Localized surface plasmon resonance (LSPR) is a physical phenomenon exhibited by nanoparticles of metals including coinage metals, alkali metals, aluminum, and some semiconductors which translates into electromagnetic, thermal, and chemical properties. In the past decade, LSPR has been taken advantage of in the context of catalysis. While plasmonic nanoparticles (PNPs) have been successfully applied toward enhancing catalysis of inorganic reactions such as water splitting, they have also demonstrated exciting performance in the catalysis of organic transformations with potential applications in synthesis of molecules from commodity to pharmaceutical compounds. The advantages of this approach include improved selectivity, enhanced reaction rates, and milder reaction conditions. This review provides the basics of LSPR theory, details the mechanisms at play in plasmon-enhanced nanocatalysis, sheds light onto such nanocatalyst design, and finally systematically presents the breadth of organic reactions hence catalyzed.

We here present the experiences collected maintaining and updating the MoSGrid science gateway over the past years. Insights are provided on a technical and organizational level useful for the design and operation of science gateways in general. The specific challenges faced and solved are considered to be valuable for other communities.

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Background As technical developments in omics and biomedical imaging increase the throughput of data generation in life sciences, the need for information systems capable of managing heterogeneous digital assets is increasing. In particular, systems supporting the findability, accessibility, interoperability, and reusability (FAIR) principles of scientific data management. Results We propose a Service Oriented Architecture approach for integrated management and analysis of multi-omics and biomedical imaging data. Our architecture introduces an image management system into a FAIR-supporting, web-based platform for omics data management. Interoperable metadata models and middleware components implement the required data management operations. The resulting architecture allows for FAIR management of omics and imaging data, facilitating metadata queries from software applications. The applicability of the proposed architecture is demonstrated using two technical proofs of concept and a use case, aimed at molecular plant biology and clinical liver cancer research, which integrate various imaging and omics modalities. Conclusions We describe a data management architecture for integrated, FAIR-supporting management of omics and biomedical imaging data, and exemplify its applicability for basic biology research and clinical studies. We anticipate that FAIR data management systems for multi-modal data repositories will play a pivotal role in data-driven research, including studies which leverage advanced machine learning methods, as the joint analysis of omics and imaging data, in conjunction with phenotypic metadata, becomes not only desirable but necessary to derive novel insights into biological processes.