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Proppe, Andrew H.

Publications and source records attributed to Proppe, Andrew H..

9 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Time-Resolved Line Shapes of Single Quantum Emitters via Machine Learned Photon Correlations

Solid-state single-photon emitters (SPEs) are quantum light sources that combine atomlike optical properties with solid-state integration and fabrication capabilities. SPEs are hindered by spectral diffusion, where the emitter’s surrounding environment induces random energy fluctuations. Timescales of spectral diffusion span nanoseconds to minutes and require probing single emitters to remove ensemble averaging. Photon correlation Fourier spectroscopy (PCFS) can be used to measure time-resolved single emitter line shapes, but is hindered by poor signal-to-noise ratio in the measured correlation functions at early times due to low photon counts. Here, we develop a framework to simulate PCFS correlation functions directly from diffusing spectra that match well with experimental data for single colloidal quantum dots. We use these simulated datasets to train a deep ensemble autoencoder machine learning model that outputs accurate, noiseless, and probabilistic reconstructions of the noisy correlations. Using this model, we obtain reconstructed time-resolved single dot emission line shapes at timescales as low as 10 ns, which are otherwise completely obscured by noise. This enables PCFS to extract optical coherence times on the same timescales as Hong-Ou-Mandel two-photon interference, but with the advantage of providing spectral information in addition to estimates of photon indistinguishability. Further, our machine learning approach is broadly applicable to different photon correlation spectroscopy techniques and SPE systems, offering an enhanced tool for probing single emitter line shapes on previously inaccessible timescales.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

Uncovering temperature-dependent exciton-polariton relaxation mechanisms in hybrid organic-inorganic perovskites

Hybrid perovskites have emerged as a promising material candidate for exciton-polariton (polariton) optoelectronics. Thermodynamically, low-threshold Bose-Einstein condensation requires efficient scattering to the polariton energy dispersion minimum, and many applications demand precise control of polariton interactions. Thus far, the primary mechanisms by which polaritons relax in perovskites remains unclear. In this work, we perform temperature-dependent measurements of polaritons in low-dimensional perovskite wedged microcavities achieving a Rabi splitting of ℏΩ Rabi = 260 ± 5 meV. We change the Hopfield coefficients by moving the optical excitation along the cavity wedge and thus tune the strength of the primary polariton relaxation mechanisms in this material. We observe the polariton bottleneck regime and show that it can be overcome by harnessing the interplay between the different excitonic species whose corresponding dynamics are modified by strong coupling. This work provides an understanding of polariton relaxation in perovskites benefiting from efficient, material-specific relaxation pathways and intracavity pumping schemes from thermally brightened excitonic species.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Adversarial autoencoder ensemble for fast and probabilistic reconstructions of few-shot photon correlation functions for solid-state quantum emitters

Second-order photon correlation measurements [g (2) (τ) functions] are widely used to classify single-photon emission purity in quantum emitters or to measure the multiexciton quantum yield of emitters that can simultaneously host multiple excitations – such as quantum dots – by evaluating the value of g (2) (τ = 0). Accumulating enough photons to accurately calculate this value is time consuming and could be accelerated by fitting of few-shot photon correlations. Here, we develop an uncertainty-aware, deep adversarial autoencoder ensemble (AAE) that reconstructs noise-free g (2) (τ) functions from noise-dominated, few-shot inputs. The model is trained with simulated g (2) (τ) functions that are facilely generated by Poisson sampling time bins. The AAE reconstructions are performed orders-of-magnitude faster, with reconstruction errors and estimates of g (2) (τ = 0) that are lower in variance and similar in accuracy compared to Maximum likelihood estimation and Levenberg-Marquardt least-squares fitting approaches, for simulated and experimentally measured few-shot g (2) (τ) functions (~100 two-photon events) of InP/ZnS/ZnSe and CdS/CdSe/CdS quantum dots. The deep-ensemble model comprises eight individual autoencoders, allowing for probabilistic reconstructions of noise-free g (2) (τ) functions, and we show that the predicted variance scales inversely with number of shots, with comparable uncertainties to computationally intensive Markov chain Monte Carlo sampling. Furthermore, this work demonstrates the advantage of machine learning models to perform uncertainty-aware, fast, and accurate reconstructions of simple Poisson-distributed photon correlation functions, allowing for on-the-fly reconstructions and accelerated materials characterization of solid-state quantum emitters.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Quantum-size-tuned heterostructures enable efficient and stable inverted perovskite solar cells

The energy landscape of reduced-dimensional perovskites (RDPs) can be tailored by adjusting their layer width (n). Recently, 2D/3D heterostructures containing n = 1 and 2 RDPs have produced PSCs with > 25% power conversion efficiency (PCE). Unfortunately, this method does not translate to inverted PSCs due to electron blocking at the 2D/3D interface. Here we report a method to increase the layer width of RDPs in 2D/3D heterostructures in order to address this problem. We discover that bulkier organics form 2D heterostructures more slowly, resulting in wider RDPs; and that small modifications to ligand design induce preferential growth of n ≥ 3 RDPs. Levering these insights, we developed efficient inverted PSCs (certified 23.91% quasi-steady state efficiency). Furthermore, unencapsulated devices operate at room temperature and ~50% relative humidity for over 1000 hrs without loss of PCE; and, when subjected to ISOS-L3 accelerated aging encapsulated devices retain 92% of PCE after 500 hrs.

14 SOLAR ENERGY↗
DOE OSTI2020

Bioinspiration in light harvesting and catalysis

Capturing and converting solar energy into fuels and feedstocks is a global challenge that spans numerous disciplines and fields of research. Billions of years of evolution have allowed natural organisms to hone strategies for harvesting light from the sun and storing energy in the form of carbon–carbon and carbon–hydrogen bonds. Photosynthetic antenna proteins capture solar photons and funnel photoexcitations to reaction centres with high yields, and enzymes catalyze multi-electron reactions, facilitating chemical transformations not yet efficiently implemented using artificially engineered catalysts. Researchers in renewable energy often look to nature to understand the mechanisms at work and, if possible, to explore their translation into artificial systems. Here, we review advances in bioinspiration across the fields of biological light harvesting and chemical energy conversion. We examine how multi-photon and multi-electron reactions in biology can inspire new methods in photoredox chemistry to achieve novel, selective and complex organic transformations; how carbonic-dehydrogenase-inspired design principles enable catalytic reactions such as the conversion of CO 2 into useful products such as fuels; and how concepts from photosynthetic antenna complexes and reaction centres can benefit artificial light-harvesting materials. We then consider areas in which bioinspiration could enable advances in the rational design of molecules and materials, the expansion of the synthetic capabilities of catalysts and the valorization of molecular building blocks. Here, we highlight the challenges that must be overcome to realize these advances and propose new directions that may use bioinspiration to achieve them.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Multi-cation perovskites prevent carrier reflection from grain surfaces

The composition of perovskite has been optimized combinatorially such that it often contains six components (A x B y C 1-x-y PbX z Y 3-z ) in state-of-art perovskite solar cells. Questions remain regarding the precise role of each component, and the lack of a mechanistic explanation limits the practical exploration of the large and growing chemical space. Here, aided by transient photoluminescence microscopy, we find that, in perovskite single crystals, carrier diffusivity is in fact independent of composition. In polycrystalline thin films, the different compositions play a crucial role in carrier diffusion. In this work, we report that methylammonium (MA)-based films show a high carrier diffusivity of 0.047 cm 2 s -1 , while MA-free mixed caesium-formamidinium (CsFA) films exhibit an order of magnitude lower diffusivity. Elemental composition studies show that CsFA grains display a graded composition. This curtails electron diffusion in these films, as seen in both vertical carrier transport and surface potential studies. Incorporation of MA leads to a uniform grain core-to-edge composition, giving rise to a diffusivity of 0.034 cm 2 s -1 in CsMAFA films. A model that invokes competing crystallization processes allows us to account for this finding, and suggests further strategies to achieve homogeneous crystallization for the benefit of perovskite optoelectronics.

36 MATERIALS SCIENCE↗