We present a new approach to ubiquitous sensing for indoor applications, using high-efficiency and low-cost indoor perovksite photovoltaic cells as external power sources for backscatter sensors. We ...demonstrate wide-bandgap perovskite photovoltaic cells for indoor light energy harvesting with the 1.63eV and 1.84 eV devices demonstrate efficiencies of 21% and 18.5% respectively under indoor compact fluorescent lighting, with a champion open-circuit voltage of 0.95 V in a 1.84 eV cell under a light intensity of 0.16 mW/cm2. Subsequently, we demonstrate a wireless temperature sensor self-powered by a perovskite indoor light-harvesting module. We connect three perovskite photovoltaic cells in series to create a module that produces 14.5 uW output power under 0.16 mW/cm2 of compact fluorescent illumination with an efficiency of 13.2%. We use this module as an external power source for a battery-assisted RFID temperature sensor and demonstrate a read range by of 5.1 meters while maintaining very high frequency measurements every 1.24 seconds. Our combined indoor perovskite photovoltaic modules and backscatter radio-frequency sensors are further discussed as a route to ubiquitous sensing in buildings given their potential to be manufactured in an integrated manner at very low-cost, their lack of a need for battery replacement and the high frequency data collection possible.
Novel photovoltaics, such as perovskites and perovskite-inspired materials, have shown great promise due to high efficiency and potentially low manufacturing cost. So far, solar cell R&D has mostly ...focused on achieving record efficiencies, a process that often results in small batches, large variance, and limited understanding of the physical causes of underperformance. This approach is intensive in time and resources, and ignores many relevant factors for industrial production, particularly the need for high reproducibility and high manufacturing yield, and the accompanying need of physical insights. The record-efficiency paradigm is effective in early-stage R&D, but becomes unsuitable for industrial translation, requiring a repetition of the optimization procedure in the industrial setting. This mismatch between optimization objectives, combined with the complexity of physical root-cause analysis, contributes to decade-long timelines to transfer new technologies into the market. Based on recent machine learning and technoeconomic advances, our perspective articulates a data-driven optimization framework to bridge R&D and manufacturing optimization approaches. We extend the maximum-efficiency optimization paradigm by considering two additional dimensions: a technoeconomic figure of merit and scalable physical inference. Our framework naturally aligns different stages of technology development with shared optimization objectives, and accelerates the optimization process by providing physical insights.
Defects in semiconductors, although atomistic in scale and often scarce in concentration,frequently represent the performance-limiting factor in optoelectronic devices such as solar cells. However, ...due to this scale and scarcity, direct experimental characterization of defectsis technically challenging, timeconsuming, and expensive. Even so, the fact that defects can limit device performance suggests that device-level characterization should be able to lend insight into their properties. In this work, we use Bayesian inference to demonstrate a way to relate experimental device measurements with defect properties (as well as other materials properties affected by the presence of defects, such as minority-carrier lifetime). We apply this method to solve the "inverse problem" to a forward device model - namely, determining which input parameters to the model produce the measured electrical output. This approach has distinct advantages over direct characterization. First, a single set of measurements can beused to determine many parameters (the number of which, in principle, is limited only by the computingresources available), saving time and cost of facilities and equipment. Second, sincemeasurements are performed on materials and interfaces in their relevant device geometries (vs.separately prepared samples), the determined parameters are guaranteed to be physically relevant. We demonstrate application of this method to both tin monosulfide and silicon solar cellsand discuss potential for future application in a broader array of systems.
The air-conditioning (AC) sector offers an interesting synergy for photovoltaic (PV) electricity generation, because the demand for AC correlates with ideal times and locations for PV production. The ...AC sector is also a significant electricity consumer, currently comprising ~3 % of global electricity consumption and it is expected to grow rapidly in the future due to income and population growth in sunny countries as well as global warming. Here, we assess the potential of the residential AC sector to sustain and accelerate PV industry growth during the 21 st century. We show that the residential AC sector could sustain more installed PV generation capacity than the current global PV manufacturing capacity could produce. We highlight other possible synergistic electricity consumption sectors with significant growth expected in the future.
