RESUMO
It is of great research interest to understand the nanostructures contributing to the activity observed in the reduction of oxygen by non-platinum group metal (PGM) electrocatalysts in acidic media. Iron- and nitrogen-containing carbon networks are often the most studied structures, among which single-atom iron-coordinated nitrogen (FeNx) moieties have often been proposed to be the structures leading to the high activity in these non-PGM electrocatalysts. Iron nanoparticles embedded within a carbon support are also formed under certain conditions as a result of the synthetic processes in making non-PGM electrocatalysts. In this study, we present a study to understand the oxygen reduction reaction (ORR) activity of prepared iron- and nitrogen-containing non-PGM electrocatalysts obtained through the pyrolysis of metal-organic framework (MOF) precursors. We studied the structure-property relationship among nanostructures made from the MOF precursor ZIF-8 under different pyrolysis conditions. Density functional theory calculations were used to explain the effect of structural moieties on the ORR activity. Our results suggest that iron-coordinated C-N structures and iron nanoparticles act synergistically to catalyze the ORR.
RESUMO
Non-platinum group metal (non-PGM) electrocatalysts for the oxygen reduction reaction (ORR) are generally composed of iron, nitrogen, and carbon synthesized through high-temperature pyrolysis. Among the various types of precursors, metal-organic frameworks (MOFs), zeolitic imidazolate framework (ZIF)-8 in particular, have often been used in the synthesis. The pyrolysis of ZIF-8 precursor relies on the use of Zn as a sacrificial metal (SM), and the optimal processing temperatures often exceed 1000 °C to generate active non-PGM catalysts. The high pyrolysis temperature tends to result in heterogeneous active moieties ranging from Fe single atoms to nanoparticles. In this study, we present the synthesis of non-PGM catalysts using Cd as the sacrificial metal instead of Zn. By using Cd, we were able to generate active non-PGM electrocatalysts from the MOF precursors at a low pyrolysis temperature of 750 °C, which helps preserve the single atomic iron active sites.
RESUMO
Novel and developing artificial intelligence (AI) systems can be integrated into healthcare settings in numerous ways. For example, in the case of automated image classification and natural language processing, AI systems are beginning to demonstrate near expert level performance in detecting abnormalities such as seizure activity. This paper, however, focuses on AI integration into clinical trials. During the clinical trial recruitment process, considerable labor and time is spent sifting through electronic health record and interviewing patients. With the advancement of deep learning techniques such as natural language processing, intricate electronic health record data can be efficiently processed. This provides utility to workflows such as recruitment for clinical trials. Studies are starting to show promise in shortening the time to recruitment and reducing workload for those involved in clinical trial design. Additionally, numerous guidelines are being constructed to encourage integration of AI into the healthcare setting with meaningful impact. The goal would be to improve the clinical trial process by reducing bias in patient composition, improving retention of participants, and lowering costs and labor.