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Hybrid materials combining the optoelectronic absorption and tunability of quantum dots (QDs) with the high surface area, chemical functionality, and porosity of metal-organic frameworks (MOFs) are emerging as systems with unique optoelectronic properties relevant to applications in catalysis, sensing, and energy conversion and storage. A key component of the electronic interaction between QDs and MOFs is the transfer of charge between the two materials. This review examines the mechanisms driving charge transfer at the QD/MOF interfaces and the effects that both physical and chemical composition have on this process. We provide an overview of the key experimental approaches, including spectroscopic and electrochemical techniques, which have been used for probing charge transfer dynamics in this hybrid system. Challenges in controlling interfacial structure, distinguishing between charge and energy transfer, and optimizing stability are also discussed. This review highlights recent work on the preparation and characterization of QD/MOF hybrid materials, as well as fundamental studies advancing the understanding of charge transfer processes that occur in these systems. 

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. 

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. 

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. 

This review highlights recent advancements in the zinc oxide electron transport layer for PbS colloidal quantum dot solar cells. 

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. 

We present a method for designing spectrally- selective optoelectronic films with a finite absorption bandwidth. We demonstrate the process by designing a film composed of lead sulfide colloidal quantum dots (PbS-CQDs). Designs incorporate the patterning of absorbing PbS-CQD films into photonic crystal- like slabs which couple incident light into leaky modes within the plane of the absorbing films, modulating the absorption spectrum. Computational times required to calculate optical spectra are drastically decreased by implementing the Fourier Modal Method. Furthermore, a supervised machine-learning-based inverse design methodology is presented which allows tailoring of the PbS-CQD film optical properties for use in a variety of photovoltaic applications, such as tandem cells in which spectral tailoring can enable current-matching flexibility. 

Colloidal quantum dots (CQDs) are promising materials for photovoltaic applications due to their solution processibility and size-dependent band gap tunability. The electron transport layer (ETL) is an important component of PbS CQD solar cells, and the quality of the zinc oxide nanoparticle (ZnO NP) ETL film significantly impacts both the power conversion efficiency (PCE) and fabrication yield of CQD solar cells. We report on multiple methods to improve the quality of ZnO NP ETL films and demonstrate increased PCE and device yield in standard CQD solar cells employing optimized ZnO NP films. We also discuss the application of these methods in an inverted CQD solar cell architecture.