349 research outputs found
Effect of intrinsic point defects on the catalytic and electronic properties of Cu2WS4 single layer: Ab initio calculations
The challenges imposed by climate change require the continued improvement and identification of materials for the development of green technologies. Point defect engineering is a promising technology for producing green hydrogen by taking advantage of catalytic hydrogen evolution reactions. In this work, we investigate the role of anionic and cationic vacancy point defects, as well as the nature of the active sites, in the catalytic activation of Cu2WS4 single layers. The stability of the pristine and defective structures of Cu2WS4 has been thoroughly investigated using density-functional theory calculations. A deep analysis of the formation enthalpy indicates that the Cu vacancy is the chemically most favorable vacancy. However, the calculated adsorption energy indicates that the presence of such vacancies slightly enhances the hydrogen evolution reaction. In contrast, the formation of an S vacancy considerably magnifies the same reaction in Cu2WS4 single layers
Hybrid localized graph kernel for machine learning energy-related properties of molecules and solids
Nowadays, the coupling of electronic structure and machine learning
techniques serves as a powerful tool to predict chemical and physical
properties of a broad range of systems. With the aim of improving the accuracy
of predictions, a large number of representations for molecules and solids for
machine learning applications has been developed. In this work we propose a
novel descriptor based on the notion of molecular graph. While graphs are
largely employed in classification problems in cheminformatics or
bioinformatics, they are not often used in regression problem, especially of
energy-related properties. Our method is based on a local decomposition of
atomic environments and on the hybridization of two kernel functions: a graph
kernel contribution that describes the chemical pattern and a Coulomb label
contribution that 1encodes finer details of the local geometry. The accuracy of
this new kernel method in energy predictions of molecular and condensed phase
systems is demonstrated by considering the popular QM7 and BA10 datasets. These
examples show that the hybrid localized graph kernel outperforms traditional
approaches such as, for example, the smooth overlap of atomic positions (SOAP)
and the Coulomb matrices
Theoretical study and analysis of o-nitrophenol adsorption using layered double hydroxides containing ca-al, ni-al and zn-al
A theoretical assessment of the o-nitrophenol adsorption on layered double hydroxides containing different metallic species (Ca-Al, Ni-Al and Zn-Al) was performed. Experimental o-nitrophenol adsorption isotherms obtained at different adsorption temperatures with these layered double hydroxides were analyzed using a statistical physics monolayer model. Model calculations showed that the o-nitrophenol aggregation could occur with a high degree. It was estimated that the o-nitrophenol adsorption implied a non-flat orientation on all adsorbent surfaces and this process was multi-molecular. It was also demonstrated that there was no significant difference on the o-nitrophenol adsorption capacities of tested adsorbents, which varied from 77 to 135, 95 to 122 and 74 and 130 mg/g for Ca-Al, Ni-Al and Zn-Al layered double hydroxides, respectively. This finding suggested that the incorporation of Ca-Al, Ni-Al and Zn-Al in the layered double hydroxide structure played a similar role to adsorb o-nitrophenol molecules from aqueous solution. Calculated adsorption energies and thermodynamic functions confirmed an exothermic adsorption with the presence of physical-based interaction forces. This paper highlights the importance of reliable theoretical calculations based on statistical physics theory to contribute in the understanding of the adsorption mechanisms of a relevant water pollutant using layered double hydroxides as promising adsorbents for industrial applications
Yellow fever virus capsid protein is a potent suppressor of RNA silencing that binds double-stranded RNA
Mosquito-borne flaviviruses, including yellow fever virus (YFV), Zika virus (ZIKV), and West Nile virus (WNV), profoundly affect human health. The successful transmission of these viruses to a human host depends on the pathogen’s ability to overcome a potentially sterilizing immune response in the vector mosquito. Similar to other invertebrate animals and plants, the mosquito’s RNA silencing pathway comprises its primary antiviral defense. Although a diverse range of plant and insect viruses has been found to encode suppressors of RNA silencing, the mechanisms by which flaviviruses antagonize antiviral small RNA pathways in disease vectors are unknown. Here we describe a viral suppressor of RNA silencing (VSR) encoded by the prototype flavivirus, YFV. We show that the YFV capsid (YFC) protein inhibits RNA silencing in the mosquito Aedes aegypti by interfering with Dicer. This VSR activity appears to be broadly conserved in the C proteins of other medically important flaviviruses, including that of ZIKV. These results suggest that a molecular “arms race” between vector and pathogen underlies the continued existence of flaviviruses in nature
