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Perovskite–silicon tandem solar cells offer the possibility of overcoming the power conversion efficiency limit of conventional silicon solar cells. Various textured tandem devices have been presented aiming at improved optical performance, but optimizing film growth on surface-textured wafers remains challenging. Here we present perovskite–silicon tandem solar cells with periodic nanotextures that offer various advantages without compromising the material quality of solution-processed perovskite layers. We show a reduction in reflection losses in comparison to planar tandems, with the new devices being less sensitive to deviations from optimum layer thicknesses. The nanotextures also enable a greatly increased fabrication yield from 50% to 95%. Moreover, the open-circuit voltage is improved by 15 mV due to the enhanced optoelectronic properties of the perovskite top cell. Our optically advanced rear reflector with a dielectric buffer layer results in reduced parasitic absorption at near-infrared wavelengths. As a result, we demonstrate a certified power conversion efficiency of 29.80%.Designing gentle sinusoidal nanotextures enables the realization of high-efficiency perovskite–silicon solar cells

Despite a wide array of applications, deep ultra-violet light emitting diodes offer relatively poor efficiencies compared to their optical counterparts. A contributing factor is the lower light extraction efficiency due to both highly absorbing p-contacts and total internal reflection. Here, we propose a structure consisting of a hexagonal periodic array of cylindrical nanoholes in the multi-layered p-contact which are filled with platinum. This nanostructure reduces the absorption of the p-contact layer, leading to a higher emission into the n-contact compared to a planar reference. An optimum geometry of the nanostructure allows a light extraction efficiency of 15.0%, much higher than the typical 4.6% of a planar reference. While the nanostructure strongly decreases the light absorption in the p-contact, it is still not able to considerably reduce the total internal reflection. Consequently, the nanostructured p-contact should be combined with other optical strategies, such as nanopatterned sapphire substrates to increase the efficiency even further. Despite this, the nanostructure described in this work provides a readily realizable path to enhancing the light extraction efficiency of state-of-the-art deep ultra-violet light emitting diodes.

Perovskite/silicon tandem solar cells are regarded as a promising candidate to surpass current efficiency limits in terrestrial photovoltaics. Tandem solar cell efficiencies meanwhile reach more than 29%. However, present high-end perovskite/silicon tandem solar cells still suffer from optical losses. We review recent numerical and experimental perovskite/silicon tandem solar cell studies and analyse the applied measures for light management. Literature indicates that highest experimental efficiencies are obtained using fully planar perovskite top cells, being in contradiction to the outcome of optical simulations calling for textured interfaces. The reason is that the preferred perovskite top cell solution-processing is often incompatible with usual micropyramidal textures of silicon bottom cells. Based on the literature survey, we propose a certain gentle nanotexture as an example to reduce optical losses in perovskite/silicon tandem solar cells. Optical simulations using the finite-element method reveal that an intermediate texture between top and bottom cell does not yield an optical benefit when compared with optimized planar designs. A double-side textured top-cell design is found to be necessary to reduce reflectance losses by the current density equivalent of 1 mA/cm . The presented results illustrate a way to push perovskite/silicon tandem solar cell efficiencies beyond 30% by improved light management.

Recently, liquid phase crystallization of thin silicon films has emerged as a candidate for thin-film photovoltaics. On 10 μm thin absorbers, wafer-equivalent morphologies and open-circuit voltages were reached, leading to 13.2% record efficiency. However, short-circuit current densities are still limited, mainly due to optical losses at the glass-silicon interface. While nano-structures at this interface have been shown to efficiently reduce reflection, up to now these textures caused a deterioration of electronic silicon material quality. Therefore, optical gains were mitigated due to recombination losses. Here, the SMooth Anti-Reflective Three-dimensional (SMART) texture is introduced to overcome this trade-off. By smoothing nanoimprinted SiO x nano-pillar arrays with spin-coated TiO x layers, light in-coupling into laser-crystallized silicon solar cells is significantly improved as successfully demonstrated in three-dimensional simulations and in experiment. At the same time, electronic silicon material quality is equivalent to that of planar references, allowing to reach V oc values above 630 mV. Furthermore, the short-circuit current density could be increased from 21.0 mA cm −2 for planar reference cells to 24.5 mA cm −2 on SMART textures, a relative increase of 18%. External quantum efficiency measurements yield an increase for wavelengths up to 700 nm compared to a state-of-the-art solar cell with 11.9% efficiency, corresponding to a j sc , EQE gain of 2.8 mA cm −2 .

