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We demonstrate how aperiodicity and disorder can be used as quantifiable mechanisms for tuning the spectral response of plasmonic nanostructure arrays. We tune the extinction spectra of these arrays using deterministically aperiodic (quasicrystal), perturbed lattice (Bernoulli point process, frozen phonon disorder, long-range frozen phonon disorder), negatively correlated (Strauss point process), and positively correlated (Log Gaussian Cox point process) assemblies. We quantify this tuning by considering the local variance of the extinction spectra, demonstrating two orders of magnitude of tunability. Our structures have potential applications in plasmonic or waveguide-based optoelectronic devices such as photovoltaics and photosensing, where spectral tuning is critical to performance. 

Two-dimensional transition metal dichalcogenides are of growing interest for flexible optoelectronics and power applications, due to their tunable optical properties, lightweight nature, and mechanical pliability. However, their thin nature inherently limits their optical absorption and, therefore, efficiency. Here, we propose a few-layer WSe₂optoelectronic device that achieves near perfect absorption through a combination of optical effects. The WSe₂can be scalably grown below an Al₂O₃superstrate. Our device includes a corrugated back reflector, modeled as a plasmonic nanowire array. We investigate the entire range of widths of the corrugations in the back reflector, including the edge cases of a simple back mirror (width equal to period) and a Fabry-Perot cavity (zero width). We demonstrate the zero-mode enhancement arising from the back reflector, the weakly coupled enhancement arising from the Fabry-Perot cavity, and the strongly coupled enhancement arising from the localized surface plasmon resonance of the nanowires, explain the physical nature of the spectral peaks, and theoretically model the hybridization of these phenomena using a coupled oscillator model. Our champion device exhibits 82% peak absorptance in the WSe₂alone, 92% in the WSe₂plus nanowires, and 98% total absorptance. Thus, we achieve a near-perfect absorber in which most of the absorption is in the few-layer WSe₂, with a desirable device framework for integration with scalable growth of the WSe₂, thereby making our designs applicable to a range of practical optoelectronic devices. 

Metamaterials are complex structured mixed-material systems with tailored physical properties that have found applications in a variety of optical and electronic technologies. New methods for homogenizing the optical properties of metamaterials are of increasing importance, both to study their exotic properties and because the simulation of these complex structures is computationally expensive. We propose a method to extract a homogeneous refractive index and wave impedance for inhomogeneous materials. We examine effective medium models, where inhomogeneities are subwavelength, and equivalent models where features are larger. Homogenization is only physically justified in the former; however, it is still useful in the latter if only the reflection, transmission, and absorption are of interest. We introduce a resolution of the branching problem in the Nicolson-Ross-Weir method that involves starting from the branch of the complex logarithm beginning with the minimum absolute mean derivative and then enforcing continuity, and also determine an effective thickness. We demonstrate the proposed method on patterned PbS colloidal quantum dot films in the form of disks and birefringent gratings. We conclude that effective models are Kramers-Kronig compliant, whereas equivalent models may not be. This work illuminates the difference between the two types of models, allowing for better analysis and interpretation of the optical properties of complex metamaterials. 

Recent advances in machine learning (ML) have enabled predictive programs for photovoltaic characterization, optimization, and materials discovery. Despite these advances, the standard photovoltaic materials development workflow still involves manually performing multiple characterization techniques on every new device, requiring significant time and expenditures. One barrier to ML implementation is that most models reported to date are trained on computer simulated data, due to the difficulty in experimentally collecting the massive data sets needed for model training, limiting the ability to assess the limitations and validity of these methods, as well as to access new potential physical mechanisms absent in simulations. Herein, several neural networks trained on experimental data from PbS colloidal quantum dot thin‐film solar cells are introduced. These models predict multiple, complex materials properties, including carrier mobility, relative photoluminescence intensity, and electronic trap‐state density, from a single, simple measurement: illuminated current–voltage curves. The measurement system considers the spatial distribution of the materials parameters to gather and predict large amounts of data by treating an inhomogeneous device as a series of thousands of micro‐devices, a novel feature compared to existing solutions. This model can be extended to other materials and devices, accelerating development times for new optoelectronic technologies. 

Abstract The morphology, chemical composition, and electronic uniformity of thin‐film solution‐processed optoelectronics are believed to greatly affect device performance. Although scanning probe microscopies can address variations on the micrometer scale, the field of view is still limited to well under the typical device area, as well as the size of extrinsic defects introduced during fabrication. Herein, a micrometer‐resolution 2D characterization method with millimeter‐scale field of view is demonstrated, which simultaneously collects photoluminescence spectra, photocurrent transients, and photovoltage transients. This high‐resolution morphology mapping is used to quantify the distribution and strength of the local optoelectronic property variations in colloidal quantum dot solar cells due to film defects, physical damage, and contaminants across nearly the entire test device area, and the extent to which these variations account for overall performance losses. It is found that macroscopic defects have effects that are confined to their localized areas, rarely prove fatal for device performance, and are largely not responsible for device shunting. Moreover, quantitative analysis based on statistical partitioning methods of such data is used to show how defect identification can be automated while identifying variations in underlying properties such as mobilities and recombination strengths and the mechanisms by which they govern device behavior. 

Abstract A novel n‐type copolymer dopant polystyrene–poly(4‐vinyl‐N‐hexylpyridinium fluoride) (PSpF) with fluoride anions is designed and synthesized by reversible addition–fragmentation chain transfer (RAFT) polymerization. This is thought to be the first polymeric fluoride dopant. Electrical conductivity of 4.2 S cm^–1and high power factor of 67 µW m^–1K^–2are achieved for PSpF‐doped polymer films, with a corresponding decrease in thermal conductivity as the PSpF concentration is increased, giving the highest ZT of 0.1. An especially high electrical conductivity of 58 S cm^–1at 88 °C and outstanding thermal stability are recorded. Further, organic transistors of PSpF‐doped thin films exhibit high electron mobility and Hall mobility of 0.86 and 1.70 cm²V^–1s^–1, respectively. The results suggest that polystyrene–poly(vinylpyridinium) salt copolymers with fluoride anions are promising for high‐performance n‐type all‐polymer thermoelectrics. This work provides a new way to realize organic thermoelectrics with high conductivity relative to the Seebeck coefficient, high power factor, thermal stability, and broad processing window.