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An artificial intrinsic RGB photoreceptor matrix can replicate the neuromorphic panchromatic vision capability of the eye.

2D/3D bilayer perovskite synthesized using sequential deposition methods has shown effectiveness in enhancing the stability of perovskite solar devices. However, these approaches present several limitations such as uncontrolled chemical processes, disordered interfacial states, and microscale heterogeneities that can chemically, structurally, and electronically compromise the performance of solar modules. Here, this work demonstrates an emulsion‐based self‐assembly approach using natural lipid biomolecules in a nonionic solution system to form a 0D/3D bilayer structure. The new capping layer is composed of 0D‐entity nanoparticles of perovskite encapsulated by a hydrophobic lipid membrane, analogous to a cell structure, formed through a molecular self‐assembly process. This 0D layer provides a strong water repellent characteristics, optimum interface microstructure, and excellent homogeneity that drives significant enhancement in stability. Solar modules with a large active area of 70 cm²fabricated using films comprising of 0D/3D bilayer structure are found to show consistent efficiency of >19% for 2800 h of continuous illumination in the air (60% relative humidity). This emulsion‐based self‐assembly approach is expected to have a transformative impact on the design and development of stable perovskite‐based devices.

Relaxor ferroelectrics (RFEs) are being actively investigated for energy‐storage applications due to their large electric‐field‐induced polarization with slim hysteresis and fast energy charging–discharging capability. Here, a novel nanograin engineering approach based upon high kinetic energy deposition is reported, for mechanically inducing the RFE behavior in a normal ferroelectric Pb(Zr_0.52Ti_0.48)O₃(PZT), which results in simultaneous enhancement in the dielectric breakdown strength (E_DBS) and polarization. Mechanically transformed relaxor thick films with 4 µm thickness exhibit an exceptionalE_DBSof 540 MV m⁻¹and reduced hysteresis with large unsaturated polarization (103.6 µC cm⁻²), resulting in a record high energy‐storage density of 124.1 J cm⁻³and a power density of 64.5 MW cm⁻³. This fundamental advancement is correlated with the generalized nanostructure design that comprises nanocrystalline phases embedded within the amorphous matrix. Microstructure‐tailored ferroelectric behavior overcomes the limitations imposed by traditional compositional design methods and provides a feasible pathway for realization of high‐performance energy‐storage materials.

Deep learning has been successful in various domains including image recognition, speech recognition and natural language processing. However, the research on its application in graph mining is still in an early stage. Here we present Model R, a neural network model created to provide a deep learning approach to link weight prediction problem. This model extracts knowledge of nodes from known links’ weights and uses this knowledge to predict unknown links’ weights. We demonstrate the power of Model R through experiments and compare it with stochastic block model and its derivatives. Model R shows that deep learning can be successfully applied to link weight prediction and it outperforms stochastic block model and its derivatives by up to 73% in terms of prediction accuracy. We anticipate this new approach to provide effective solutions to more graph mining tasks.