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Abstract. Endmember mixing analysis (EMMA) is often used by hydrogeochemiststo interpret the sources of stream solutes, but variations in streamconcentrations and discharges remain difficult to explain. We discoveredthat machine learning can be used to highlight patterns in stream chemistrythat reveal information about sources of solutes and subsurface groundwaterflowpaths. The investigation has implications, in turn, for the balance ofCO2 in the atmosphere. For example, CO2-driven weathering ofsilicate minerals removes carbon from the atmosphere over ∼106-year timescales. Weathering of another common mineral, pyrite, releases sulfuricacid that in turn causes dissolution of carbonates. In that process,however, CO2 is released instead of sequestered from the atmosphere. Thus, understanding long-term global CO2 sequestration by weatheringrequires quantification of CO2- versus H2SO4-drivenreactions. Most researchers estimate such weathering fluxes from streamchemistry, but interpreting the reactant minerals and acids dissolved in streams has been fraught with difficulty. We apply a machine-learningtechnique to EMMA in three watersheds to determine the extent of mineraldissolution by each acid, without pre-defining the endmembers. The resultsshow that the watersheds continuously or intermittently sequester CO2, but the extent of CO2 drawdown is diminished in areas heavily affectedby acid rain. Prior to applying the new algorithm, CO2 drawdown wasoverestimated. The new technique, which elucidates the importance ofdifferent subsurface flowpaths and long-timescale changes in the watersheds,should have utility as a new EMMA for investigating water resourcesworldwide. 

In spite of numerous programs and interventions, homelessness remains a significant societal concern. Long-term homelessness is particularly problematic because it can be increasingly difficult to escape from, and because it represents a continuous drain on societal resources. This paper develops a model for predicting long-term homelessness in response to a simple question: if an individual becomes homeless, what influences the individual's slide to long-term homelessness? The data we analyze to answer the question comes from the City of Boston. The model points to race, veteran status, disability, and age as key factors that predict this slide. The paper describes and illustrates the model along with problems encountered in data preparation and cleansing, prior scholarly work that helped to shape our decisions, and collaboration with participants in the ecosystem for homeless care that complemented the model-building effort. The results are important because they point to possible policy interventions (programs and funding) and process improvements (at homeless shelters) to mitigate this slide. 

A multi-component radical addition strategy enables difunctionalization of alkenes with heteroarenes and a variety of radical precursors, including N 3 , P(O)R 2 , and CF 3 . This unified approach for coupling diverse classes of electrophilic radicals and heteroarenes to vinyl ethers allows for direct, vicinal C–C as well as C–N, C–P, and C–R f bond formation. 

Due to the facile manipulation and non-invasive nature of light-triggered release, it is one of the most potent ways to selectively and remotely deliver a molecular target. Among the various carrier platforms, plasmonic nanoparticles possess advantages such as enhanced cellular uptake and easy loading of “cargo” molecules. Two general strategies are currently utilized to achieve light-induced molecule release from plasmonic nanoparticles. The first uses femtosecond laser pulses to directly break the bond between the nanoparticle and the loaded target. The other requires significant photo-thermal effects to weaken the interaction between the cargo molecules and nanoparticle-attached host molecules. Different from above mechanisms, herein, we introduce a new light-controlled molecular-release method by taking advantage of a plasmon-driven catalytic reaction at the particle surface. In this strategy, we link the target to a plasmon responsive molecule, 4-aminobenzenethiol (4-ABT), through the robust and simple EDC coupling reaction and subsequently load the complex onto the particles via the strong Au–thiol interaction. Upon continuous-wave (CW) laser illumination, the excited surface plasmon catalyzes the formation of 4,4′-dimercaptoazobenzenethiol (DMAB) and simultaneously releases the loaded molecules with high efficiency. This method does not require the use of high-power pulsed lasers, nor does it rely on photo-thermal effects. We believe that plasmon-driven release strategies open a new direction for the designing of next-generation light-triggered release processes.