No abstract

Organic halide salt passivation is considered to be an essential strategy to reduce defects in state-of-the-art perovskite solar cells (PSCs). This strategy, however, suffers from the inevitable formation of in-plane favored two-dimensional (2D) perovskite layers with impaired charge transport, especially under thermal conditions, impeding photovoltaic performance and device scale-up. To overcome this limitation, we studied the energy barrier of 2D perovskite formation from ortho-, meta- and para-isomers of (phenylene)di(ethylammonium) iodide (PDEAI2) that were designed for tailored defect passivation. Treatment with the most sterically hindered ortho-isomer not only prevents the formation of surficial 2D perovskite film, even at elevated temperatures, but also maximizes the passivation effect on both shallow- and deep-level defects. The ensuing PSCs achieve an efficiency of 23.9% with long-term operational stability (over 1000 h). Importantly, a record efficiency of 21.4% for the perovskite module with an active area of 26 cm2 was achieved.

and are important in various technological fields such as energy, electronics, medicine, and many more. [1][2][3][4][5] However, as a consequence of industrial processes and man-made pollution, unwanted nanoparticle size distributions and concentrations [6] give rise to concerns with respect to human health and environmental pollution. While the nanoparticles' physicochemical properties (size, shape, surface chemistry, etc.) determine the quality of products, [7,8] such characteristics are also important in order to evaluate the biological impact of nanoparticles at a molecular, cellular, and systemic level for any risk assessment for environmental and human health. [9] Characterizing nanoparticles in a dynamic context and on a case-by-case basis, microscopic imaging techniques including those that use focused electron or ion beams in scanning electron microscopes (SEMs) or helium ion microscopes [10] (HIMs) to generate nanometer scale spatial resolution are frequently applied in the scientific community. Given the substantial information content of digital images, these techniques often benefit from, or require, automated high-throughput data analysis that enables the accurate identification of large numbers of particles in a robust way.Nanoparticles occur in various environments as a consequence of man-made processes, which raises concerns about their impact on the environment and human health. To allow for proper risk assessment, a precise and statistically relevant analysis of particle characteristics (such as size, shape, and composition) is required that would greatly benefit from automated image analysis procedures. While deep learning shows impressive results in object detection tasks, its applicability is limited by the amount of representative, experimentally collected and manually annotated training data. Here, an elegant, flexible, and versatile method to bypass this costly and tedious data acquisition process is presented. It shows that using a rendering software allows to generate realistic, synthetic training data to train a state-of-the art deep neural network. Using this approach, a segmentation accuracy can be derived that is comparable to man-made annotations for toxicologically relevant metal-oxide nanoparticle ensembles which were chosen as examples. The presented study paves the way toward the use of deep learning for automated, highthroughput particle detection in a variety of imaging techniques such as in microscopies and spectroscopies, for a wide range of applications, including the detection of micro-and nanoplastic particles in water and tissue samples.

Titanium diffusion profiles in natural quartz crystals have become an increasingly popular tool to reconstruct the time scales of various magmatic, metamorphic, and hydrothermal processes. However, the original calibration of Ti diffusion rates in quartz has recently been challenged, and diffusivities were found to be about three orders of magnitude lower. We performed annealing experiments on crystal-crystal diffusion couples consisting of Ti-free synthetic quartz seeds over which Ti-rich quartz (100–3000 μg/g Ti) was grown hydrothermally. The annealing experiments were performed at 1000–1600 °C and 0.1 MPa to 2.0 GPa, and they lasted for 3–84 days. The resulting diffusion profiles were mapped by cathodoluminescence (CL), transmission electron microscope–energy-dispersive X-ray spectroscopy (TEM-EDXS), and, for the first time, by helium ion microscope–secondary ion mass spectrometry (HIM-SIMS). Obtained diffusion coefficients range from values similar to the lower range in previous research to values up to two orders of magnitude lower. In addition, inversely zoned quartz and sanidine phenocrysts in a natural rhyolite were studied. Comparison of the diffusion profiles suggests that at ~735 °C, the Ti diffusivity in quartz is ~1.5 and 3.0 orders of magnitude lower than that of Ba and Sr, respectively, in sanidine. The combined evidence confirms that Ti diffusion in quartz is very slow, potentially even slower than proposed earlier. Consequently, previous time scales derived from Ti diffusion profiles in quartz are likely orders of magnitude too short, and further experiments are necessary to fully clarify the issue.

Remarkable progress in power conversion efficiency of perovskite solar cells (PSCs) has been achieved over the last decade, reaching 25.5%. However, transferring these accomplishments from individual small-size devices into large-area modules while preserving their commercial competitiveness compared to other thin-film solar cells remains a challenge. A major obstacle is to reduce the resistive losses and the number of intrinsic defects of electron transport layers (mesoporous TiO2, ETL) and to fabricate high-quality large-area perovskite films. Here, we report a facile solvothermal method to synthesize single-crystalline TiO2 rhombus-like nanoparticles with exposed {001} facets. Owing to their low lattice mismatch with the perovskite absorber, high electron mobility and lower density of defects, single-crystalline TiO2 nanoparticle-based small-size devices (0.09 cm2) achieve an efficiency of 24.05% and a fill factor of 84.7%. Importantly, these devices maintain about 90% of their initial performance after continuous operation for 1400 h. Combined with vacuum quenching-assisted techniques, we have fabricated large-area modules and obtained a certified efficiency of 22.72% with an active area of nearly 24 cm2. This represents the highest efficiency modules with the lowest efficiency loss between small-size devices and modules, enabling to reproducibly fabricate stable and efficient PSC modules.