Enhancing the Optoelectronic Performance of Perovskite Solar Cells via a Textured CH3NH3PbI3 Morphology

Perovskite‐based solar cells are generally assembled as planar structures comprising a flat organoammonium metal halide perovskite layer, or mesoscopic structures employing a mesoporous metal‐oxide scaffold into which the perovskite material is infiltrated. To present, little attention has been directed toward the texturing of the perovskite material itself. Herein, a textured CH₃NH₃PbI₃ morphology formed through a thin mesoporous TiO₂ seeding layer and a gas‐assisted crystallization method is reported. The textured morphology comprises a multitiered nanostructure, which allows for significant improvements in the light harvesting and charge extraction performance of the solar cells. Due to these improvements, average short‐circuit current densities for a batch of 28 devices are in excess of 22 mA cm^?2, and the maximum recorded power conversion efficiency is 16.3%. The performance gains concomitant with this textured CH₃NH₃PbI₃ morphology provide further insights into how control of the perovskite microstructure can be used to enhance the cell performance.     

Passive and active acoustics using an autonomous wave glider

The recently developed wave glider has the potential to be an effective unmanned platform for acoustic applications. We present the results of a variety of experiments that quantify this potential. The radiated self‐noise of the autonomous platform is evaluated using an integrated passive acoustic recorder during a set of field trials off the coast of Hawaii. We present the radiated noise spectra from these trials to illustrate the dependence on hydrophone location and sea state. Using the same instrumentation, we demonstrate the ability of a modified wave glider to detect marine mammals using passive acoustic monitoring techniques. We also evaluate the performance of the wave glider operating as an active acoustic gateway, highlighting the potential of this platform to serve as a navigation reference and communications relay for scientific, industrial, and military subsea assets. To demonstrate the potential of the wave glider platform to support acoustic navigation, we assess the performance of time‐of‐flight range estimation and seafloor transponder localization. These tests were performed using commercial off‐the‐shelf acoustic positioning hardware integrated with the wave glider to illustrate that the low self‐noise of the wave glider makes it possible to achieve acoustic positioning performance similar to previously reported results. Finally, we show that the glider can operate as a station‐keeping surface communications gateway and provide recommendations for its use. © 2012 Wiley Periodicals, Inc.     

Compositional learning of mutually recursive procedural systems

This paper presents a compositional approach to active automata learning of Systems of Procedural Automata (SPAs), an extension of Deterministic Finite Automata (DFAs) to systems of DFAs that can mutually call each other. SPAs are of high practical relevance, as they allow one to efficiently learn intuitive recursive models of recursive programs after an easy instrumentation that makes calls and returns observable. Key to our approach is the simultaneous inference of individual DFAs for each of the involved procedures via expansion and projection: membership queries for the individual DFAs are expanded to membership queries of the entire SPA, and global counterexample traces are transformed into counterexamples for the DFAs of concerned procedures. This reduces the inference of SPAs to a simultaneous inference of the DFAs for the involved procedures for which we can utilize various existing regular learning algorithms. The inferred models are easy to understand and allow for an intuitive display of the procedural system under learning that reveals its recursive structure. We implemented the algorithm within the LearnLib framework in order to provide a ready-to-use tool for practical application which is publicly available on GitHub for experimentation.

     

Plant-to-Plant Variability in Root Metabolite Profiles of 19 Arabidopsis thaliana Accessions Is Substance-Class-Dependent

Natural variation of secondary metabolism between different accessions of Arabidopsis thaliana (A. thaliana) has been studied extensively. In this study, we extended the natural variation approach by including biological variability (plant-to-plant variability) and analysed root metabolic patterns as well as their variability between plants and naturally occurring accessions. To screen 19 accessions of A. thaliana, comprehensive non-targeted metabolite profiling of single plant root extracts was performed using ultra performance liquid chromatography/electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC/ESI-QTOF-MS) and gas chromatography/electron ionization quadrupole mass spectrometry (GC/EI-QMS). Linear mixed models were applied to dissect the total observed variance. All metabolic profiles pointed towards a larger plant-to-plant variability than natural variation between accessions and variance of experimental batches. Ratios of plant-to-plant to total variability were high and distinct for certain secondary metabolites. None of the investigated accessions displayed a specifically high or low biological variability for these substance classes. This study provides recommendations for future natural variation analyses of glucosinolates, flavonoids, and phenylpropanoids and also reference data for additional substance classes.     