Multi-junction photovoltaics approaches are being explored to mitigate thermalization losses that occur in the absorption of high-energy photons. However, the design of tandem cells faces challenges such as light reflection and parasitic absorption. Nanostructures have emerged as promising solutions due to their anti-reflection properties, which enhances light absorption. III-V nanowires (NWs) solar cells can achieve strong power conversion efficiencies, offering the advantage of potentially integrating tunnel diodes within the same fabrication process. Metal halide perovskites (MHPs) have gained attention for their optoelectronic attributes and cost-effectiveness. Notably, both material classes allow for tunable bandgaps. This study explores the integration of MHPs with III-V NWs solar cells in both two-terminal and three-terminal configurations. Our primary focus lies in the optical analysis of a tandem design using III-V semiconductor nanowire arrays in combination with perovskites, highlighting their potential for tandem applications. The space offered by the compact footprint of NW arrays is used in an interpenetrated tandem structure. We systematically optimize the bottom cell, addressing reflectivity and parasitic absorption, and extend to a full tandem structure, considering experimentally feasible thicknesses. Simulation of a three-terminal structure highlights a potential increase in efficiency, decoupling the operating points of the subcells. The two-terminal analysis underscores the benefits of nanowires in reducing reflection and achieving a higher matched current between the top and the bottom cells. This research provides significant insights into NW tandem solar cell optics, enhancing our understanding of their potential to improve photovoltaic performance.

Transparent electrodes with high conductivity and mechanical robustness are essential for flexible opto‐electronic applications. Indium tin oxide (ITO) single layers have long been considered as unsuitable for flexible applications due to their brittleness. Here, it is shown that their mechanical stability can be substantially enhanced by texturing the flexible substrate. First, the opto‐electronic performance of single ITO layers and ITO/Ag/ITO stacks on polyethylene terephthalate (PET) foils is evaluated numerically by means of Haacke's figure‐of‐merit. Single ITO layers are found to be the electrode of choice for applications with a near‐infrared response due to their superior transparency. Following this, the sheet resistance of ITO layers is experimentally investigated on textured PET upon deformation parallel and perpendicular to a 1D texture grating. An “accordion‐like” deformation perpendicular to the grating and high texture aspect ratios are shown to avoid crack formation and loss of conductivity in the ITO. Simulations prove the considerably reduced occurrence of mechanical stress in this case. It is further experimentally demonstrated that texturing foils increase transmittance and haze. The enhanced mechanical robustness and optical performance by using textured foils make single ITO layers promising candidates for flexible opto‐electronic applications with a near‐infrared response, such as all‐perovskite tandem solar cells, thermal sensors, and photodetectors. Texturing of indium tin oxide (ITO) electrodes on plastic substrates is found to enable both, a remarkable transparency up to the near‐infrared region, and a massively enhanced mechanical stability upon bending. Therefore, textured ITO electrodes are an appealing option for flexible opto‐electronic applications with near‐infrared response, such as all‐perovskite tandem solar cells, thermal sensors, and medical imaging.

Background: Quantitative PCR (qPCR) is a valuable technique for accurately and reliably profiling and quantifying gene expression. Typically, samples obtained from the organism of study have to be processed via several preparative steps before qPCR. Method: We estimated the errors of sample withdrawal and extraction, reverse transcription (RT), and qPCR that are introduced into measurements of mRNA concentrations. We performed hierarchically arranged experiments with 3 animals, 3 samples, 3 RT reactions, and 3 qPCRs and quantified the expression of several genes in solid tissue, blood, cell culture, and single cells. Results: A nested ANOVA design was used to model the experiments, and relative and absolute errors were calculated with this model for each processing level in the hierarchical design. We found that intersubject differences became easily confounded by sample heterogeneity for single cells and solid tissue. In cell cultures and blood, the noise from the RT and qPCR steps contributed substantially to the overall error because the sampling noise was less pronounced. Conclusions: We recommend the use of sample replicates preferentially to any other replicates when working with solid tissue, cell cultures, and single cells, and we recommend the use of RT replicates when working with blood. We show how an optimal sampling plan can be calculated for a limited budget. .

