Chemical Forms of Mercury in Pyrite: Implications for Predicting Mercury Releases in Acid Mine Drainage Settings
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Correction was made to the funding and acknowledgment: Funding V.-A.G. was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division. R.R. and M.-T.N. were supported by the Laboratory Directed Research and Development Program at Pacific Northwest National Laboratory (PNNL) CheMSR Agile Investment. PNNL is operated by DE-AC05-76RLO01830. J.-B.L. and J.L. were supported by the National Natural Science Foundation of China (Nos. 21433005, 91645203, and 21590792). D.C.C. was supported by Research and Innovation at the University of Nevada, Reno. ACKNOWLEDGMENTS The research was performed using resources available through Research Computing at Pacific Northwest National Laboratory (PNNL), Tsinghua National Laboratory for Information Science and Technology and the Computational Chemistry Laboratory under Tsinghua Xuetang Talents Program, and University of Nevada, Reno High Performance Computing. J.- B.L. and J.L. acknowledge discussions with Dr. Yang-Gang Wang (Southern University of Science and Technology).
A great need exists for computationally efficient quantum simulation approaches that can achieve an accuracy similar to high-level theories at a fraction of the computational cost. In this regard, we have leveraged a machine-learned interaction potential based on Chebyshev polynomials to improve density functional tight binding (DFTB) models for organic materials. The benefit of our approach is two-fold: (1) many-body interactions can be corrected for in a systematic and rapidly tunable process, and (2) high-level quantum accuracy for a broad range of compounds can be achieved with ~0.3% of data required for one advanced deep learning potential. Our model exhibits both transferability and extensibility through comparison to quantum chemical results for organic clusters, solid carbon phases, and molecular crystal phase stability rankings. Overall, our efforts thus allow for high-throughput physical and chemical predictions with up to coupled-cluster accuracy for systems that are computationally intractable with standard approaches.
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The environmental and economic impacts of implementing a circular economy in plastic waste supply chains are not well understood. The proposed systems analysis framework assesses environmental, social, and economic impacts of plastic waste supply chains in a circular economy. The first objective of this article is to identify datasets, models and knowledge gaps associated with waste plastic supply chain processes, mainly in the U.S. Our literature review indicated that the best datasets exist for virgin plastic resin production, mechanical recycling, landfilling, and incineration, with materials recovery facility being intermediate, and with chemical recycling the lowest. The second objective of this article is to develop an illustrative application of the framework by conducting a preliminary systems analysis of PET bottles with closed-loop recycling. Here, the preliminary systems analysis of PET bottles utilized a linear programming optimization method. Our optimization model indicated that both chemical and mechanical recycling processes are needed to achieve a true circular economy of PET bottles with the least greenhouse gas emissions, specifically reductions of 24% when compared with the linear economy. Good quality and standardized life cycle assessment and techno-economic analysis studies are needed to better understand the environmental, economic, and social impacts of advanced sorting and chemical recycling technologies.
Lianas, or woody vines, and trees dominate the canopy of tropical forests and comprise the majority of tropical aboveground carbon storage. These growth forms respond differently to contemporary variation in climate and resource availability, but their responses to future climate change are poorly understood because there are very few predictive ecosystem models representing lianas. We compile a database of liana functional traits (846 species) and use it to parameterize a mechanistic model of liana-tree competition. The substantial difference between liana and tree hydraulic conductivity represents a critical source of inter-growth form variation. Here, we show that lianas are many times more sensitive to drying atmospheric conditions than trees as a result of this trait difference. Further, we use our competition model and projections of tropical hydroclimate based on Representative Concentration Pathway 4.5 to show that lianas are more susceptible to reaching a hydraulic threshold for viability by 2100.
Metabotropic glutamate receptors (mGluRs) are dimeric class C G-protein–coupled receptors that operate in glia and neurons. Glutamate affinity and efficacy vary greatly between the eight mGluRs. The molecular basis of this diversity is not understood. We used single-molecule fluorescence energy transfer to monitor the structural rearrangements of activation in the mGluR ligand binding domain (LBD). In saturating glutamate, group II homodimers fully occupy the activated LBD conformation (full efficacy) but homodimers of group III mGluRs do not. Strikingly, the reduced efficacy of Group III homodimers does not arise from differences in the glutamate binding pocket but, instead, from interactions within the extracellular dimerization interface that impede active state occupancy. By contrast, the functionally boosted mGluR II/III heterodimers lack these interface ‘brakes’ to activation and heterodimer asymmetry in the flexibility of a disulfide loop connecting LBDs greatly favors occupancy of the activated conformation. Our results suggest that dimerization interface interactions generate substantial functional diversity by differentially stabilizing the activated conformation. This diversity may optimize mGluR responsiveness for the distinct spatio-temporal profiles of synaptic versus extrasynaptic glutamate.