Trapping of Ag+, Cu2+, and Co2+ by faujasite zeolite Y: new interpretations of the adsorption mechanism via DFT and statistical modeling investigation
This work evaluated the potential of a synthesized faujasite-type zeolite Y as an adsorbent for the removal of relevant heavy metals such as silver (Ag+), copper (Cu2+), and cobalt (Co2+). The adsorption data of Ag+, Cu2+, and Co2+ ions were determined experimentally at pH 6 and temperatures of 298, 308, and 318 K. Two theoretical approaches have been applied based on statistical physics modeling and density functional theory (DFT) to understand and characterize the ion exchanges involved in the removal of all metals. Results showed that this zeolite was more efficient for the adsorption of Ag+ via cation-exchange. Based on the physical modelling, the removal of heavy metals on this zeolite was mono and multi-ionic (simple and multi-interactions), where the ions interacted via one and two adsorption sites. It was also noted that the temperature increment generated more available functional groups of the zeolite, facilitating the access to the smaller cavities and the interactions with the adsorbent. Adsorption energies for removing these metals with tested zeolite were slightly endothermic and were consistent with the typical values reported for ion exchange systems of heavy metals + zeolites. DFT results demonstrated that these cationic exchange energies depend on the nature of precursor salt, but with the same ranking. Both statistical and DFT approaches agreed that exchange Ag+ in zeolite Y was easier than Cu2+ and Co2+. Overall, the application of both theoretical approaches provided a reliable interpretation of the adsorption mechanism
Mentoring Prospective Engineering Students Through the After School Program Girls in Engineering Focused on Building an Underwater Remotely Operated Vehicle
A number of studies by engineering education researchers have pointed out that all-female teams, rather than mixed teams, result in better forms of participation and interaction in engineering related after-school programs and clubs. In particular, for after-school programs or clubs that form in response to a STEM competition, all-female teams have better chances of developing. One such competition, which will be discussed in this paper, is a regional Marine Advanced Technology Education (MATE) competition in which students from Blind_Review High School have been participating for many years.For each year’s competition, an all-female team of students enrolled in the Career and Technical Education program at Blind_Review High School, City, State build an underwater autonomous robotic vehicle, for which the robot specifications and competition rules are formulated each year by the MATE regional competition. Any team participating in the competition must have a mentor, and the students must be enrolled in courses within the engineering studies program. This paper will discuss the collaboration developed between the high school and college students, how the mentorship program was delivered, and how the program successfully helped future engineering students to establish their engineering and future STEM identities
High-Pressure Properties of Wolframite-Type ScNbO4
In this work, we used Raman spectroscopic and optical absorption measurements and first-principles calculations to unravel the properties of wolframite-type ScNbO4 at ambient pressure and under high pressure. We found that monoclinic wolframite-type ScNbO4 is less compressible than most wolframites and that under high pressure it undergoes two phase transitions at ∼5 and ∼11 GPa, respectively. The first transition induces a 9% collapse of volume and a 1.5 eV decrease of the band gap energy, changing the direct band gap to an indirect one. According to calculations, pressure induces symmetry changes (P2/c–Pnna–P2/c). The structural sequence is validated by the agreement between phonon calculations and Raman experiments and between band structure calculations and optical absorption experiments. We also obtained the pressure dependence of Raman modes and proposed a mode assignment based upon calculations. They also provided information on infrared modes and elastic constants. Finally, noncovalent and charge analyses were employed to analyze the bonding evolution of ScNbO4 under pressure. They show that the bonding nature of ScNbO4 does not change significantly under pressure. In particular, the ionicity of the wolframite phase is 61% and changes to 63.5% at the phase transition taking place at ∼5 GPa
High-resolution 18F-FDG PET with MRI for monitoring response to treatment in rheumatoid arthritis
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