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Red-phosphorescent bis-cyclometalated iridium compounds with salicylaldimine, 2-picolinamide, and related ancillary ligand classes are described; the 2-picolinamide analogues exhibit multiple binding modes that influence photophysical properties.

 

This perspective focuses on strategies to manipulate and optimize three key determinants of metal-based molecular photosensitizers – the absorption profile, the excited-state redox potentials, and the excited-state lifetime.

 

In salt marshes of the Southeastern USA, purple marsh crabs (Sesarma reticulatum), hereafterSesarma, aggregate in grazing and burrowing fronts at the heads of tidal creeks, accelerating creek incision into marsh platforms. We explored the effects of this keystone grazer and sediment engineer on salt marsh sediment accumulation, hydrology, and carbon (C) and nitrogen (N) turnover using radionuclides (²¹⁰Pb and⁷Be), total hydrolyzable amino acids (THAA), and C and N stable isotopes (δ¹³C and δ¹⁵N) in sediment from pairedSesarma-grazed and un-grazed creeks.Sesarma-grazed-creek sediments exhibited greater bioturbation and tidal inundation compared to sediments in un-grazed creeks, as indicated by larger²¹⁰Pb and⁷Be inventories. Total organic carbon (TOC) to total nitrogen (TN) weight ratios (C:N) were higher and δ¹⁵N values were lower in grazed-creek sediments than in un-grazed-creek sediments, suggestingSesarmaremove and assimilate N in their tissues, and excrete N with lower δ¹⁵N values into sediments. In support of this inference, the percent total carbon (TC) and percent TOC declined by nearly half, percent TN decreased by ~ 80%, and the C:N ratio exhibited a ~ threefold increase betweenSesarmafore-gut and hind-gut contents. An estimated 91% ofSesarma’s diet was derived fromSpartina alterniflora,the region’s dominant salt marsh plant. We found that, asSesarmagrazing fronts progress across marsh landscapes, they enhance the decay ofSpartina-derived organic matter and prolong marsh tidal inundation. These findings highlight the need to better account for the effects of keystone grazers and sediment engineers, likeSesarma, in estimates of the stability and size of blue C stores in coastal wetlands.

 

The early-type star gamma Cas illuminates the reflection nebulae IC 59 and IC 63, creating two photodissociation regions (PDRs). Uncertainties about the distances to the nebulae and the resulting uncertainty about the density of the radiation fields incident on their surfaces have hampered the study of these PDRs during the past three decades. We employed far-ultraviolet (UV) – optical nebula – star colour differences of dust-scattered light to infer the locations of the nebulae relative to the plane of the sky containing gamma Cas, finding IC 63 to be positioned behind the star and IC 59 in front of the star. To obtain the linear distances of the nebulae relative to gamma Cas, we fit far-infrared archival Herschel flux data for IC 59 and IC 63 with modified blackbody curves and relate the resulting dust temperatures with the luminosity of gamma Cas, yielding approximate distances of 4.15 pc for IC 59 and 2.3 pc for IC 63. With these distances, using updated far-UV flux data in the 6–13.6 eV range for gamma Cas with two recent determinations of the interstellar extinction for gamma Cas, we estimate that the far-UV radiation density at the surface of IC 63 takes on values of G0 = 58 or G0 = 38 with respective values for E(B − V) for gamma Cas of 0.08 and 0.04 mag. This is a substantial reduction from the range 150 ≤ G0 ≤ 650 used for IC 63 during the past three decades. The corresponding, even lower new values for IC 59 are G0 = 18 and G0 = 12.

 

A strongly photoreducing iridium photosensitizer enables diverse, additive-free reductive photoredox transformations on challenging ketone and imine substrates.

 

Sedimentary arsenic (As) in the shallow aquifers of Bangladesh is enriched in finer-grained de-posits rich in organic matter, clays, and iron (Fe)-oxides. In Bangladesh, sediment color is a use-ful indicator of pore-water As concentrations. The pore-waters of orange sediments are usually associated with lower As concentrations (<50 µg/L) owing to abundant Fe-oxides which sorb As. Using this color signal as a guide, spectroscopic measurements alongside thermal treatment have been extensively utilized for analyzing the properties of both Fe-oxides and clay minerals. This study uses Fourier transform infrared (FTIR) and diffuse reflectance (DR) measurements along with thermal treatment to evaluate the solid-phase associations of As from sediment col-lected along the Meghna River in Bangladesh. The samples analyzed in this study were chosen to represent the various lithologies present at the study site and included riverbank sands (1 m depth), silt (6 m depth), aquifer sand (23 m depth), and a clay aquitard (37 m depth). The concen-trations of sedimentary As and Fe were measured by X-Ray Fluorescence and the spectroscopic measurements were taken on the samples prior to the thermal treatment. For the thermal treat-ment, sediment samples were placed in a preheated furnace at 600C for 3 hours. The thermal treatment caused a deepening of reddish-brown hues in all samples, and the greatest change of color was observed in the finer-grained samples. The FTIR spectral analysis revealed that the clay minerals were composed primarily of illite, smectite, and kaolinite. The DR results indicat-ed that the majority of Fe in sands was present as goethite; however, in the clay and silt samples, Fe was incorporated into the structure of clay minerals as Fe(II). The amount of structural Fe(II) was strongly positively correlated with the sedimentary As concentrations, which were highest in the finer-grained samples. After thermal treatment, the concentrations of As in the finer-grained samples decreased by an average of 40% whereas the change in the As concentrations of the sand samples was negligible. These findings indicate that significant proportions of solid-phase As may be retained by OM and Fe(II)-bearing clay minerals. 

MLCommons is an effort to develop and improve the artificial intelligence (AI) ecosystem through benchmarks, public data sets, and research. It consists of members from start-ups, leading companies, academics, and non-profits from around the world. The goal is to make machine learning better for everyone. In order to increase participation by others, educational institutions provide valuable opportunities for engagement. In this article, we identify numerous insights obtained from different viewpoints as part of efforts to utilize high-performance computing (HPC) big data systems in existing education while developing and conducting science benchmarks for earthquake prediction. As this activity was conducted across multiple educational efforts, we project if and how it is possible to make such efforts available on a wider scale. This includes the integration of sophisticated benchmarks into courses and research activities at universities, exposing the students and researchers to topics that are otherwise typically not sufficiently covered in current course curricula as we witnessed from our practical experience across multiple organizations. As such, we have outlined the many lessons we learned throughout these efforts, culminating in the need forbenchmark carpentryfor scientists using advanced computational resources. The article also presents the analysis of an earthquake prediction code benchmark while focusing on the accuracy of the results and not only on the runtime; notedly, this benchmark was created as a result of our lessons learned. Energy traces were produced throughout these benchmarks, which are vital to analyzing the power expenditure within HPC environments. Additionally, one of the insights is that in the short time of the project with limited student availability, the activity was only possible by utilizing a benchmark runtime pipeline while developing and using software to generate jobs from the permutation of hyperparameters automatically. It integrates a templated job management framework for executing tasks and experiments based on hyperparameters while leveraging hybrid compute resources available at different institutions. The software is part of a collection calledcloudmeshwith its newly developed components, cloudmesh-ee (experiment executor) and cloudmesh-cc (compute coordinator).