The impact of Metal–Ligand Cooperation in Hydrogenation of Carbon Dioxide Catalyzed by Ruthenium PNP Pincer

ACS Catalysis ◽  
2013 ◽  
Vol 3 (11) ◽  
pp. 2522-2526 ◽  
Author(s):  
Georgy A. Filonenko ◽  
Matthew P. Conley ◽  
Christophe Copéret ◽  
Martin Lutz ◽  
Emiel J. M. Hensen ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
The Impact ◽  
Hydrogenation Of Carbon Dioxide ◽  
Metal Ligand

scholarly journals Unraveling the Effect of Aromatic Groups in Mn(I)NNN Pincer Complexes on Carbon Dioxide Activation Using Density Functional Study

2021 ◽  
Vol 9 ◽  
Author(s):  
Saurabh Vinod Parmar ◽  
Vidya Avasare ◽  
Sourav Pal
Keyword(s):  
Carbon Dioxide ◽  
Transition Metal ◽  
Density Functional ◽  
Value Added ◽  
Catalytic Performance ◽  
Transition Metal Catalysts ◽  
Functional Study ◽  
Pincer Complexes ◽  
The Impact ◽  
Hydrogenation Of Carbon Dioxide

Carbon dioxide utilization is necessary to reduce carbon footprint and also to synthesize value-added chemicals. The transition metal pincer complexes are attractive catalysts for the hydrogenation of carbon dioxide to formic acid. There is a need to understand the factors affecting the catalytic performance of these pincer complexes through a structure–activity relationship study using computational methods. It is a well-established fact that aromatic functionalities offer stability and selectivity to transition metal catalysts. However, their impact on the performance of the catalysts is lesser known in the case of metal pincer complexes. Hence, it is necessary to investigate the catalytic performance of Mn(I)NNN pincer complexes with variably activated aromatic functionalities. In this context, 15 catalysts are designed by placing different types of aromatic rings at the pincer carbons and two terminal nitrogen of Mn(I)NNN pincer complexes. A benzene moiety, placed at C2–C3 carbons of Mn(I)NNN pincer complex with identical aromatic groups at the terminal nitrogen, is found to be most efficient toward CO2 hydrogenation than the rest of the catalysts. On the other hand, when N,N-dimethyl aniline is placed at C2–C3 carbons of Mn(I)NNN pincer complexes, then the catalytic performance is significantly decreased. Thus, the present study unravels the impact of aromatic groups in Mn(I)NNN pincer complexes toward the catalytic hydrogenation of carbon dioxide.

Climate-forced changes of bio-productivity of Belorussian terrestrial ecosystems

2019 ◽  
pp. 77-88
Author(s):  
S. A. Lysenko
Keyword(s):  
Climate Change ◽  
Carbon Dioxide ◽  
Vegetation Index ◽  
Precipitation Amount ◽  
Terrestrial Ecosystems ◽  
Vegetation Period ◽  
Climatic Trend ◽  
Vegetation Growth ◽  
Current Century ◽  
The Impact

The spatial and temporal particularities of Normalized Differential Vegetation Index (NDVI) changes over territory of Belarus in the current century and their relationship with climate change were investigated. The rise of NDVI is observed at approximately 84% of the Belarus area. The statistically significant growth of NDVI has exhibited at nearly 35% of the studied area (t-test at 95% confidence interval), which are mainly forests and undeveloped areas. Croplands vegetation index is largely descending. The main factor of croplands bio-productivity interannual variability is precipitation amount in vegetation period. This factor determines more than 60% of the croplands NDVI dispersion. The long-term changes of NDVI could be explained by combination of two factors: photosynthesis intensifying action of carbon dioxide and vegetation growth suppressing action of air warming with almost unchanged precipitation amount. If the observed climatic trend continues the croplands bio-productivity in many Belarus regions could be decreased at more than 20% in comparison with 2000 year. The impact of climate change on the bio-productivity of undeveloped lands is only slightly noticed on the background of its growth in conditions of rising level of carbon dioxide in the atmosphere.

