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Abstract This article introduces a general processing framework to effectively utilize waveform data stored on modern cloud platforms. The focus is hybrid processing schemes for which a local system drives processing. We show that downloading files and doing all processing locally is problematic even when the local system is a high-performance computing (HPC) cluster. Benchmark tests with parallel processing show that approach always creates a bottleneck as the volume of data being handled increases with more processes pulling data. We find a hybrid model for which processing to reduce the volume of data transferred from the cloud servers to the local system can dramatically improve processing time. Tests implemented with the Massively Parallel Analysis System for Seismology (MsPASS) utilizing Amazon Web Service’s (AWS) Lambda service yield throughput comparable to processing day files on a local HPC file system. Given the ongoing migration of seismology data to cloud storage, our results show doing some or all processing on the cloud will be essential for any processing involving large volumes of data. 

Abstract With the rise of data volume and computing power, seismological research requires more advanced skills in data processing, numerical methods, and parallel computing. We present the experience of conducting training workshops in various forms of delivery to support the adoption of large-scale high-performance computing (HPC) and cloud computing, advancing seismological research. The seismological foci were on earthquake source parameter estimation in catalogs, forward and adjoint wavefield simulations in 2D and 3D at local, regional, and global scales, earthquake dynamics, ambient noise seismology, and machine learning. This contribution describes the series of workshops delivered as part of research projects, the learning outcomes for participants, and lessons learned by the instructors. Our curriculum was grounded on open and reproducible science, large-scale scientific computing and data mining, and computing infrastructure (access and usage) for HPC and the cloud. We also describe the types of teaching materials that have proven beneficial to the instruction and the sustainability of the program. We propose guidelines to deliver future workshops on these topics. 

We present an overview of the opportunities provided by bimetallic core–shell nanocrystals, followed by a discussion of the challenges and promising solutions regarding the elucidation of the true surface composition and its dynamics. 

Abstract Combining different precious metals to generate alloy nanocrystals with desirable shapes and compositions remains a challenge because of the low miscibility between these metals and/or the different reduction potentials of their salt precursors. Specifically, Rh and Pd are considered to be immiscible in the bulk solid over the entire composition range. Here we demonstrate that Rh−Pd alloy nanorods with well‐distributed and tunable compositions can be synthesized using a one‐pot polyol method. The success of our synthesis relies on the introduction of bromide as a coordination ligand to tune the redox potentials of Rh(III) and Pd(II) ions for the achievement of co‐reduction. The atomic ratio of the Rh−Pd alloy nanorods can be facilely tuned by changing the molar feeding ratio between the two precursors. We also systematically investigate the effects of water on the morphology of the Rh−Pd alloy nanocrystals. In an attempt to promote future use of these alloy nanorods, we successfully scale up their synthesis in a continuous‐flow reactor with no degradation to the product quality.