methylammonium lead halide perovskites solar cells to illustrate the application of our generic design principles to improve specific devices .Comment: 27 pages, 9 Figure

Empirical evaluation of scoring functions for Bayesian network model selection

In this work, we empirically evaluate the capability of various scoring functions of Bayesian networks for recovering true underlying structures. Similar investigations have been carried out before, but they typically relied on approximate learning algorithms to learn the network structures. The suboptimal structures found by the approximation methods have unknown quality and may affect the reliability of their conclusions. Our study uses an optimal algorithm to learn Bayesian network structures from datasets generated from a set of gold standard Bayesian networks. Because all optimal algorithms always learn equivalent networks, this ensures that only the choice of scoring function affects the learned networks. Another shortcoming of the previous studies stems from their use of random synthetic networks as test cases. There is no guarantee that these networks reflect real-world data. We use real-world data to generate our gold-standard structures, so our experimental design more closely approximates real-world situations. A major finding of our study suggests that, in contrast to results reported by several prior works, the Minimum Description Length (MDL) (or equivalently, Bayesian information criterion (BIC)) consistently outperforms other scoring functions such as Akaike\u27s information criterion (AIC), Bayesian Dirichlet equivalence score (BDeu), and factorized normalized maximum likelihood (fNML) in recovering the underlying Bayesian network structures. We believe this finding is a result of using both datasets generated from real-world applications rather than from random processes used in previous studies and learning algorithms to select high-scoring structures rather than selecting random models. Other findings of our study support existing work, e.g., large sample sizes result in learning structures closer to the true underlying structure; the BDeu score is sensitive to the parameter settings; and the fNML performs pretty well on small datasets. We also tested a greedy hill climbing algorithm and observed similar results as the optimal algorithm

NCK2 Is Significantly Associated with Opiates Addiction in African-Origin Men

Substance dependence is a complex environmental and genetic disorder with significant social and medical concerns. Understanding the etiology of substance dependence is imperative to the development of effective treatment and prevention strategies. To this end, substantial effort has been made to identify genes underlying substance dependence, and in recent years, genome-wide association studies (GWASs) have led to discoveries of numerous genetic variants for complex diseases including substance dependence. Most of the GWAS discoveries were only based on single nucleotide polymorphisms (SNPs) and a single dichotomized outcome. By employing both SNP- and gene-based methods of analysis, we identified a strong (odds ratio = 13.87) and significant (P value = 1.33E−11) association of an SNP in the NCK2 gene on chromosome 2 with opiates addiction in African-origin men. Codependence analysis also identified a genome-wide significant association between NCK2 and comorbidity of substance dependence (P value = 3.65E−08) in African-origin men. Furthermore, we observed that the association between the NCK2 gene (P value = 3.12E−10) and opiates addiction reached the gene-based genome-wide significant level. In summary, our findings provided the first evidence for the involvement of NCK2 in the susceptibility to opiates addiction and further revealed the racial and gender specificities of its impact

SVSI: Fast and Powerful Set-Valued System Identification Approach to Identifying Rare Variants in Sequencing Studies for Ordered Categorical Traits: SVSIfor Genetic Association Studies

For genetic association studies that involve an ordered categorical phenotype, we usually either regroup multiple categories of the phenotype into two categories (“cases” and “controls”) and then apply the standard logistic regression (LG), or apply ordered logistic (oLG) or ordered probit (oPRB) regression which accounts for the ordinal nature of the phenotype. However, these approaches may lose statistical power or may not control type I error rate due to their model assumption and/or instable parameter estimation algorithm when the genetic variant is rare or sample size is limited. Here to solve this problem, we propose a set-valued (SV) system model, which assumes that an underlying continuous phenotype follows a normal distribution, to identify genetic variants associated with an ordinal categorical phenotype. We couple this model with a set-valued system identification algorithm to identify all the key system parameters. Simulations and two real data analyses show that SV and LG accurately controlled the Type I error rate even at a significance level of 10−6 but not oLG and oPRB in some cases. LG had significantly smaller power than the other three methods due to disregarding of the ordinal nature of the phenotype, and SV had similar or greater power than oLG and oPRB. For instance, in a simulation with data generated from an additive SV model with odds ratio of 7.4 for a phenotype with three categories, a single nucleotide polymorphism with minor allele frequency of 0.75% and sample size of 999 (333 per category), the power of SV, oLG and LG models were 70%, 40% and <1%, respectively, at a significance level of 10−6. Thus, SV should be employed in genetic association studies for ordered categorical phenotype

Multilayer Capacitances: How Selective Contacts Affect Capacitance Measurements of Perovskite Solar Cells

Capacitance measurements as a function of voltage, frequency and temperature are useful tools to identify fundamental parameters that affect solar cell operation. Techniques such as capacitance-voltage (CV), Mott-Schottky analysis and thermal admittance spectroscopy (TAS) measurements are therefore frequently employed to obtain relevant parameters of the perovskite absorber layer in perovskite solar cells. However, state-of-the-art perovskite solar cells employ thin electron and hole transport layers that improve contact selectivity. These selective contacts are often quite resistive in nature, which implies that their capacitances will contribute to the total capacitance and thereby affect the extraction of the capacitance of the perovskite layer. Based on this premise, we develop a simple multilayer model that considers the perovskite solar cell as a series connection of the geometric capacitance of each layer in parallel with their voltage-dependent resistances. Analysis of this model yields fundamental limits to the resolution of spatial doping profiles and minimum values of doping/trap densities, built-in voltages and activation energies. We observe that most of the experimental capacitance-voltage-frequency-temperature data, calculated doping/trap densities and activation energies reported in literature are within these cut-off values derived, indicating that the capacitance response of the perovskite solar cell is indeed strongly affected by the capacitance of its selective contacts

Application of comprehensive advanced geological prediction technology in Da-puling tunnel

For the Da-puling tunnel of Puqing Expressway in Guangxi, the advanced geological prediction is carried out by combining TSP long-distance forecast method with short distance geological radar method. This paper describes the principle of seismic wave propagation in elastic medium, as well as the key points of data processing and analysis, some requirements that should be paid attention to the field test and scientific way of image interpretation put forward to improve the accuracy of prediction; When TSP is deployed, it should be sharp angle with potential joint surface. P-wave reacts surrounding rock properties, the shear wave is closely related to the transverse skeleton of medium. In data interpretation, it is necessary to focus on the analysis of the characteristics of P-wave and S-wave, weakening Poisson’s ratio and Young’s modulus. TSP and GPR can achieve the mutual complement and improve the detection accuracy