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Halide perovskites is a new class of semiconductors with exceptional optoelectronic properties. Among many advantages offered by halide perovskites, the bandgap energy can be tuned in a much broader range than what was possible in conventional semiconductors. This was commonly achieved in previous research by mixing different species of halides into solid solutions. The tuned bandgap using this method, however, often underwent an energy shift under optical or electrical stimuli due to halide segregation. In this work, we discovered an alternative approach to achieve super-bandgap electroluminescence from CsPbBr3. The peak energy of the light emission can be 0.7 eV higher than the reported bandgap energy. Evidence pointed to the radiative recombination at the perovskite-PEDOT:PSS interface being responsible for the unexpected blueshift of electroluminescence. We speculated that perovskite nanocrystals were formed therein and produced higher-energy photons due to quantum confinement. The results suggested an alternative strategy to manipulate and stabilize the color of electroluminescence and achieve particularly blue emission in perovskite-based LEDs.

Polariton condensation relies on the macroscopic occupation of the lowest-energy polariton state beyond a critical density. The mechanisms driving the occupation and depopulation of this state all rely on multi-particle scattering, whose dynamics determine the extent to which condensates can form spontaneously. To pinpoint many-body processes hindering polariton condensation in two-dimensional metal-halide semiconductors, we examine the exciton-polariton dynamics in a Fabry-P\'erot microcavity over timescales involving polariton ($\bm{\ll 1}$\,ps) and exciton scattering ($\bm{\gg 1}$\,ps). We find enhanced nonlinear exciton-exciton interactions in the microcavity versus the bare semiconductor and ultrafast polariton scattering depopulating the lowest-energy polariton state. We posit that the complex scattering landscape between the exciton reservoir and polaritons limits the formation of polariton condensates in these semiconductors, and we discuss the generality of our conclusions for highly polar materials in which the lattice mediates multi-particle correlations., Comment: 27 pages, 6 figures, plus supplementary material document. Submitted for publication

We explore the application of excitation correlation spectroscopy to detect nonlinear photophysical dynamics in two distinct semiconductor classes through time-integrated photoluminescence and photocurrent measurements. In this experiment, two variably delayed femtosecond pulses excite the semiconductor, and the time-integrated photoluminescence or photocurrent component arising from the nonlinear dynamics of the populations induced by each pulse is measured as a function of inter-pulse delay by phase-sensitive detection with a lock-in amplifier. We focus on two limiting materials systems with contrasting optical properties: a prototypical lead-halide perovskite (LHP) solar cell, in which primary photoexcitations are charge photocarriers, and a single-component organic-semiconductor diode, which features Frenkel excitons as primary photoexcitations. The photoexcitation dynamics perceived by the two detection schemes in these contrasting systems are distinct. Nonlinear-dynamic contributions in the photoluminescence detection scheme arise from contributions to radiative recombination in both materials systems, while photocurrent arises directly in the LHP but indirectly following exciton dissociation in the organic system. Consequently, the basic photophysics of the two systems are reflected differently when comparing measurements with the two detection schemes., Comment: Submitted for publication

Photovoltaic (PV) cells have the potential to serve as on-board power sources for low-power IoT devices. Here, we explore the use of perovskite solar cells to power Radio Frequency (RF) backscatter-based IoT devices with a few {\mu}W power demand. Perovskites are suitable for low-cost, high-performance, low-temperature processing, and flexible light energy harvesting that hold the possibility to significantly extend the range and lifetime of current backscatter techniques such as Radio Frequency Identification (RFID). For these reasons, perovskite solar cells are prominent candidates for future low-power wireless applications. We report on realizing a functional perovskite-powered wireless temperature sensor with 4 m communication range. We use a 10.1% efficient perovskite PV module generating an output voltage of 4.3 V with an active area of 1.06 cm2 under 1 sun illumination, with AM 1.5G spectrum, to power a commercial off-the-shelf RFID IC, requiring 10 - 45 {\mu}W of power. Having an on-board energy harvester provides extra-energy to boost the range of the sensor (5x) in addition to providing energy to carry out high-volume sensor measurements (hundreds of measurements per min). Our evaluation of the prototype suggests that perovskite photovoltaic cells are able to meet the energy needs to enable fully autonomous low-power RF backscatter applications of the future. We conclude with an outlook into a range of applications that we envision to leverage the synergies offered by combining perovskite photovoltaics and RFID.

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., Comment: 16 pages, 3 figures

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.

