Since their introduction in 2017, the efficiency of lead‐free halide perovskite solar cells based on Cs2AgBiBr6 has not exceeded 3%. The limiting bottlenecks are attributed to a low electron diffusion length, self‐trapping events and poor selectivity of the contacts, leading to large non‐radiative VOC losses. Here, 2D/3D hybrid double perovskites are introduced for the first time, using phenethyl ammonium as the constituting cation. The resulting solar cells show an increased efficiency of up to 2.5% for the champion cells and 2.03% on average, marking an improvement by 10% compared to the 3D reference on mesoporous TiO2. The effect is mainly due to a VOC improvement by up to 70 mV on average, yielding a maximum VOC of 1.18 V using different concentrations of phenethylammonium bromide. While these are among the highest reported VOC values for Cs2AgBiBr6 solar cells, the effect is attributed to a change in recombination behavior within the full device and a better selectivity at the interface toward the hole transporting material (HTM). This explanation is supported by voltage‐dependent external quantum efficiency, as well as photoelectron spectroscopy, revealing a better energy level alignment and thus a better hole‐extraction and improved electron blocking at the HTM interface.

A key direction toward managing extrinsic instabilities in perovskite solar cells (PSCs) is encapsulation. Thus, a suitable sealing layer is required for an efficient device encapsulation, preventing moisture and oxygen ingression into the perovskite layer. In this work, a solution-based, low-cost, and commercially available bilayer structure of poly(methyl methacrylate)/styrene-butadiene (PMMA/SB) is investigated for PSCs encapsulation. Encapsulated devices retained 80% of the initial power conversion efficiency (PCE) at 85 °C temperature and 85% relative humidity after 100 h, while reference devices without SB (only PMMA) suffer from rapid and intense degradation after only 2 h, under the same condition. In addition, encapsulated devices retained 95% of the initial PCE under −15 °C freezing temperature after 6 h and retained ∼80% of the initial PCE after immersion in HCl (37%) for 90 min. Moreover, applying an additional aluminum metal sheet on the PMMA/SB protective bilayer leads to the improvement of device stability up to 500 h under outdoor illumination, retaining almost 90% of the initial PCE. Considering the urge to develop reliable, scalable, and simple encapsulation for future large-area PSCs, this work establishes solution-based bilayer encapsulation, which is applicable for flexible solar modules as well as other optoelectronic devices such as light-emitting devices and photodetectors.

BackgroundQuantitative electroencephalogram (EEG) is one neuroimaging technique that has been shown to differentiate patients with major depressive disorder (MDD) and non-depressed healthy volunteers (HV) at the group-level, but its diagnostic potential for detecting differences at the individual level has yet to be realized. Quantitative EEGs produce complex data sets derived from digitally analyzed electrical activity at different frequency bands, at multiple electrode locations, and under different vigilance (eyes open vs. closed) states, resulting in potential feature patterns which may be diagnostically useful, but detectable only with advanced mathematical models.MethodsThis paper uses a data mining methodology for classifying EEGs of 53 MDD patients and 43 HVs. This included: (a) pre-processing the data, including cleaning and normalization, applying Linear Discriminant Analysis (LDA) to map the features into a new feature space; and applying Genetic Algorithm (GA) to identify the most significant features; (b) building predictive models using the Decision Tree (DT) algorithm to discover rules and hidden patterns based on the reduced and mapped features; and (c) evaluating the models based on the accuracy and false positive values on the EEG data of MDD and HV participants. Two categories of experiments were performed. The first experiment analyzed each frequency band individually, while the second experiment analyzed the bands together.ResultsApplication of LDA and GA markedly reduced the total number of utilized features by ≥ 50 % and, with all frequency bands analyzed together, the model showed average classification accuracy (MDD vs. HV) of 80 %. The best results from model testing with additional test EEG recordings from 9 MDD patients and 35 HV individuals demonstrated an accuracy of 80 % and showed an average sensitivity of 70 %, a specificity of 76 %, and a positive (PPV) and negative predictive value (NPV) of 74 and 75 %, respectively.ConclusionsThese initial findings suggest that the proposed automated EEG analytical approach could be a useful adjunctive diagnostic approach in clinical practice.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-015-0227-6) contains supplementary material, which is available to authorized users.

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A wide range of diseases and conditions are monitored or diagnosed from blood plasma, but the ability to analyze a whole blood sample with the requirements for a point-of-care device, such as robustness, user-friendliness, and simple handling, remains unmet. Microfluidics technology offers the possibility not only to work fresh thumb-pricked whole blood but also to maximize the amount of the obtained plasma from the initial sample and therefore the possibility to implement multiple tests in a single cartridge. The microfluidic design presented in this paper is a combination of cross-flow filtration with a reversible electroosmotic flow that prevents clogging at the filter entrance and maximizes the amount of separated plasma. The main advantage of this design is its efficiency, since from a small amount of sample (a single droplet $10 ll) almost 10% of this (approx 1 ll) is extracted and collected with high purity (more than 99%) in a reasonable time (5-8 min). To validate the quality and quantity of the separated plasma and to show its potential as a clinical tool, the microfluidic chip has been combined with lateral flow immunochromatography technology to perform a qualitative detection of the thyroid-stimulating hormone and a blood panel for measuring cardiac Troponin and Creatine Kinase MB. The results from the microfluidic system are comparable to previous commercial lateral flow assays that required more sample for implementing fewer tests. V C 2015 AIP Publishing LLC. [http://dx