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Abstract Earthquake location programs employ diverse approaches to address the challenges posed by incomplete knowledge and simplified representation of complex Earth structures. Assessing their reliability in location and uncertainty characterization remains challenging as benchmark datasets with known event locations are rare, and usually confined to particular sources, such as quarry blasts. This study evaluates eight earthquake location methods (GrowClust, HypoDD, Hypoinverse, HypoSVI, NonLinLoc, NonLinLoc_SSST, VELEST, and XCORLOC) through a controlled synthetic computational experiment on 1000 clustered earthquakes based on the setting of the 2019 Ridgecrest, California, earthquake sequence. We construct a travel-time dataset using the fast-marching method and a 3D velocity model extracted from the Community Velocity Model, supplemented with a von Karman perturbation to represent small-scale heterogeneity, and including elevation effects. Picking errors, phase availability, and outliers are introduced to mimic difficulties encountered in seismic network monitoring. We compare the location results from eight programs applied to the same travel-time dataset and 1D velocity structure against the ground-truth locations. For this aftershock sequence, our results reveal the superior accuracy and precision of differential time-based location methods compared to single-event location methods. The results validate the significance of compensating for deviations from assumed 1D velocity structure either by path or site correction modeling or by cancellation of unmodeled structure using differential arrival times. We also evaluate the uncertainty quantification of each program and find that most of the programs underestimate the errors. 

Plasmonic catalysis is uniquely positioned between photo/electrochemistry and thermal chemistry such that multiple factors may compete to dominate the reaction enhancement mechanism. The adoption of norms originating in both photochemistry and thermal chemistry has resulted in the use of language and methods of data analysis, which, in the context of plasmonic catalysis, may be implicitly contradictory. This article tracks several years of research towards understanding thermal and nonthermal effects in plasmonic catalysis and culminates with a discussion on how the choice of language and presentation of data can be tuned to avoid subtle yet significant contradictory implications. 

Plasmonic photocatalysis presents a promising method for light-to-matter conversion. However, most current studies focused on understanding the relative importance of thermal and nonthermal effects while their synergistic effects remained less studied. Here, we propose an index, termed Overall Light Effectiveness (OLE), to capture the combined impact of these light effects on reactions. By systematically varying the thickness of catalyst layers, we isolated thermal and nonthermal contributions and optimized them to achieve maximum light enhancement. We demonstrate the approach using carbon dioxide hydrogenation reaction on titania-supported rhodium nanoparticles as a model reaction system. It shows a generalizable potential in designing catalyst systems with optimum combinations of heating and light illumination, especially with broadband light illumination such as sunlight, for achieving the most economical light-to-matter conversion in plasmonic catalysis. 

Plasmonic photocatalysis is an emerging research field that holds promise for sustainable energy applications, particularly in solar energy conversion. In this study, we focus on the enhancement of broadband light absorption capabilities for plasmonic photocatalyst under white light illumination. By replacing parts of the catalyst with solar absorber, we can significantly improve the total reaction rate under mild heating conditions with less catalyst. Through careful comparison of reaction conditions and systematic optimization of the contributions from photothermal and non-thermal effects, we demonstrate a substantial enhancement in broadband light absorption capacity and overall light effectiveness, paving the way for advanced plasmonic photocatalysts with greater efficiency and practical applicability using solar light as the energy source.