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Free, publicly-accessible full text available May 24, 2025
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Conventional time-of-flight methods can be used to determine carrier mobilities for photovoltaic cells in which the transit time between electrodes is greater than the RC time constant of the device. To measure carrier drift on sub-ns timescales, we have recently developed a two-pulse time-of-flight technique capable of detecting drift velocities with 100-ps time resolution in perovskite materials. In this method, the rates of carrier transit across the active layer of a device are determined by varying the delay time between laser pulses and measuring the magnitude of the recombination-induced nonlinearity in the photocurrent. Here, we present a related experimental approach in which diffractive optic-based transient grating spectroscopy is combined with our two-pulse time-of-flight technique to simultaneously probe drift and diffusion in orthogonal directions within the active layer of a photovoltaic cell. Carrier density gratings are generated using two time-coincident pulse-pairs with passively stabilized phases. Relaxation of the grating amplitude associated with the first pulse-pair is detected by varying the delay and phase of the density grating corresponding to the second pulse-pair. The ability of the technique to reveal carrier diffusion is demonstrated with model calculations and experiments conducted using MAPbI3 photovoltaic cells.
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Low-dimensional organic/inorganic hybrid perovskites (OIHPs) are a promising class of materials with a wide range of potential applications in optoelectronics and other fields since these materials can synergistically combine individual features of organic molecules and inorganics into unique properties. Non-covalent interactions are commonly observed in OIHPs, in particular, π-effect interactions between the organic cations. Such non-covalent interactions can significantly influence important properties of the low-dimensional OIHPs, including dielectric confinement, bandgap, photoluminescence, quantum efficiency, charge mobility, trap density, stability, and chirality. This perspective reviews recent studies of non-covalent interactions involving the π systems of organic cations in low-dimensional OIHPs. The analysis of crystal structures of low-dimensional OIHPs offers significant insight into understanding such non-covalent interactions and their impacts on specific properties of these OIHPs. The developed structure–property relationships can be used to engineer non-covalent interactions in low-dimensional OIHPs for applications.more » « less
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Prodromal detection of Alzheimer’s Disease(AD) is a substantial challenge in the research community. Among the tools used in AD diagnosis, cognitive exams are standard in most procedures. However, the barrage of cognitive examinations is both time and resource consuming. With the use of Machine Learning, Feature Elimination (FE) can be combined with classification algorithms to determine which cognitive exams are best suited for diagnosis. Using the results of FE, it can be determined if subsections of different composite scores can be combined to create a new enhanced and exhaustive exam. This paper implements a Recursive Feature Elimination with Cross Validation (RFECV) machine learning algorithm to determine which cognitive exams perform best for AD classification tasks. Out of 119 features, an average of 16 features were selected as optimal. These optimal features average 75% Accuracy, 70% Precision, and 75% Recall and an F1 Weighted score of 71% in classification.more » « less
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Analyzing the hippocampus in the brain through magnetic resonance imaging (MRI) plays a crucial role in diagnosing and making treatment decisions for several neurological diseases. Hippocampus atrophy is among the most informative early diagnostic biomarkers of Alzheimer's disease (AD), yet its automatic segmentation is extremely difficult given the anatomical structure of the brain and the lack of any contrast in between its different regions. The gold standard remains manual segmentation and the use of brain atlases. In this study, we use a well-known image segmentation model, UNet++, and introduce an attention mechanism called the Convolutional Block Attention Module (CBAM) to the UNet++ model. This integrated model improves the feature weights of our region of interest, and hence increases the accuracy in segmenting the hippocampus. Results show averages of 0.8715, 0.8107, 0.8872, and 0.9039 for the metrics of Dice, Jaccard, Precision, and Recall, respectively.more » « less
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Conventional time-of-flight (TOF) measurements yield charge carrier mobilities in photovoltaic cells with time resolution limited by the RC time constant of the device, which is on the order of 0.1–1 µs for the systems targeted in the present work. We have recently developed an alternate TOF method, termed nonlinear photocurrent spectroscopy (NLPC), in which carrier drift velocities are determined with picosecond time resolution by applying a pair of laser pulses to a device with an experimentally controlled delay time. In this technique, carriers photoexcited by the first laser pulse are “probed” by way of recombination processes involving carriers associated with the second laser pulse. Here, we report NLPC measurements conducted with a simplified experimental apparatus in which synchronized 40 ps diode lasers enable delay times up to 100 µs at 5 kHz repetition rates. Carrier mobilities of ∼0.025 cm2/V/s are determined for MAPbI3 photovoltaic cells with active layer thicknesses of 240 and 460 nm using this instrument. Our experiments and model calculations suggest that the nonlinear response of the photocurrent weakens as the carrier densities photoexcited by the first laser pulse trap and broaden while traversing the active layer of a device. Based on this aspect of the signal generation mechanism, experiments conducted with co-propagating and counter-propagating laser beam geometries are leveraged to determine a 60 nm length scale of drift velocity dispersion in MAPbI3 films. Contributions from localized states induced by thermal fluctuations are consistent with drift velocity dispersion on this length scale.
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Utilization of the interaction between spin and heat currents is the central focus of the field of spin caloritronics. Chiral phonons possessing angular momentum arising from the broken symmetry of a non-magnetic material create the potential for generating spin currents at room temperature in response to a thermal gradient, precluding the need for a ferromagnetic contact. Here we show the observation of spin currents generated by chiral phonons in a two-dimensional layered hybrid organic–inorganic perovskite implanted with chiral cations when subjected to a thermal gradient. The generated spin current shows a strong dependence on the chirality of the film and external magnetic fields, of which the coefficient is orders of magnitude larger than that produced by the reported spin Seebeck effect. Our findings indicate the potential of chiral phonons for spin caloritronic applications and offer a new route towards spin generation in the absence of magnetic materials.more » « less