The low toxicity and a near-ideal choice of bandgap make tin perovskite an attractive alternative to lead perovskite in low cost solar cells. However, the development of Sn perovskite solar cells has been impeded by their extremely poor stability when exposed to oxygen. We report low-dimensional Sn perovskites that exhibit markedly enhanced air stability in comparison with their 3D counterparts. The reduced degradation under air exposure is attributed to the improved thermodynamic stability after dimensional reduction, the encapsulating organic ligands, and the compact perovskite film preventing oxygen ingress. We then explore these highly oriented low-dimensional Sn perovskite films in solar cells. The perpendicular growth of the perovskite domains between electrodes allows efficient charge carrier transport, leading to power conversion efficiencies of 5.94% without the requirement of further device structure engineering. We tracked the performance of unencapsulated devices over 100 h and found no appreciable decay in efficiency. These findings raise the prospects of pure Sn perovskites for solar cells application.

Development of tin halide perovskites is limited by the extremely poor stability and high background carrier density. Here, based on a pseudohalogen ''catalyst,'' we fabricated a Sn-based hierarchy structure perovskite in a one-step process, comprising highly parallel-orientation 2D PEA 2 SnI 4 on the surface of 3D FASnI 3 . The hierarchy structure delivers significantly enhanced stability and oxidation resistance in air atmosphere. We then explored hierarchy structure perovskite films in planar structure solar cells and achieved a PCE up to 9.41%. HIGHLIGHTS 2D-quasi-2D-3D hierarchy structure perovskite is fabricated for the first time Removable pseudohalogen acts as a regulator to manipulate tin perovskite structureThe hierarchy structure effectively resists oxidation and increases carrier mobilityThe hierarchy structure tin perovskite solar cells achieve a record PCE of 9.41% Wang et al., Joule SUMMARYThe power conversion efficiency (PCE) of tin perovskite solar cells is impeded by the extremely poor resistance to oxidation and high density of intrinsic Sn vacancies. Herein, we grow a 2D-quasi-2D-3D Sn perovskite film using removable pseudohalogen NH 4 SCN as a structure regulator. This hierarchy structure remarkably enhances air stability resulting from the parallel growth of 2D PEA 2 SnI 4 as the surface layer. We then explore the hierarchy structure perovskite films in planar structural solar cells, which generate a PCE up to 9.41%. The device retains 90% of its initial performance for almost 600 hr. Our results suggest that adding removable NH 4 SCN in a perovskite precursor can significantly improve the stability and photovoltaic performance of Sn perovskite. This finding provides a powerful strategy to manipulate the structure of low-dimensional perovskite in order to enhance the performance of perovskite solar cells.

Organic-inorganic hybrid perovskite solar cells have been developing rapidly in the past several years, and their power conversion efficiency has reached over 20%, nearing that of polycrystalline silicon solar cells. Because the diffusion length of the hole in perovskites is longer than that of the electron, the performance of the device can be improved by using an electron transporting layer, e.g., TiO2, ZnO and TiO2/Al2O3. Nano-structured electron transporting materials facilitate not only electron collection but also morphology control of the perovskites. The properties, morphology and preparation methods of perovskites are reviewed in the present article. A comprehensive understanding of the relationship between the structure and property will benefit the precise control of the electron transporting process and thus further improve the performance of perovskite solar cells.

Neural networks based on memristive devices [1][2][3] have shown potential in substantially improving throughput and energy efficiency for machine learning [4] and artificial intelligence [5], especially in edge applications. [6][7][8][9][10][11][12][13][14][15][16][17][18][19] Because training a neural network model from scratch is very costly, it is impractical to do it individually on billions of memristive neural networks distributed at the edge. A practical approach would be to download the synaptic weights obtained from the cloud training and program them directly into memristors for the commercialization of edge applications (Figure 1a). Some posttuning in memristor conductance to adapt local situations may follow afterward or during applications. Therefore, a critical requirement on memristors for neural network applications is a high-precision programming ability to guarantee uniform and accurate performance across a massive number of memristive networks. [20][21][22][23][24][25][26] That translates into the requirement of many distinguishable conductance levels on each memristive device, not just lab-made devices but more importantly, devices fabricated in foundries. High precision memristors also benefit other neural network applications, such as training and scientific computing. [23,27] Here we report over 2048 conductance levels, the largest number among all types of memories ever reported, achieved with memristors in fully integrated chips with 256 ´ 256 memristor arrays monolithically integrated on CMOS circuits in a standard foundry. We have unearthed the underlying physics that previously limited the number of achievable conductance levels in memristors and developed electrical operation protocols to circumvent such limitations. These results reveal insights into the fundamental understanding of the microscopic picture of memristive switching and provide approaches to enable high-precision memristors for various applications.Memristive switching devices are known for their relatively large dynamical range of conductance, which can potentially lead to a large number of discrete conductance levels. However, the highest number reported to date has been no more than two hundred. [20]