Model-based optical and electrical characterization of dye-sensitized solar cells

We present an experimentally validated coupled optical and electrical model for the dye-sensitized solar cell. The light absorption and subsequent charge generation in the photoactive layer is calculated accurately using coherent and incoherent optics. The charge generation function is then used as a source term in the electron continuity equation of the electrical model. The optical model allows to precisely analyze the reflection and absorbance losses in the individual layers. By comparing the calculated and measured quantum efficiency of a test device, one can assess the electron recombination losses at short-circuit conditions. We conclude with an integrated power loss analysis to quantify the contribution of the respective optical and electric loss mechanisms

Coupled Optical and Electronic Modeling of Dye-Sensitized Solar Cells for Steady-State Parameter Extraction

The design and development of dye-sensitized solar cells (DSCs) is currently often realized on an empirical basis. In view of assisting in this optimization process, we present the framework of a model which consists in a coupled optical and electrical model of the DSC. The experimentally validated optical model, based on a ray-tracing algorithm, allows accurate determination of the internal quantum efficiency of devices, an important parameter that is not easily estimated. Coupling the output of the optical model-the dye absorption rate-to an electrical model for charge generation, transport, and first-order (linear) recombination allows extraction of a set of intrinsic parameters from steady-state photocurrent measurements, such as the diffusion length or the dye electron injection efficiency. Importantly, the sources of optical and electric losses in the losses). The model has been validated for two dye systems (Z907 and C101) and the strong effect of the presence of Li+ ions in the electrolyte on intrinsic parameters is confirmed. This optoelectronic model of the DSC is a significant step toward a future systematic model-assisted optimization of DSC devices

D-pi-A Dye System Containing Cyano-Benzoic Acid as Anchoring Group for Dye-Sensitized Solar Cells

A D-pi-A dye (KM-1) incorporating cyanobenzoic acid as a new acceptor/anchoring group has been synthesized for dye-sensitized solar cells (DSCs) with a high molar extinction coefficient of 66 700 M-1 cm(-1) at 437 nm. Theoretical calculations show that the hydrogen bond between -CN and surface hydroxyl leads to the most stable configuration on the surface of TiO2. In addition, the adsorption of the dye on TiO2 follows a Brunauer-Emmett-Teller (BET) isotherm. Multilayer adsorption of KM-1 on TiO2 seems to take place particularly at higher dye concentrations. DSC device using KM-1 reached a maximum incident photon-to-current conversion efficiency (IPCE) of 84%, with a solar to electric power conversion efficiency (PCE) of 3.3% at AM1.5 G illumination (100 mW cm(-2)). This new type of anchoring group paves a way to light harvesting with strong binding to the metal oxide surface. design new dyes that combine good visibl

Charge separation: From the topology of molecular electronic transitions to the dye/semiconductor interfacial energetics and kinetics

Charge separation properties, that is the ability of a chromophore, or a chromophore/semiconductor interface, to separate charges upon light absorption, are crucial characteristics for an efficient photovoltaic device. Starting from this concept, we devote the first part of this book chapter to the topological analysis of molecular electronic transitions induced by photon capture. Such analysis can be either qualitative or quantitative, and is presented here in the framework of the reduced density matrix theory applied to single-reference, multiconfigurational excited states. The qualitative strategies are separated into density-based and wave function-based approaches, while the quantitative methods reported here for analysing the photoinduced charge transfer nature are either fragment-based, global or statistical. In the second part of this chapter we extend the analysis to dye-sensitized metal oxide surface models, discussing interfacial charge separation, energetics and electron injection kinetics from the dye excited state to the semiconductor conduction band states

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders