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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. 

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

Lead Sulfide (PbS) colloidal quantum dots (CQDs) are promising materials for flexible and wearable photovoltaic devices and technologies due to their low cost, solution processibility and bandgap tunability with quantum dot size. However, PbS CQD solar cells have limitations on performance efficiency due to charge transport losses in the CQD layers and hole transport layer (HTL). This study pursues two promising techniques in parallel to address these challenges. Solution-phase annealing of the absorbing PbS-PbX2 (X = Br, I) layer can reduce charge transport losses by removing oleic acid and parasitic hydroxyl ligands. Additionally, optoelectronic simulations are used to show that HTL performance can be improved by the addition of a 2D transition metal dichalcogenide (TMD) layer to the PbS CQD-based HTL. We use solution-phase exfoliation to produce and incorporate 2D WSe2 nanoflakes into the HTL. We report a power conversion efficiency (PCE) increase of up to 3.4% for the solution-phase-annealed devices and up to 1% for the 2D WSe2 HTL augmented devices. A combination of these two techniques should result in high-performing PbS CQD solar cells, paving the way for further advancements in flexible photovoltaics. 

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. 

Achieving high electrical conductivity and thermoelectric power factor simultaneously for n-type organic thermoelectrics is still challenging. By constructing two new acceptor-acceptor n-type conjugated polymers with different backbones and introducing the 3,4,5-trimethoxyphenyl group to form the new n-type dopant 1,3-dimethyl-2-(3,4,5-trimethoxyphenyl)-2,3-dihydro-1H-benzo[d]imidazole (TP-DMBI), high electrical conductivity of 11 S cm-1 and power factor of 32 μW m-1 K-2 are achieved. Calculations using Density Functional Theory show that TP-DMBI presents a higher singly occupied molecular orbital (SOMO) energy level of -1.94 eV than that of the common dopant 4-(1, 3-dimethyl-2, 3-dihydro-1H-benzoimidazol-2-yl) phenyl) dimethylamine (N-DMBI) (-2.36 eV), which can result in a larger offset between the SOMO of dopant and lowest unoccupied molecular orbital (LUMO) of n-type polymers, though that effect may not be dominant in the present work. The doped polymer films exhibit higher Seebeck coefficient and power factor than films using N-DMBI at the same doping levels or similar electrical conductivity levels. Moreover, TP-DMBI doped polymer films offer much higher electron mobility of up to 0.53 cm2 V-1 s-1 than films with N-DMBI doping, demonstrating the potential of TP-DMBI, and 3,4,5-trialkoxy DMBIs more broadly, for high performance n-type organic thermoelectrics. 

A novel n-type copolymer dopant polystyrene-polyvinyl hexylpyridinium fluoride (PSpF) with fluoride anion is designed and synthesized by reversible addition-fragmentation chain transfer (RAFT) polymerization. To our knowledge, it is the first polymeric fluoride dopant. Electrical conductivity of 4.2 S cm-1 and high power factor of 67 μW m-1 K-2 are 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-1 at 88 ℃ and outstanding thermal stability were recorded. Further, organic transistors of PSpF-doped thin films exhibit high electron mobility and Hall mobility of 0.86 and 1.70 cm2 V-1 s-1, respectively. The results suggest that polystyrene-polyvinyl pyridinium salt copolymers with fluoride anion 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 Seebeck coefficient, high power factor, thermal stability and broad processing window. 

The inverse design of photovoltaic 2D photonic crystals using machine learning will be presented. The technique bypasses calculation of photonic bandstructure in favor of directly computing designer-friendly properties such as spectral transmission.