The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/solr.202100662.
Off‐grid devices require autonomous power supply systems that can be achieved via coupling a solar cell with a supercapacitor in one integrated multifunctional device that is able to harvest energy from the environment, store, and release it on demand. Herein, a straight‐forward approach is proposed to realize a monolithic photosupercapacitor rechargeable under illumination by integrating a silicon (Si) solar cell with an electrochemical double‐layer capacitor (EDLC) based on mesoporous N‐doped carbon nanospheres (MPNC) in a three‐electrode configuration, i.e., via a shared electrode. The optimized porous structure of the MPNC‐EDLC electrodes results in a high storage efficiency of 95% and a superior performance compared to an activated carbon based EDLC. When monolithically integrated with a highly compatible and technologically robust Si solar cell in a photosupercapacitor, fast charging up to 0.63 V (2.5 V for a module of four photosupercapacitors connected in series) in less than 5 s is attained even under weak illumination conditions, with an outstanding peak overall efficiency of 11.8%. These results show high potential toward the development of photorechargeable and decentralized power sources for deployed smart electronic devices.
Platinum is one of the best‐performing catalysts for the hydrogen evolution reaction (HER). However, high cost and scarcity severely hinder the large‐scale application of Pt electrocatalysts. Constructing highly dispersed ultrasmall Platinum entities is thereby a very effective strategy to increase Pt utilization and mass activities, and reduce costs. Herein, highly dispersed Pt entities composed of a mixture of Pt single atoms, clusters, and nanoparticles are synthesized on mesoporous N‐doped carbon nanospheres. The presence of Pt single atoms, clusters, and nanoparticles is demonstrated by combining among others aberration‐corrected annular dark‐field scanning transmission electron microscopy, X‐ray absorption spectroscopy, and electrochemical CO stripping. The best catalyst exhibits excellent geometric and Pt HER mass activity, respectively ≈4 and 26 times higher than that of a commercial Pt/C reference and a Pt catalyst supported on nonporous N‐doped carbon nanofibers with similar Pt loadings. Noteworthily, after optimization of the geometrical Pt electrode loading, the best catalyst exhibits ultrahigh Pt and catalyst mass activities (56 ± 3 A mg−1Pt and 11.7 ± 0.6 A mg−1Cat at −50 mV vs. reversible hydrogen electrode), which are respectively ≈1.5 and 58 times higher than the highest Pt and catalyst mass activities for Pt single‐atom and cluster‐based catalysts reported so far.
Organic batteries are considered as environmentally friendly alternative to lithium‐ion batteries due to the application of transition metal‐free redox‐active polymers. One well‐established polymer is poly(3‐vinyl‐N‐methylphenothiazine) (PVMPT) with a fast reversibility of the electrochemical redox reaction at a potential of 3.5 V versus Li|Li+. The oxidized PVMPT is soluble in many standard battery electrolytes, which diminishes its available specific capacity but at the same time can lead to a unique charge/discharge mechanism involving a redeposition process upon discharge. Herein, the influence of different conductive carbon additives and their properties, e.g., specific surface area, pore size distribution, and electrical conductivity, on the dissolution behavior of oxidized PVMPT is investigated. Compared to the state‐of‐the‐art conductive carbon Super C65 employed in many organic battery electrodes, Ketjenblack EC‐300J and EC‐600J reduce the dissolution of the oxidized PVMPT due to better immobilization on the carbon additive and in the resulting 3D structure of the electrode, as assessed by N2‐physisorption, electrochemical, UV–vis spectroscopy and scanning electron microscopy investigations. The studies demonstrate that a dense packing of the carbon particles in the electrode is decisive for the stable immobilization of PVMPT while maintaining its long‐term cycling performance.