Lead halide-based perovskite thin films have attracted great attention due to the explosive increase in perovskite solar cell efficiencies. The same optoelectronic properties that make perovskites ...ideal absorber materials in solar cells are also beneficial in other light-harvesting applications and make them prime candidates as triplet sensitizers in upconversion via triplet-triplet annihilation in rubrene. In this contribution, we take advantage of long carrier lifetimes and carrier diffusion lengths in perovskite thin films, their high absorption cross sections throughout the visible spectrum, as well as the strong spin-orbit coupling owing to the abundance of heavy atoms to sensitize the upconverter rubrene. Employing bulk perovskite thin films as the absorber layer and spin-mixer in inorganic/organic heterojunction upconversion devices allows us to forego the additional tunneling barrier owing from the passivating ligands required for colloidal sensitizers. Our bilayer device exhibits an upconversion efficiency in excess of 3% under 785 nm illumination.
We present the latest developments in the characterization of thin-film solar cells based on the combination of elemental mapping from fluorescence measurements using synchrotron x-rays, with beam ...induced current from electron and x-ray beams. This is a powerful method to directly correlate compositional variations with charge collection efficiency. We compare different approaches for mapping solar cells both in cross-section and in plan view on CIGS and CdTe solar cells. Based on examples from our latest research, we discuss the experimental approaches and highlight the advantages and limitations of each technique. Finally, we present an outlook to experiments that will allow x-ray based characterization to enter new fields of research that were not accessible before.
Process optimization of photovoltaic devices is a time-intensive, trial and error endeavor, without full transparency of the underlying physics, and with user-imposed constraints that may or may not ...lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach that identifies the root cause(s) of underperformance with layer by-layer resolution and reveals alternative optimal process windows beyond global black-box optimization. Our Bayesian-network approach links process conditions to materials descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device performance parameters (e.g., cell efficiency), using a Bayesian inference framework with an autoencoder-based surrogate device-physics model that is 100x faster than numerical solvers. With the trained surrogate model, our approach is robust and reduces significantly the time consuming experimentalist intervention, even with small numbers of fabricated samples. To demonstrate our method, we perform layer-by-layer optimization of GaAs solar cells. In a single cycle of learning, we find an improved growth temperature for the GaAs solar cells without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above baseline and traditional black-box optimization methods.
Strain in a material induces shifts in vibrational frequencies, which is a probe of the nature of the vibrations and interatomic potentials, and can be used to map local stress/strain distributions ...via Raman microscopy. This method is standard for crystalline silicon devices, but due to lack of calibration relations, it has not been applied to amorphous materials such as hydrogenated amorphous silicon (a-Si:H), a widely studied material for thin-film photovoltaic and electronic devices. We calculated the Raman spectrum of a-Si:H \ab initio under different strains \(\epsilon\) and found peak shifts \(\Delta \omega = \left( -460 \pm 10\ \mathrm{cm}^{-1} \right) {\rm Tr}\ \epsilon\). This proportionality to the trace of the strain is the general form for isotropic amorphous vibrational modes, as we show by symmetry analysis and explicit computation. We also performed Raman measurements under strain and found a consistent coefficient of \(-510 \pm 120\ \mathrm{cm}^{-1}\). These results demonstrate that a reliable calibration for the Raman/strain relation can be achieved even for the broad peaks of an amorphous material, with similar accuracy and precision as for crystalline materials.
We use electronic transport and atom probe tomography to study ZnO:Al / SiO2 / Si Schottky junctions on lightly-doped n- and p-type Si. We vary the carrier concentration in the the ZnO:Al films by ...two orders of magnitude but the Schottky barrier height remains constant, consistent with Fermi level pinning seen in metal / Si junctions. Atom probe tomography shows that Al segregates to the interface, so that the ZnO:Al at the junction is likely to be metallic even when the bulk of the ZnO:Al film is semiconducting. We hypothesize that Fermi level pinning is connected to the insulator-metal transition in doped ZnO, and that controlling this transition may be key to un-pinning the Fermi level in oxide / Si Schottky junctions.
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