Chiral chromophores and their ordered
assemblies are intriguing
for yielding circularly polarized luminescence (CPL) and exploring
intrinsic structure–light emission relationships. With the
extensively studied chiral organic molecules and inorganic nanoparticle
assemblies for the amplified CPL, the assemblies of copper halide
hybrid clusters have attracted intensive attention due to their potential
efficient CPL. Here, we report robust chiral phosphine–copper
iodide hybrid clusters and their layered assemblies in crystalline
states for amplified CPL. We reveal that the intermolecular interactions
endow the clusters with the capability of assembling into chiral crystalline
CPL materials, including hexagonal platelet-shaped microcrystals (g
lum ≈ 9.5 × 10–3) and highly oriented crystalline films (g
lum ≈ 5 × 10–3). Owing to the high crystalline
feature of the thin film, we demonstrate an electroluminescent device
with bright electroluminescence (1200 cd m–2).
Ultraviolet (UV) absorption spectra are commonly used for characterizing the global structure of proteins. However, the theoretical interpretation of UV spectra is hindered by the large number of required expensive ab initio calculations of excited states spanning a huge conformation space. We present a machine-learning (ML) protocol for far-UV (FUV) spectra of proteins, which can predict FUV spectra of proteins with comparable accuracy to density functional theory (DFT) calculations but with 3−4 orders of magnitude reduced computational cost. It further shows excellent predictive power and transferability that can be used to probe structural mutations and protein folding pathways.
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