Abstract Kitaev exchange, a new paradigm in quantum magnetism research, occurs for 90° metal-ligand-metal links, $${t}_{2g}^{5}$$ t 2 g 5 transition ions, and sizable spin-orbit coupling. It is being studied in honeycomb compounds but also on triangular lattices. While for the former it is known by now that the Kitaev intersite couplings are ferromagnetic, for the latter the situation is unclear. Here we pin down the exchange mechanisms and determine the effective coupling constants in the $${t}_{2g}^{5}$$ t 2 g 5 triangular-lattice material NaRuO 2 , recently found to host a quantum spin liquid ground state. We show that, compared to honeycomb compounds, the characteristic triangular-lattice cation surroundings dramatically affect exchange paths and effective coupling parameters, changing the Kitaev interactions to antiferromagnetic. Quantum chemical analysis combined with subsequent effective spin model simulations provide perspective onto the nature of the experimentally observed quantum spin liquid—it seemingly implies fairly large antiferromagnetic second-neighbor isotropic exchange, and the atypical proximity to ferromagnetic order is related to ferromagnetic nearest-neighbor Heisenberg coupling.
Abstract Although metabolic syndrome (MetS) is linked to an elevated risk of cardiovascular disease (CVD), the cardiac-specific risk mechanism is unknown. Obesity, hypertension, and diabetes (all MetS components) are the most common form of CVD and represent risk factors for worse COVID-19 outcomes compared to their non MetS peers. Here, we use obese Yorkshire pigs as a highly relevant animal model of human MetS, where pigs develop the hallmarks of human MetS and reproducibly mimics the myocardial pathophysiology in patients. Myocardium-specific mass spectroscopy-derived metabolomics, proteomics, and transcriptomics enabled the identity and quality of proteins and metabolites to be investigated in the myocardium to greater depth. Myocardium-specific deregulation of pro-inflammatory markers, propensity for arterial thrombosis, and platelet aggregation was revealed by computational analysis of differentially enriched pathways between MetS and control animals. While key components of the complement pathway and the immune response to viruses are under expressed, key N6-methyladenosin RNA methylation enzymes are largely overexpressed in MetS. Blood tests do not capture the entirety of metabolic changes that the myocardium undergoes, making this analysis of greater value than blood component analysis alone. Our findings create data associations to further characterize the MetS myocardium and disease vulnerability, emphasize the need for a multimodal therapeutic approach, and suggests a mechanism for observed worse outcomes in MetS patients with COVID-19 comorbidity.
Abstract For workplaces which cannot operate as telework or remotely, there is a critical need for routine occupational SARS-CoV-2 diagnostic testing. Although diagnostic tests including the CDC 2019-Novel Coronavirus (2019-nCoV) Real-Time RT-PCR Diagnostic Panel (CDC Diagnostic Panel) (EUA200001) were made available early in the pandemic, resource scarcity and high demand for reagents and equipment necessitated priority of symptomatic patients. There is a clearly defined need for flexible testing methodologies and strategies with rapid turnaround of results for (1) symptomatic, (2) asymptomatic with high-risk exposures and (3) asymptomatic populations without preexisting conditions for routine screening to address the needs of an on-site work force. We developed a distinct SARS-CoV-2 diagnostic assay based on the original CDC Diagnostic Panel (EUA200001), yet, with minimum overlap for currently employed reagents to eliminate direct competition for limited resources. As the pandemic progressed with testing loads increasing, we modified the assay to include 5-sample pooling and amplicon target multiplexing. Analytical sensitivity of the pooled and multiplexed assays was rigorously tested with contrived positive samples in realistic patient backgrounds. Assay performance was determined with clinical samples previously assessed with an FDA authorized assay. Throughout the pandemic we successfully tested symptomatic, known contact and travelers within our occupational population with a ~ 24–48-h turnaround time to limit the spread of COVID-19 in the workplace. Our singleplex assay had a detection limit of 31.25 copies per reaction. The three-color multiplexed assay maintained similar sensitivity to the singleplex assay, while tripling the throughput. The pooling assay further increased the throughput to five-fold the singleplex assay, albeit with a subtle loss of sensitivity. We subsequently developed a hybrid ‘multiplex-pooled’ strategy to testing to address the need for both rapid analysis of samples from personnel at high risk of COVID infection and routine screening. Herein, our SARS-CoV-2 assays specifically address the needs of occupational healthcare for both rapid analysis of personnel at high-risk of infection and routine screening that is essential for controlling COVID-19 disease transmission. In addition to SARS-CoV-2 and COVID-19, this work demonstrates successful flexible assays developments and deployments with implications for emerging highly transmissible diseases and future pandemics.
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