Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph)

Biological neural networks define the brain function and intelligence of humans and other mammals, and form ultra-large, spatial, structured graphs. Their neuronal organization is closely interconnected with the spatial organization of the brain's microvasculature, which supplies oxygen to the neurons and builds a complementary spatial graph. This vasculature (or the vessel structure) plays an important role in neuroscience; for example, the organization of (and changes to) vessel structure can represent early signs of various pathologies, e.g. Alzheimer's disease or stroke. Recently, advances in tissue clearing have enabled whole brain imaging and segmentation of the entirety of the mouse brain's vasculature. Building on these advances in imaging, we are presenting an extendable dataset of whole-brain vessel graphs based on specific imaging protocols. Specifically, we extract vascular graphs using a refined graph extraction scheme leveraging the volume rendering engine Voreen and provide them in an accessible and adaptable form through the OGB and PyTorch Geometric dataloaders. Moreover, we benchmark numerous state-of-the-art graph learning algorithms on the biologically relevant tasks of vessel prediction and vessel classification using the introduced vessel graph dataset. Our work paves a path towards advancing graph learning research into the field of neuroscience. Complementarily, the presented dataset raises challenging graph learning research questions for the machine learning community, in terms of incorporating biological priors into learning algorithms, or in scaling these algorithms to handle sparse,spatial graphs with millions of nodes and edges. All datasets and code are available for download at this https UR

Sn-Pb Mixed Perovskites with Fullerene-Derivative Interlayers for Efficient Four-Terminal All-Perovskite Tandem Solar Cells

Interfacial engineering is the key to high-performance perovskite solar cells (PSCs). While a wide range of fullerene interlayers are investigated for Pb-based counterparts with a bandgap of >1.5 eV, the role of fullerene interlayers is barely investigated in Sn-Pb mixed narrow-bandgap (NBG) PSCs. In this work, two novel solution-processed fullerene derivatives are investigated, namely indene-C60-propionic acid butyl ester and indene-C60-propionic acid hexyl ester (IPH), as the interlayers in NBG PSCs. It is found that the devices with IPH-interlayer show the highest performance with a remarkable short-circuit current density of 30.7 mA cm−2 and a low deficit in open-circuit voltage. The reduction in voltage deficit down to 0.43 V is attributed to reduced non-radiative recombination that the authors attribute to two aspects: 1) a higher conduction band offset of ≈0.2 eV (>0 eV) that hampers charge-carrier-back-transfer recombination; 2) a decrease in trap density at the perovskite/interlayer/C60 interfaces that results in reduced trap-assisted recombination. In addition, incorporating the IPH interlayer enhances charge extraction within the devices that results in considerable enhancement in short-circuit current density. Using a NBG device with an IPH interlayer, a respectable power conversion efficiency of 24.8% is demonstrated in a four-terminal all-perovskite tandem solar cell

A for-loop is all you need. For solving the inverse problem in the case of personalized tumor growth modeling

Solving the inverse problem is the key step in evaluating the capacity of a physical model to describe real phenomena. In medical image computing, it aligns with the classical theme of image-based model personalization. Traditionally, a solution to the problem is obtained by performing either sampling or variational inference based methods. Both approaches aim to identify a set of free physical model parameters that results in a simulation best matching an empirical observation. When applied to brain tumor modeling, one of the instances of image-based model personalization in medical image computing, the overarching drawback of the methods is the time complexity of finding such a set. In a clinical setting with limited time between imaging and diagnosis or even intervention, this time complexity may prove critical. As the history of quantitative science is the history of compression (Schmidhuber and Fridman, 2018), we align in this paper with the historical tendency and propose a method compressing complex traditional strategies for solving an inverse problem into a simple database query task. We evaluated different ways of performing the database query task assessing the trade-off between accuracy and execution time. On the exemplary task of brain tumor growth modeling, we prove that the proposed method achieves one order speed-up compared to existing approaches for solving the inverse problem. The resulting compute time offers critical means for relying on more complex and, hence, realistic models, for integrating image preprocessing and inverse modeling even deeper, or for implementing the current model into a clinical workflow. The code is available at https://github.com/IvanEz/for-loop-tumor

Chemical vapor deposited polymer layer for efficient passivation of planar perovskite solar cells

