Commercial passivated emitter and rear cell (PERC) devices are critically hindered by a thus-far unidentified degradation mechanism called light- and elevated temperature-induced degradation (LeTID). ...In contrast to PERC devices, aluminum back-surface field (Al-BSF) devices are markedly more resistant to the same degradation mechanism. In this work, we employ device simulations to elucidate the differences between Al-BSF and PERC degradation behavior and thus accelerate the search for the root cause of LeTID. We find that a difference in defect activation under degradation conditions is a more likely explanation than enhanced sensitivity to bulk lifetime for PERC compared to Al-BSF devices. By employing device simulation for the two architectures under high illumination intensity, we identify a narrow parameter space for the LeTID defect precursor. When combined with experimental observations, this may yield important new information about LeTID defect formation.
The past decade's record of growth in the photovoltaics manufacturing industry indicates that global investment in manufacturing capacity for photovoltaic modules tends to increase in proportion to ...the size of the industry. The slope of this proportionality determines how fast the industry will grow in the future. Two key parameters determine this slope. One is the annual global investment in manufacturing capacity normalized to the manufacturing capacity for the previous year (capacity-normalized capital investment rate, CapIR, units /W). The other is how much capital investment is required for each watt of annual manufacturing capacity, normalized to the service life of the assets (capacity-normalized capital demand rate, CapDR, units /W). If these two parameters remain unchanged from the values they have held for the past few years, global manufacturing capacity will peak in the next few years and then decline. However, it only takes a small improvement in CapIR to ensure future growth in photovoltaics. Any accompanying improvement in CapDR will accelerate that growth.
By harvesting sub-bandgap photons, we have a path to overcome the Shockley-Queisser limit in photovoltaics (PVs). We investigate semiconductor nanocrystal (NC) sensitized upconversion via ...triplet-triplet annihilation (TTA) in organic semiconductors (OSCs). Since this process relies on optically inactive triplet states in the OSCs, we utilize PbS NCs to directly sensitize the triplet state via energy transfer. This is possible due to the strong spin-orbit coupling in PbS NCs, resulting in rapid spin-dephasing of the exciton. Current technology allows for upconversion of light with a photon energy above \sim 1.1 eV. However, while internal efficiencies are rapidly improving, the low external device efficiencies render them impractical for applications, as devices are based on a single monolayer of NCs. Our results show simply increasing the PbS NC film thickness does not show improvement in the efficiency due to poor exciton transport between PbS NCs. Here, we present a new strategy to increase the external upconversion efficiency by utilizing thin tinbased halide perovskites as the absorbing layer. Resonant energy transfer from the perovskite to the PbS NCs allows for subsequent sensitization of the triplet state in rubrene.
Combining high-throughput experiments with machine learning allows quick optimization of parameter spaces towards achieving target properties. In this study, we demonstrate that machine learning, ...combined with multi-labeled datasets, can additionally be used for scientific understanding and hypothesis testing. We introduce an automated flow system with high-throughput drop-casting for thin film preparation, followed by fast characterization of optical and electrical properties, with the capability to complete one cycle of learning of fully labeled ~160 samples in a single day. We combine regio-regular poly-3-hexylthiophene with various carbon nanotubes to achieve electrical conductivities as high as 1200 S/cm. Interestingly, a non-intuitive local optimum emerges when 10% of double-walled carbon nanotubes are added with long single wall carbon nanotubes, where the conductivity is seen to be as high as 700 S/cm, which we subsequently explain with high fidelity optical characterization. Employing dataset resampling strategies and graph-based regressions allows us to account for experimental cost and uncertainty estimation of correlated multi-outputs, and supports the proving of the hypothesis linking charge delocalization to electrical conductivity. We therefore present a robust machine-learning driven high-throughput experimental scheme that can be applied to optimize and understand properties of composites, or hybrid organic-inorganic materials.
As the photovoltaic community accelerates the development of new absorber
candidate materials towards high-performing PV devices, it is essential to
follow best practices and leverage deeper ...characterization tools. We have
identified temperature- and illumination-dependent current density-voltage
$J$($V$,$T$,$i$) and electron-beam induced current (EBIC) measurements as two
powerful PV device characterization techniques to evaluate the potential of
novel absorber candidate materials. Herein, we focus on the experimental
methods and best practices for applying $J$($V$,$T$,$i$) and EBIC, addressing
particular challenges in sample preparation and mounting. We demonstrate these
on the example of tin monosulfide, a promising PV absorber candidate material
that shares characteristics of many novel thin-film PV absorbers: mechanically
soft, polycrystalline, and used in heterojunction thin-film PV devices.
The emergence of methyl-ammonium lead halide (MAPbX3) perovskites motivates the identification of unique properties giving rise to exceptional bulk transport properties, and identifying future ...materials with similar properties. Here, we propose that this "defect tolerance" emerges from fundamental electronic structure properties, including the orbital character of the conduction and valence band extrema, the effective masses, and the static dielectric constant. We use MaterialsProject.org searches and detailed electronic-structure calculations to demonstrate these properties in other materials than MAPbX3. This framework of materials discovery may be applied more broadly, to accelerate discovery of new semiconductors based on emerging understanding of recent successes.
We present a framework (TIT-4-TAT) that enables the study of manufacturing strategies by coupling a technoeconomic model with tariff and transportation algorithms to optimize supply chain layouts for ...PV manufacturing. We use this framework in a scenario where Mexico, China, USA, and Brazil interact under two tariffs scenarios. The optimal manufacturing locations due to tariff levels variations are highlighted through this approach.
Inverse design is an outstanding challenge in disordered systems with multiple length scales such as polymers, particularly when designing polymers with desired phase behavior. We demonstrate ...high-accuracy tuning of poly(2-oxazoline) cloud point via machine learning. With a design space of four repeating units and a range of molecular masses, we achieve an accuracy of 4 {\deg}C root mean squared error (RMSE) in a temperature range of 24-90 {\deg}C, employing gradient boosting with decision trees. The RMSE is >3x better than linear and polynomial regression. We perform inverse design via particle-swarm optimization, predicting and synthesizing 17 polymers with constrained design at 4 target cloud points from 37 to 80 {\deg}C. Our approach challenges the status quo in polymer design with a machine learning algorithm, that is capable of fast and systematic discovery of new polymers.
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