The same optical and electronic properties that make perovskite thin films ideal absorber materials in photovoltaic applications are also beneficial in photon upconversion devices. In this contribution, we investigate the rubrene-triplet sensitization by perovskite thin films based on methylammonium formamidinium lead triiodide (MAFA). To elucidate the role of trap states which affect the free carrier lifetimes, we fabricate MAFA perovskite thin films with three different thicknesses. By measuring the change in the photoluminescence properties under different excitation fluences, we find that the prevalent recombination mechanism shifts from monomolecular for thinner films to bimolecular recombination for thicker MAFA films, indicating a reduction in shallow trap-assisted recombination. The addition of rubrene shows a passivating effect on the MAFA surface, but adds an additional quenching pathway due to charge transfer to the triplet state of rubrene. We observe that the threshold for efficient triplet-triplet annihilation shifts to lower incident powers with increasing MAFA thickness, which suggests that the charge transfer to the triplet state competes with non-radiative trap filling. Hence, injection of free electrons and holes into the upconverting organic semiconductor can provide a new avenue for sensitization of rubrene, and may allow us to move away from the necessity of efficient excitonic singlet-to-triplet converters.

Accelerating the experimental cycle for new materials development is vital for addressing the grand energy challenges of the 21st century. We fabricate and characterize 75 unique halide perovskite-inspired solution-based thin-film materials within a two-month period, with 87% exhibiting band gaps between 1.2 eV and 2.4 eV that are of interest for energy-harvesting applications. This increased throughput is enabled by streamlining experimental workflows, developing a set of precursors amenable to high-throughput synthesis, and developing machine-learning assisted diagnosis. We utilize a deep neural network to classify compounds based on experimental X-ray diffraction data into 0D, 2D, and 3D structures more than 10 times faster than human analysis and with 90% accuracy. We validate our methods using lead-halide perovskites and extend the application to novel lead-free compositions. The wider synthesis window and faster cycle of learning enables three noteworthy scientific findings: (1) we realize four inorganic layered perovskites, A3B2Br9 (A = Cs, Rb; B = Bi, Sb) in thin-film form via one-step liquid deposition; (2) we report a multi-site lead-free alloy series that was not previously described in literature, Cs3(Bi1-xSbx)2(I1-xBrx)9; and (3) we reveal the effect on bandgap (reduction to <2 eV) and structure upon simultaneous alloying on the B-site and X-site of Cs3Bi2I9 with Sb and Br. This study demonstrates that combining an accelerated experimental cycle of learning and machine-learning based diagnosis represents an important step toward realizing fully-automated laboratories for materials discovery and development., Comment: NIPS 2018 Workshop: Machine Learning for Molecules and Materials

Perovskite solar cells have shown remarkable efficiencies beyond 22%, through organic and inorganic cation alloying. However, the role of alkali-metal cations is not well-understood. By using synchrotron-based nano-X-ray fluorescence and complementary measurements, we show that when adding RbI and/or CsI the halide distribution becomes homogenous. This homogenization translates into long-lived charge carrier decays, spatially homogenous carrier dynamics visualized by ultrafast microscopy, as well as improved photovoltaic device performance. We find that Rb and K phase-segregate in highly concentrated aggregates. Synchrotron-based X-ray-beam-induced current and electron-beam-induced current of solar cells show that Rb clusters do not contribute to the current and are recombination active. Our findings bring light to the beneficial effects of alkali metal halides in perovskites, and point at areas of weakness in the elemental composition of these complex perovskites, paving the way to improved performance in this rapidly growing family of materials for solar cell applications., Comment: updated author metadata

Successful materials innovations can transform society. However, materials research often involves long timelines and low success probabilities, dissuading investors who have expectations of shorter times from bench to business. A combination of emergent technologies could accelerate the pace of novel materials development by 10x or more, aligning the timelines of stakeholders (investors and researchers), markets, and the environment, while increasing return-on-investment. First, tool automation enables rapid experimental testing of candidate materials. Second, high-throughput computing (HPC) concentrates experimental bandwidth on promising compounds by predicting and inferring bulk, interface, and defect-related properties. Third, machine learning connects the former two, where experimental outputs automatically refine theory and help define next experiments. We describe state-of-the-art attempts to realize this vision and identify resource gaps. We posit that over the coming decade, this combination of tools will transform the way we perform materials research. There are considerable first-mover advantages at stake, especially for grand challenges in energy and related fields, including computing, healthcare, urbanization, water, food, and the environment., Comment: 22 pages, 3 figures