Internet of Things devices – wireless sensors and actuators – require long-term off-grid power sources that would be cheap, and at the same time have a small footprint with low environmental impact throughout their life cycle. Such an off-grid power source could be realized with a photosupercapacitor: A solar cell is integrated with an electrochemical double-layer capacitor (EDLC) into a single monolithic device using a three-electrode design, where the two components share a common electrode.1 The solar cell converts ambient light into an electric current and charges the integrated supercapacitor via the common electrode, which in turn powers the external device. The supercapacitor, on the other hand, acts as a buffer, mediating between an intermittent light source and the load of the device. In terms of footprint and hence energy and power density, integrated monolithic photosupercapacitors have a clear advantage in comparison with simply wiring a solar cell to a supercapacitor as storage unit. When compared to batteries or pseudocapacitors that involve slow solid-state diffusion reactions and/or surface redox processes, EDLCs are particularly suited for the integration with solar cells into photosupercapacitors as they are able to operate efficiently in fast fluctuating environments where rapid on/off cycles are required without the need to supply a fixed voltage onset.2 Owing to their large specific surface area, good electrical conductivity, and electrochemical stability, mesoporous nitrogen-doped carbon nanospheres (MPNCs) show a great promise for being implemented as an electrode material for supercapacitors.3 , 4 To synthesize MPNCs we used a hard-templating strategy based on the polymerization and self-assembly of aniline in the presence of SiO2 nanoparticles (7 nm), which led to the formation of spherical SiO2/Polyaniline composites. Their carbonization and template removal resulted in highly monodisperse, 140 nm-diameter spherical MPNCs with a large specific surface area (825 m2 g-1) and defined mesopores (7 nm). The MPNCs are easily dispersible and could be processed by doctor-blading in homogeneous 3D-percolated electrodes. Benefiting from the well-defined mesoporous network and the highly accessible electrochemical surface area, our MPNC-based gel-electrolyte freestanding EDLC delivered large energy and power densities and a high capacitance (400 F g-1 at 1 A g-1) with high (95 %) coulombic efficiency. To achieve an energy-autonomous self-powered system, we combined the MPNC-based EDLCs with halide-perovskite-based solar cells in a monolithic three-electrode fashion. As the solar cell we used a p-i-n perovskite solar cell with a FA0.75Cs0.25Pb(I0.8Br0.2)3 (high bandgap) absorber and large photosensitive area (1 cm2) delivering a high open-short circuit voltage (VOC ) of 1.08 V and a short-circuit current (JSC ) of 17.9 mA/cm2. To facilitate the assembly process and to address the adverse sensitivity of the perovskite layer to the electrolyte solvent, we optimized the solar cell layer sequence and termination. At the same time, we used a semi-solid gel electrolyte for the EDLC, which minimizes the contact of the perovskite layer with the electrolyte solvent. This allowed us to obtain a free-standing integrated photosupercapacitor without the necessity of any encapsulation. This reduces the overall device footprint and cost, and simplifies the assembly. Benefiting from the high efficiency of the solar cell and the excellent performance of the EDLC, the integrated hybrid photosupercapacitor demonstrated fast (< 5 s) photocharging up to 1 V under 1 sun illumination and discharge through the EDLC terminals, proving that the solar energy was harvested, converted, and stored. The photosupercapacitor delivered 4.27 μWh/cm2 at a power density of 0.29 mW/cm2 and 1.68 μWh/cm2 at 2.2mW/cm2 with an areal capacitance of 31 mF/cm2. This resulted in an unprecedented overall electrochemical energy conversion efficiency of 11.5 %.5 The strategy for photosupercapacitor fabrication presented here was extended to the integration of MPNC-based EDLCs with other types of solar cells, depending on the intended application. The achieved results pave the way towards the development of off-grid powered energy-autonomous devices. 1 Q. Zeng, Y. Lai, L. Jiang, F. Liu, X. Hao, L. Wang and M. A. Green, Adv. Energy Mater., 2020, 10, 1–30. 2 F. Béguin, V. Presser, A. Balducci and E. Frackowiak, Adv. Mater., 2014, 26, 2219–2251. 3 J. Melke, R. Schuster, S. Möbus, T. Jurzinsky, P. Elsässer, A. Heilemann and A. Fischer, Carbon N. Y., 2019, 146, 44–59. 4 J. Melke, J. Martin, M. Bruns, P. Hu, A. Scho, A. Fischer, S. M. Isaza, F. Fink and P. Elsa, ACS Appl. Energy Mater, 2020, 3, 11627. 5 T. Berestok, C. Diestel, N. Ortlieb, J. Buettner, J. Matthews, P. S. C. Schulze, J. C. Goldschmidt, S. W. Glunz and A. Fischer, Sol. RRL, 2021, 5, 1–13.
BackgroundMachine learning (ML) is an inquiry domain that aims to establish methodologies that leverage information to enhance performance of various applications. In the healthcare domain, the ML concept has gained prominence over the years. As a result, the adoption of ML algorithms has become expansive. The aim of this scoping review is to evaluate the application of ML in pancreatic surgery.MethodsWe integrated the preferred reporting items for systematic reviews and meta-analyses for scoping reviews. Articles that contained relevant data specializing in ML in pancreas surgery were included.ResultsA search of the following four databases PubMed, Cochrane, EMBASE, and IEEE and files adopted from Google and Google Scholar was 21. The main features of included studies revolved around the year of publication, the country, and the type of article. Additionally, all the included articles were published within January 2019 to May 2022.ConclusionThe integration of ML in pancreas surgery has gained much attention in previous years. The outcomes derived from this study indicate an extensive literature gap on the topic despite efforts by various researchers. Hence, future studies exploring how pancreas surgeons can apply different learning algorithms to perform essential practices may ultimately improve patient outcomes.
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