Optical Switching of Hole Transfer in Double-Perovskite/Graphene Heterostructure

Synergically combining their respective ultrahigh charge mobility and strong light absorption, graphene (Gr)/semiconductor heterostructures are promising building blocks for efficient optoelectronics, particularly photodetectors. Charge transfer (CT) across the heterostructure interface crucially determines device efficiency and functionality. Here, it is reported that hole-transfer processes dominate the ultrafast CT across strongly coupled double-perovskite Cs2AgBiBr6/graphene (DP/Gr) heterostructures following optical excitation. While holes are the primary charges flowing across interfaces, their transfer direction, as well as efficiency, show a remarkable dependence on the excitation wavelength. For excitation with photon energies below the bandgap of DPs, the photoexcited hot holes in Gr can compete with the thermalization process and inject into in-gap defect states in DPs. In contrast, above-bandgap excitation of DP reverses the hole-transfer direction, leading to hole transfer from the valence band of DPs to Gr. Experimental evidence that increasing the excitation photon energy enhances CT efficiency for both below- and above-bandgap photoexcitation regimes is further provided, unveiling the positive role of excess energy in enhancing interfacial CT. The possibility of switching the hole-transfer direction and thus the interfacial photogating field by tuning the excitation wavelength, provides a novel way to control the interfacial charge flow across a DP/Gr heterojunction.</p

Subspace corrected relevance learning with application in neuroimaging

In machine learning, data often comes from different sources, but combining them can introduce extraneous variation that affects both generalization and interpretability. For example, we investigate the classification of neurodegenerative diseases using FDG-PET data collected from multiple neuroimaging centers. However, data collected at different centers introduces unwanted variation due to differences in scanners, scanning protocols, and processing methods. To address this issue, we propose a two-step approach to limit the influence of center-dependent variation on the classification of healthy controls and early vs. late-stage Parkinson's disease patients. First, we train a Generalized Matrix Learning Vector Quantization (GMLVQ) model on healthy control data to identify a “relevance space” that distinguishes between centers. Second, we use this space to construct a correction matrix that restricts a second GMLVQ system's training on the diagnostic problem. We evaluate the effectiveness of this approach on the real-world multi-center datasets and simulated artificial dataset. Our results demonstrate that the approach produces machine learning systems with reduced bias - being more specific due to eliminating information related to center differences during the training process - and more informative relevance profiles that can be interpreted by medical experts. This method can be adapted to similar problems outside the neuroimaging domain, as long as an appropriate “relevance space” can be identified to construct the correction matrix.</p