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Nanocluster-based photoresists enable 3D printing of polymer nanocomposites with enhanced mechanical strength and stability.

Optical and confocal microscopy is used to image the self-assembly of microscale colloidal particles. The density and size of self-assembled structures is typically quantified by hand, but this is extremely tedious. Here, we investigate whether machine learning can be used to improve the speed and accuracy of identification. This method is applied to confocal images of dense arrays of two-photon lithographed colloidal cones. RetinaNet, a deep learning implementation that uses a convolutional neural network, is used to identify self-assembled stacks of cones. Synthetic data is generated using Blender to supplement experimental training data for the machine learning model. This synthetic data captures key characteristics of confocal images, including slicing in the z-direction and Gaussian noise. We find that the best performance is achieved with a model trained on a mixture of synthetic data and experimental data. This model achieves a mean Average Precision (mAP) of ∼85%, and accurately measures the degree of assembly and distribution of self-assembled stack sizes for different cone diameters. Minor discrepancies between machine learning and hand labeled data is discussed in terms of the quality of synthetic data, and differences in cones of different sizes.

Polymeric particles with complex shapes are required for biomedical therapies, colloidal self‐assembly, and micro‐robotics. It has been challenging to synthesize particles beyond simple shapes (e.g., spheres, cubes) with high structural accuracy using existing methods. Here, a method for fabricating polymeric microparticles of complex 3D shapes is reported using two‐photon lithography, and dispersing the particles in an aqueous solution on a glass substrate. The fabrication of polyhedrons (e.g., tetrahedron, pyramid), polypods (e.g., tetrapod, hexapod), and other shapes of 5–10 µm in size is demonstrated. Confocal microscopy is used to track the motion of the sphere, tetrahedron, tetrapod, and screw‐shaped particles near the substrate, and determine their translational diffusion coefficients. HYDRO++ is used to simulate the motion of the particles far from the substrate. The influence of particle size and substrate effects on diffusion in the spherical particles is determined and finds that the non‐spherical particles have increased hindrance at the substrate compared to the spherical particles.

Fluorophores with high quantum yields, extended maximum emission wavelengths, and long photoluminescence (PL) lifetimes are still lacking for sensing and imaging applications in the second near‐infrared window (NIR‐II). In this work, a series of rod‐shaped icosahedral nanoclusters with bright NIR‐II PL, quantum yields up to≈8%, and a peak emission wavelength of 1520 nm are reported. It is found that the bright NIR‐II emission arises from a previously ignored state with near‐zero oscillator strength in the ground‐state geometry and the central Au atom in the nanoclusters suppresses the non‐radiative transitions and enhances the overall PL efficiency. In addition, a framework is developed to analyze and relate the underlying transitions for the absorptions and the NIR‐II emissions in the Au nanoclusters based on the experimentally defined absorption coefficient. Overall, this work not only shows good performance of the rod‐shaped icosahedral series of Au nanoclusters as NIR‐II fluorophores, but also unravels the fundamental electronic transitions and atomic‐level structure‐property relations for the enhancement of the NIR‐II PL in gold nanoclusters. The framework developed here also provides a simple method to analyze the underlying electronic transitions in metal nanoclusters.

Despite extensive studies on size effects in ferroelectrics, how structures and properties evolve in antiferroelectrics with reduced dimensions still remains elusive. Given the enormous potential of utilizing antiferroelectrics for high‐energy‐density storage applications, understanding their size effects will provide key information for optimizing device performances at small scales. Here, the fundamental intrinsic size dependence of antiferroelectricity in lead‐free NaNbO₃membranes is investigated. Via a wide range of experimental and theoretical approaches, an intriguing antiferroelectric‐to‐ferroelectric transition upon reducing membrane thickness is probed. This size effect leads to a ferroelectric single‐phase below 40 nm, as well as a mixed‐phase state with ferroelectric and antiferroelectric orders coexisting above this critical thickness. Furthermore, it is shown that the antiferroelectric and ferroelectric orders are electrically switchable. First‐principle calculations further reveal that the observed transition is driven by the structural distortion arising from the membrane surface. This work provides direct experimental evidence for intrinsic size‐driven scaling in antiferroelectrics and demonstrates enormous potential of utilizing size effects to drive emergent properties in environmentally benign lead‐free oxides with the membrane platform.