Selective catalytic reduction (SCR) of NO by ammonia is one of the dominant pollution abatement technologies for near-zero NO emission diesel engines. A crucial step in the reduction of NO to N with Cu zeolite NH-SCR catalysts is the generation of a multi-electron donating active site, implying the permanent or transient dimerization of Cu ions. Cu atom mobility has been implicated by computational chemistry as a key factor in this process. This report demonstrates how variable temperature H NMR reveals the Cu induced generation of sharp H resonances associated with a low concentration of sites on the zeolite. The onset temperature of the appearance of these signals was found to strongly correlate with the NH-SCR activity and was observed for a range of catalysts covering multiple frameworks (CHA, AEI, AFX, ERI, ERI-CHA, ERI-OFF, *BEA), with different Si/Al ratios and different Cu contents. The results point towards universal applicability of variable temperature NMR to predict the activity of a Cu-zeolite SCR catalyst. The unique relationship of a spectroscopic feature with catalytic behavior for zeolites with different structures and chemical compositions is exceptional in heterogeneous catalysis.
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http://dx.doi.org/10.3390/molecules28186456 | DOI Listing |
Environ Monit Assess
January 2025
Bhaskaracharya College of Applied Sciences, University of Delhi, New Delhi, Delhi, 110078, India.
This study investigates the spatio-temporal distribution of formaldehyde (HCHO) over the mainland Southeast Asian region (including Northeast India) from 2019 to 2022 using TROPOMI satellite data. HCHO is a key atmospheric trace gas which is influenced by both natural processes and anthropogenic activities. We analyze HCHO levels in relation to atmospheric species including carbon monoxide (CO), nitrogen dioxide (NO), and environmental factors such as land surface temperature (LST), precipitation (PPT), fire radiative power (FRP), and enhanced vegetation index (EVI).
View Article and Find Full Text PDFSci Rep
January 2025
Lawrence Livermore National Laboratory, Livermore, CA, USA.
Climate models simulate a wide range of temperatures in the Arctic. Here we investigate one of the main drivers of changes in surface temperature: the net surface heat flux in the models. We show that in the winter months of the dark Arctic, there is a more than two-fold difference in the net surface heat fluxes among the models, and this difference is dominated by the downward infrared radiation from clouds.
View Article and Find Full Text PDFResuscitation
January 2025
Division of Neurology, Department of Pediatrics, The Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA.
Aims: To determine which patient and cardiac arrest factors were associated with obtaining neuroimaging after in-hospital cardiac arrest, and among those patients who had neuroimaging, factors associated with which neuroimaging modality was obtained.
Methods: Retrospective cohort study of patients who survived in-hospital cardiac arrest (IHCA) and were enrolled in the ICU-RESUS trial (NCT02837497).
Results: We tabulated ultrasound (US), CT, and MRI frequency within 7 days following IHCA and identified patient and cardiac arrest factors associated with neuroimaging modalities utilized.
Int J Biol Macromol
January 2025
Chemical Department, Faculty of Science, Damietta University, Damietta 34517, Egypt.
The study investigated the enhancement of stability and efficacy in the removal of bivalent nickel ions (Ni(II)) by utilizing a cerium metal-organic framework (Ce-MOF) encapsulated within a food-grade algal matrix. This composite material is integrated into a dual-layer hydrogel containing chitosan and carboxymethyl cellulose. The enhancement of structural integrity in the final product can be attributed to the cross-linking process with epichlorohydrin, leading to the development of Ce-MOF-FGA/CMC-CS hydrogel beads.
View Article and Find Full Text PDFNutrition
December 2024
Central University of Jharkhand, Ranchi, Jharkland, India. Electronic address:
Objectives: Childhood stunting remains a significant public health issue in India, affecting approximately 35% of children under 5. Despite extensive research, existing prediction models often fail to incorporate diverse data sources and address the complex interplay of socioeconomic, demographic, and environmental factors. This study bridges this gap by employing machine learning methods to predict stunting at the household level, using data from the National Family Health Survey combined with satellite-driven datasets.
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