RESUMO
Vapor-based deposition techniques are emerging approaches for the design of carbon-supported metal powder electrocatalysts with tailored catalyst entities, sizes, and dispersions. Herein, a pulsed CVD (Pt-pCVD) approach is employed to deposit different Pt entities on mesoporous N-doped carbon (MPNC) nanospheres to design high-performance hydrogen evolution reaction (HER) electrocatalysts. The influence of consecutive precursor pulse number (50-250) and deposition temperature (225-300 °C) are investigated. The Pt-pCVD process results in highly dispersed ultrasmall Pt clusters (≈1 nm in size) and Pt single atoms, while under certain conditions few larger Pt nanoparticles are formed. The best MPNC-Pt-pCVD electrocatalyst prepared in this work (250 pulses, 250 °C) reveals a Pt HER mass activity of 22.2 ± 1.2 A mg-1 Pt at -50 mV versus the reversible hydrogen electrode (RHE), thereby outperforming a commercially available Pt/C electrocatalyst by 40% as a result of the increased Pt utilization. Remarkably, after optimization of the Pt electrode loading, an ultrahigh Pt mass activity of 56 ± 2 A mg-1 Pt at -50 mV versus RHE is found, which is among the highest Pt mass activities of Pt single atom and cluster-based electrocatalysts reported so far.
RESUMO
Background: Machine 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. Methods: We 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. Results: A 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. Conclusion: The 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.
RESUMO
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-1 Pt and 11.7 ± 0.6 A mg-1 Cat 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.