Reducing non-radiative recombination losses by advanced passivation strategies is pivotal to maximize the power conversion efficiency (PCE) of perovskite solar cells (PSCs). Previously, polymers such as poly(methyl methacrylate), poly(ethylene oxide), and polystyrene were successfully applied in solution-processed passivation layers. However, controlling the thickness and homogeneity of these ultra-thin passivation layers on top of polycrystalline perovskite thin films is a major challenge. In response to this challenge, this work reports on chemical vapor deposition (CVD) polymerization of poly(p-xylylene) (PPX) layers at controlled substrate temperatures (14–16 °C) for efficient surface passivation of perovskite thin films. Prototype double-cation PSCs using a ∼1 nm PPX passivation layer exhibit an increase in open-circuit voltage (V$_{OC}$) of ∼40 mV along with an enhanced fill factor (FF) compared to a non-passivated PSC. These improvements result in a substantially enhanced PCE of 20.4% compared to 19.4% for the non-passivated PSC. Moreover, the power output measurements over 30 days under ambient atmosphere (relative humidity ∼40–50%) confirm that the passivated PSCs are more resilient towards humidity-induced degradation. Considering the urge to develop reliable, scalable and homogeneous deposition techniques for future large-area perovskite solar modules, this work establishes CVD polymerization as a novel approach for the passivation of perovskite thin films

A review of assessment methods for river hydromorphology

The work leading to this paper has received funding for the EU’s FP7 under Grant Agreement No. 282656 (REFORM

Device Performance of Emerging Photovoltaic Materials (Version 3)

Following the 2nd release of the “Emerging PV reports,” the best achievements in the performance of emerging photovoltaic devices in diverse emerging photovoltaic research subjects are summarized, as reported in peer-reviewed articles in academic journals since August 2021. Updated graphs, tables, and analyses are provided with several performance parameters, e.g., power conversion efficiency, open-circuit voltage, short-circuit current density, fill factor, light utilization efficiency, and stability test energy yield. These parameters are presented as a function of the photovoltaic bandgap energy and the average visible transmittance for each technology and application, and are put into perspective using, e.g., the detailed balance efficiency limit. The 3rd installment of the “Emerging PV reports” extends the scope toward triple junction solar cells

Device Performance of Emerging Photovoltaic Materials (Version 3)

Following the 2nd release of the “Emerging PV reports,” the best achievements in the performance of emerging photovoltaic devices in diverse emerging photovoltaic research subjects are summarized, as reported in peer-reviewed articles in academic journals since August 2021. Updated graphs, tables, and analyses are provided with several performance parameters, e.g., power conversion efficiency, open-circuit voltage, short-circuit current density, fill factor, light utilization efficiency, and stability test energy yield. These parameters are presented as a function of the photovoltaic bandgap energy and the average visible transmittance for each technology and application, and are put into perspective using, e.g., the detailed balance efficiency limit. The 3rd installment of the “Emerging PV reports” extends the scope toward triple junction solar cells

Device Performance of Emerging Photovoltaic Materials (Version 2)

Following the 1st release of the “Emerging photovoltaic (PV) reports”, the best achievements in the performance of emerging photovoltaic devices in diverse emerging photovoltaic research subjects are summarized, as reported in peer-reviewed articles in academic journals since August 2020. Updated graphs, tables, and analyses are provided with several performance parameters, e.g., power conversion efficiency, open-circuit voltage, short-circuit current density, fill factor, light utilization efficiency, and stability test energy yield. These parameters are presented as a function of the photovoltaic bandgap energy and the average visible transmittance for each technology and application and are put into perspective using, e.g., the detailed balance efficiency limit. The 2nd instalment of the “Emerging PV reports” extends the scope toward tandem solar cells and presents the current state-of-the-art in tandem solar cell performance for various material combinations.</p

Device Performance of Emerging Photovoltaic Materials (Version 1)

Emerging photovoltaics (PVs) focus on a variety of applications complementing large scale electricity generation. Organic, dye‐sensitized, and some perovskite solar cells are considered in building integration, greenhouses, wearable, and indoor applications, thereby motivating research on flexible, transparent, semitransparent, and multi‐junction PVs. Nevertheless, it can be very time consuming to find or develop an up‐to‐date overview of the state‐of‐the‐art performance for these systems and applications. Two important resources for recording research cells efficiencies are the National Renewable Energy Laboratory chart and the efficiency tables compiled biannually by Martin Green and colleagues. Both publications provide an effective coverage over the established technologies, bridging research and industry. An alternative approach is proposed here summarizing the best reports in the diverse research subjects for emerging PVs. Best performance parameters are provided as a function of the photovoltaic bandgap energy for each technology and application, and are put into perspective using, e.g., the Shockley–Queisser limit. In all cases, the reported data correspond to published and/or properly described certified results, with enough details provided for prospective data reproduction. Additionally, the stability test energy yield is included as an analysis parameter among state‐of‐the‐art emerging PVs