Quasi-Random Multimetallic Nanoparticle Arrays.

This paper describes a nanofabrication procedure that can generate multiscale substrates with quasi-random microregions of nanoparticle arrays having different periodicities and metals. We combine cycles of large-area nanoparticle array fabrication with solvent-assisted wrinkle lithography to mask and etch quasi-random areas of prefabricated nanoparticles to control the fill factors of the arrays. The approach is highly flexible, and parameters, including nanoparticle size and material, array geometry, and fill factor, can be tailored independently. Multimetallic nanoparticle arrays can support surface lattice resonances at fill factors as low as 20% and can function as nanoscale cavities for lasing action with as few as 10% of the nanoparticles in an array. We demonstrated that multimetallic nanoparticle substrates that combine two or three arrays with different periodicities can exhibit lasing responses over visible and near-infrared wavelengths. Our work showcases the robust optical responses of multimetallic and periodic devices for broadband light manipulation.

Strong Coupling Between Plasmons and Molecular Excitons in Metal-Organic Frameworks.

This Letter describes strong coupling of densely packed molecular emitters in metal-organic frameworks (MOFs) and plasmonic nanoparticle (NP) lattices. Porphyrin-derived ligands with small transition dipole moments in an ordered MOF film were grown on Ag NP arrays. Angle-resolved optical measurements of the MOF-NP lattice system showed the formation of a polariton that is lower in energy and does not cross the uncoupled MOF Q1 band. Modeling predicted the upper polariton energy and a calculated Rabi splitting of 110 meV. The coupling strength was systematically controlled by detuning the plasmon energy by changing the refractive index of the solvents infiltrating the MOF pores. Through transient absorption spectroscopy, we found that the lower polariton decays quickly at shorter time scales (<500 ps) and slowly at longer times because of energy transfer from the upper polariton. This hybrid system demonstrates how MOFs can function as an accessible excitonic material for polariton chemistry.

Single-Nanoparticle Orientation Sensing by Deep Learning.

This paper describes a computational imaging platform to determine the orientation of anisotropic optical probes under differential interference contrast (DIC) microscopy. We established a deep-learning model based on data sets of DIC images collected from metal nanoparticle optical probes at different orientations. This model predicted the in-plane angle of gold nanorods with an error below 20°, the inherent limit of the DIC method. Using low-symmetry gold nanostars as optical probes, we demonstrated the detection of in-plane particle orientation in the full 0-360° range. We also showed that orientation predictions of the same particle were consistent even with variations in the imaging background. Finally, the deep-learning model was extended to enable simultaneous prediction of in-plane and out-of-plane rotation angles for a multibranched nanostar by concurrent analysis of DIC images measured at multiple wavelengths.

Concurrent design of quasi-random photonic nanostructures.

Nanostructured surfaces with quasi-random geometries can manipulate light over broadband wavelengths and wide ranges of angles. Optimization and realization of stochastic patterns have typically relied on serial, direct-write fabrication methods combined with real-space design. However, this approach is not suitable for customizable features or scalable nanomanufacturing. Moreover, trial-and-error processing cannot guarantee fabrication feasibility because processing-structure relations are not included in conventional designs. Here, we report wrinkle lithography integrated with concurrent design to produce quasi-random nanostructures in amorphous silicon at wafer scales that achieved over 160% light absorption enhancement from 800 to 1,200 nm. The quasi-periodicity of patterns, materials filling ratio, and feature depths could be independently controlled. We statistically represented the quasi-random patterns by Fourier spectral density functions (SDFs) that could bridge the processing-structure and structure-performance relations. Iterative search of the optimal structure via the SDF representation enabled concurrent design of nanostructures and processing.