The prevalence of substance abuse is high during adolescence, and several studies have linked the use of alcohol and cannabis in adolescence to different cognitive impairments. To investigate whether specific cognitive deficits can be observed in adolescents with substance use disorder (SUD), we compared the cognitive profiles of inpatient adolescents diagnosed with SUD to a control group matched for sex, age and educational status. The inpatient adolescents received diagnoses of cannabis use disorder, alcohol use disorder or both. We compared the WISC-V profiles of 22 inpatients (45.5% female, Mage: 14.5; SD: 0.8) and the WAIS-IV profiles of 27 inpatients (44.4% female, Mage: 17.1; SD: 0.9) to 49 matched control participants with no diagnosed SUD. At the time of testing, participants were hospitalized for treatment of their SUD and were abstinent for a period of at least 6 weeks. To gain greater power, we jointly analyzed the Verbal Comprehension Index, Working Memory Index, Processing Speed Index and Full Scale IQ as assessed by WISC-V and WAIS-IV. The clinical group performed significantly worse than the control group on all the above indices. When only the group of inpatients was observed, in a model with the factors sex, educational status, presence of a comorbid diagnosis of depression and the number of comorbid diagnoses, only the factor educational status was significantly associated with the Full Scale IQ, whereas the factors sex and a comorbid diagnosis of depression in this group were associated with the Processing Speed Index. The results show that adolescents diagnosed with SUD (cannabis and/or alcohol) display broad cognitive impairments after 6 weeks of abstinence. Future research is required to further explore the role of comorbid diagnoses.

Measurement invariance of the Wechsler Intelligence Scale for Children, Fifth Edition (WISC-V) 10-primary subtest battery was analyzed across a group of children and adolescents with ADHD (n = 91) and a control group (n = 91) matched by sex, age, migration background, and parental education or type of school. First, confirmatory factor analyses (CFAs) were performed to establish the model fit for the WISC-V second-order five-factor model in each group. A sufficiently good fit of the model was found for the data in both groups. Subsequently, multigroup confirmatory factor analyses (MGCFAs) were conducted to test for measurement invariance across the ADHD and control group. Results of these analyses indicated configural and metric invariance but did not support full scalar invariance. However, after relaxing equality constraints on the Vocabulary (VC), Digit Span (DS), Coding (CD), Symbol Search (SS), and Picture Span (PS) subtest intercepts as well as on the intercepts of the first-order factors Working Memory (WM) and Processing Speed (PS), partial scalar invariance could be obtained. Furthermore, model-based reliability coefficients indicated that the WISC-V provides a more precise measurement of general intelligence (e.g., represented by the Full-Scale IQ, FSIQ) than it does for cognitive subdomains (e.g., represented by the WISC-V indexes). Group comparisons revealed that the ADHD group scored significantly lower than the control group on four primary subtests, thus achieving significantly lower scores on the corresponding primary indexes and the FSIQ. Given that measurement invariance across the ADHD and the control group could not be fully confirmed for the German WISC-V, clinical interpretations based on the WISC-V primary indexes are limited and should only be made with great caution regarding the cognitive profiles of children and adolescents with ADHD.

Machine learning techniques can reveal hidden structures in large amounts of data and have the potential to replace analytical scientific methods. Electromagnetic simulations of photonic nanostructures often produce data in significant amounts, particularly when three-dimensional field distributions are calculated. An optimisation task, aiming at increased light yield from emitters interacting with photonic nanostructures, enforces systematic analysis of these data. Here we present a method that combines finite element simulations and clustering for the identification of photonic modes with large local field energies and specific spatial properties. For illustration, we use an experimental–numerical data set of quantum dot fluorescence on a photonic crystal surface. The application of Gaussian mixture model-based clustering allows to reduce the electric field distributions to a minimal subset of prototypes and the identification of characteristic spatial mode profiles. The presented clustering method potentially enables systematic optimisation of nanostructures for biosensing, bioimaging, and photon upconversion applications. Machine learning techniques are increasingly expanding their capabilities of making predictions on data across a variety of fields. The authors present a machine learning based approach capable of classifying the three-dimensional spatial electromagnetic field distributions of photonic crystals.