Control of light emission in solids underpins modern photonics, quantum technologies, and radiation detection. Strong coupling between excitons and confined electromagnetic modes forms hybrid light–matter states known as polaritons, enabling new regimes of emission control. To date, such effects have been largely restricted to nanoscale or ultrathin architectures and mostly reported under optical or electrical excitations, limiting their relevance for bulk scintillator applications. Here, macroscopic exciton–plasmon strong coupling is demonstrated in bulk nanocomposite scintillators based on lead-halide perovskite nanoplatelets coupled with silver nanocubes. Precise resonance alignment between excitonic and plasmonic modes is achieved through nanocube size engineering and temperature tuning, resulting in pronounced Rabi splitting and clear mode anticrossing. The extracted coupling strength exceeds both excitonic and plasmonic dissipation rates, confirming operation well within the strong-coupling regime. Angular-resolved photoluminescence measurements directly reveal polaritonic dispersion, providing unambiguous evidence of hybrid mode formation. Strong coupling is realized in a bulk scintillating composite, demonstrating that polaritonic hybridization can be implemented directly in materials designed for ionizing radiation detection. These results establish a scalable route to polaritonic scintillators in which light yield and temporal response can be engineered through controlled light–matter hybridization, opening opportunities for next-generation radiation detectors and medical imaging technologies.
Gypsum precipitation is a common process observed in diverse natural settings, offering valuable insights into (paleo)environmental conditions. It also holds significant industrial value, notably in wallboard production. However, its precipitation also poses severe challenges, for example, during water desalination. Despite numerous experimental and computational studies on gypsum since the late 19th century, the growth behavior and crystal habit remain poorly understood. In this work, we studied the nanoscale growth mechanisms controlling the formation of gypsum crystals from supersaturated solutions. We developed a method for obtaining oriented samples of natural idiomorphic gypsum crystals, enabling atomic force microscopy (AFM) imaging and microtopography characterization of all facets. Using this novel approach we (re-)examined the extensively studied {010} form, and, for the first time, the {120}, {-111}, and {011} forms. Remarkably, each form is dominated by a specific growth behavior, including cluster-mediated growth for {010}, stepped face growth for {120}, 2D nucleation and spiral growth for the {-111} form, and rough growth for {011}. These experimental findings differ from theoretical predictions, where all four forms are considered flat faces (F faces). We further demonstrate that the interplay between growth mechanisms of different forms, and the control that external factors exert on these, are the two key factors that dictate the crystal habit. Overall, this study establishes a framework for linking gypsum crystal morphology to the prevailing environmental conditions during growth. This understanding is critical not only for comprehending gypsum deposit formation in the geological record but also for the processing of advanced geomaterials and more effective anti-scaling strategies.
Despite the huge development of machine learning (ML) models to predict molecular properties, ML predictors for the molecular first hyperpolarizability tensors (β) are scarce, and solvation effects are rarely taken into account in the model. In this work, we develop and compare three ML approaches to predict the β tensor of water molecules embedded in an explicit liquid water environment: (1) Convolutional Neural Networks applied to electric field maps (EM-CNNs), (2) Message Passing Graph Neural Networks (MP-GNNs), and (3) Symmetry-Adapted Gaussian Process Regression (SA-GPR). Using a dataset of 150,000 β values computed at the quantum chemistry level for the non-resonant Second Harmonic Generation (SHG) process, we optimize each model’s hyperparameters and evaluate their precision and usability. All three approaches can accurately predict the β tensors for water molecules in a liquid environment. SA-GPR provides an efficient solution for relatively small training datasets, whereas the Neural Network architectures achieve near-reference accuracy (R2 > 0.997) for the largest datasets. Among them, MP-GNNs provide the highest accuracy while requiring fewer trainable parameters than the EM-CNN architecture. We further apply the best-performing model (EquiNNx ) to study its usability to predict Second Harmonic Scattering (SHS) intensities of bulk liquid water. The ML models can successfully capture the molecular first hyperpolarizability fluctuations and correlations essential for interpreting SHS of aqueous solutions. These approaches are generalizable to other embedded molecules, and other embedding solvents.
The experimentally retrieved value of the optical extinction cross section per unit length, sigma L,ext NT, of individual MoS2 multiwall nanotubes (NTs) is here reported over the 440-940 nm wavelength range for light polarization both parallel and perpendicular to the nanotube longitudinal axis. The impact of nanotube diameter and environment on sigma L,ext NT is addressed for individual nanotubes with diameters of 120 and 220 nm in suspended, sapphire-supported, and PMMA-supported configurations. Measuring individual nanotubes is of utmost importance given the wide nanotube size dispersion intrinsic to the synthesis process. The findings are interpreted in conjunction with finite element method simulations, informed by morphological input parameters from electron microscopy, offering insight into the respective contributions of absorption and scattering cross sections per unit length to overall sigma L,ext NT. These quantitative results are of relevance in view of optoelectronics applications involving MoS2 nanotubes while providing benchmark values for theoretical investigations on their nano-optical response.