Substrate‐Determined Diastereoselectivity in an Enzymatic Carboligation

Thiamine diphosphate‐dependent enzymes catalyze the formation of C?C bonds, thereby generating chiral secondary or tertiary alcohols. By the use of vibrational circular dichroism (VCD) spectroscopy we studied the stereoselectivity of carboligations catalyzed by YerE, a carbohydrate‐modifying enzyme from Yersinia pseudotuberculosis. Conversion of the non‐physiological substrate (R)‐3‐methylcyclohexanone led to an R,R‐configured tertiary alcohol (diastereomeric ratio (dr) >99:1), whereas the corresponding reaction with the S enantiomer gave the S,S‐configured product (dr>99:1). This suggests that YerE‐catalyzed carboligations can undergo either an R‐ or an S‐specific pathway. We show that, in this case, the high stereoselectivity of the YerE‐catalyzed reaction depends on the substrate's preference to acquire a low‐energy conformation.     

Multi-sensoral and automated derivation of inundated areas using TerraSAR-X and ENVISAT ASAR data

During recent years, synthetic aperture radar (SAR) data have been increasingly used for flood mapping. New radar satellites especially, such as TerraSAR-X, Radarsat-2 and COSMO-SkyMed, provide high-resolution data with high potential for fast and reliable detection of inundated areas. This article compares three simple approaches to derive water areas from SAR data in relation to the German–Vietnamese project, Water-related Information System for the Sustainable Development of the Mekong Delta (WISDOM). Two methods are pixel based and use histogram-based grey-level thresholds, as well as a homogeneity criterion for classification. The third approach is object based and applies characteristic attributes of water objects such as grey value, texture and relations to neighbouring objects. Further discussed are the influence of a variation of the thresholds and the challenges to validate water masks derived from active remote-sensing data. We implemented one of the introduced approaches for surface water derivation in a water mask processor for automatic water mask calculation from radar satellite imagery (WaMaPro). This fully automatic processing chain was developed to process TerraSAR-X and Environmental Satellite Advanced Synthetic Aperture Radar (ENVISAT ASAR) imagery in order to meet the demands for automatic flood monitoring.     

The optimal unbiased value estimator and its relation to LSTD,TD and MC

In this analytical study we derive the optimal unbiased value estimator (MVU) and compare its statistical risk to three well known value estimators: Temporal Difference learning (TD), Monte Carlo estimation (MC) and Least-Squares Temporal Difference Learning (LSTD). We demonstrate that LSTD is equivalent to the MVU if the Markov Reward Process (MRP) is acyclic and show that both differ for most cyclic MRPs as LSTD is then typically biased. More generally, we show that estimators that fulfill the Bellman equation can only be unbiased for special cyclic MRPs. The reason for this is that at each state the bias is calculated with a different probability measure and due to the strong coupling by the Bellman equation it is typically not possible for a set of value estimators to be unbiased with respect to each of these measures. Furthermore, we derive relations of the MVU to MC and TD. The most important of these relations is the equivalence of MC to the MVU and to LSTD for undiscounted MRPs in which MC has the same amount of information. In the discounted case this equivalence does not hold anymore. For TD we show that it is essentially unbiased for acyclic MRPs and biased for cyclic MRPs. We also order estimators according to their risk and present counter-examples to show that no general ordering exists between the MVU and LSTD, between MC and LSTD and between TD and MC. Theoretical results are supported by examples and an empirical evaluation.     

Regularization techniques for ill-posed inverse problems in data assimilation

Optimal state estimation from given observations of a dynamical system by data assimilation is generally an ill-posed inverse problem. In order to solve the problem, a standard Tikhonov, or L₂, regularization is used, based on certain statistical assumptions on the errors in the data. The regularization term constrains the estimate of the state to remain close to a prior estimate. In the presence of model error, this approach does not capture the initial state of the system accurately, as the initial state estimate is derived by minimizing the average error between the model predictions and the observations over a time window. Here we examine an alternative L₁ regularization technique that has proved valuable in image processing. We show that for examples of flow with sharp fronts and shocks, the L₁ regularization technique performs more accurately than standard L₂ regularization.