The spreading behavior of particles has a significant impact on the processing quality of additive manufacturing. Compared with spherical metal material, polymer particles are usually non-spherical in shape. However, the effects of particle shape and underlying mechanisms remain unclear. Here, the spreading process of particles with reconstructed shapes (non-spherical particles decomposed into several spherical shapes by stereo-lithography models) are simulated by integrating spherical particles with the discrete element method. The results show that more cavities form in the spreading beds of particles with reconstructed shapes than those of spheres with blade spreading. Correspondingly, particles with reconstructed shapes have lower packing densities, leading to more uniform packing patterns. Slow propagation speeds of velocity and angular velocity lead to “right-upwards” turning boundaries for particles with reconstructed shapes and “right-downwards” turning boundaries for spherical particles. Moreover, as the blade velocity increases, the packing density decreases. Our calculation results verify each other and are in good agreement with the experiment, providing more details of the behavior of non-spherical particles before additive manufacturing. The comprehensive comparison between polymer non-spherical particles and spherical particles helps develop a reasonable map for the appropriate choice of operating parameters in real processes.

Organic optical limiters are vital for protecting human eyes and sensitive optics against laser radiation, offering exceptional optical properties, and ultrafast responses. However, their practical applications are hindered by aggregation-caused quenching and photodegradation in the solid state. Here, we proposed an ingenious all-solid, passive optical limiter via high-elastic-state thermo-compression, integrating indium phthalocyanine anchored to functional moieties within polymer microspheres. The key innovation lay in the coordination-bond anchoring strategy, which effectively suppressed indium phthalocyanine aggregation and facilitated the intersystem crossing. The resulting device demonstrated exceptional limiting performance, with a giant nonlinear absorption coefficient (4.80 × 10⁻⁵ m/W) and an ultralow optical limiting threshold (< 0.013 J/cm²) at 532 nm, originating from long-lived triplet carrier accumulation. Moreover, the device exhibited excellent mechanical robustness and practical protection capability, as applied in smartphone camera. This work provided a viable strategy toward high-performance, practical organic solid optical limiter for next-generation laser protection applications.

Self-assembled nanosphere structures are widely used as bottom-up platforms for ordered micro- and nanostructures, with applications in photonic crystals, sensing platforms, functional coatings, drug delivery, and nanosphere lithography. Their performance and reproducibility depend on structural order, packing density, interparticle spacing, and defect density and distribution. Thus, reliable imaging and quantitative defect detection are needed for quality evaluation and process optimization. This review provides an overview of defect characteristics, imaging systems, and defect detection algorithms for self-assembled nanosphere structures. It first introduces representative zero-, one-, two-, and three-dimensional assemblies, followed by a summary of common defects, including vacancies, interstitial particles, dislocations, grain boundaries, stacking faults, voids, and cracks. Optical microscopy, electron microscopy, atomic force microscopy, scanning near-field optical microscopy, and correlative techniques are compared in terms of resolution, field of view, temporal resolution, contrast mechanism, in situ capability, and compatibility with feedback control. Algorithmic approaches are also reviewed, encompassing classical image processing, machine learning, and deep learning, along with their applications in segmentation, localization, classification, and high-throughput analysis. Overall, reliable defect inspection requires integrated workflows. These workflows should combine appropriate imaging systems, image quality control, transferable algorithms, standardized datasets, and closed-loop feedback.