Catalytic Hydrogenation of Carbon Dioxide to Fuels

2014 ◽  
Vol 18 (10) ◽  
pp. 1335-1345 ◽  
Author(s):  
Xuecheng Yan ◽  
Han Guo ◽  
Dongjiang Yang ◽  
Shilun Qiu ◽  
Xiangdong Yao
Keyword(s):  
Carbon Dioxide ◽  
Catalytic Hydrogenation ◽  
Hydrogenation Of Carbon Dioxide

scholarly journals An Examination of Green Credit Promoting Carbon Dioxide Emissions Reduction: A Provincial Panel Analysis of China

2021 ◽  
Vol 13 (13) ◽  
pp. 7148
Author(s):  
Wenjie Zhang ◽  
Mingyong Hong ◽  
Juan Li ◽  
Fuhong Li
Keyword(s):  
Carbon Dioxide ◽  
Carbon Emissions ◽  
Carbon Dioxide Emissions ◽  
Industrial Structure ◽  
Emissions Reduction ◽  
Green Finance ◽  
Carbon Emissions Reduction ◽  
Negative Effect ◽  
Emissions Intensity ◽  
The Impact

The implementation of green finance is a powerful measure to promote global carbon emissions reduction that has been highly valued by academic circles in recent years. However, the role of green credit in carbon emissions reduction in China is still lacking testing. Using a set of panel data including 30 provinces and cities, this study focused on the impact of green credit on carbon dioxide emissions in China from 2006 to 2016. The empirical results indicated that green credit has a significantly negative effect on carbon dioxide emissions intensity. Furthermore, after the mechanism examination, we found that the promotion impacts of green credit on industrial structure upgrading and technological innovation are two effective channels to help reduce carbon dioxide emissions. Heterogeneity analysis found that there are regional differences in the effect of green credit. In the western and northeastern regions, the effect of green credit is invalid. Quantile regression results implied that the greater the carbon emissions intensity, the better the effect of green credit. Finally, a further discussion revealed there exists a nonlinear correlation between green credit and carbon dioxide emissions intensity. These findings suggest that the core measures to promote carbon emission reduction in China are to continue to expand the scale of green credit, increase the technology R&D investment of enterprises, and to vigorously develop the tertiary industry.

scholarly journals Twenty Years of Research in Seabass and Seabream Welfare during Slaughter

Animals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 2164
Author(s):  
Ignacio de la Rosa ◽  
Pedro L. Castro ◽  
Rafael Ginés
Keyword(s):  
Carbon Dioxide ◽  
Adverse Reaction ◽  
Loss Of Consciousness ◽  
Stressful Situation ◽  
Clove Oil ◽  
Fish Welfare ◽  
Behavioural Responses ◽  
Welfare Indicators ◽  
New Methods ◽  
The Impact

The behavioural responses of fish to a stressful situation must be considered an adverse reaction caused by the perception of pain. Consequently, the handling prior to stunning and the immediacy of loss consciousness following stunning are the aspects to take into account during the slaughtering process. The most common commercial stunning method in seabream and seabass is based on hypothermia, but other methods such as electrical stunning, carbon dioxide narcosis or anaesthetic with clove oil, are discussed in relation to the time to reach the unconsciousness stage and some welfare indicators. Although seawater plus ice slurry is currently accepted in some guidelines of fish welfare well practices at slaughter, it cannot be considered completely adequate due to the deferred speed at which cause loss of consciousness. New methods of incorporating some kind of anaesthetic in the stunning tank could be a solution to minimize the impact on the welfare of seabass and seabream at slaughtering.

scholarly journals Estimating Causal Effects When the Treatment Affects All Subjects Simultaneously: An Application

2021 ◽  
Vol 5 (2) ◽  
pp. 22
Author(s):  
Chiara Binelli
Keyword(s):  
Machine Learning ◽  
Carbon Dioxide ◽  
Global Warming ◽  
Carbon Dioxide Emissions ◽  
Control Group ◽  
Sources Of Information ◽  
Average Global Temperature ◽  
Causal Impact ◽  
The Impact ◽  
Robust Evidence

Several important questions cannot be answered with the standard toolkit of causal inference since all subjects are treated for a given period and thus there is no control group. One example of this type of questions is the impact of carbon dioxide emissions on global warming. In this paper, we address this question using a machine learning method, which allows estimating causal impacts in settings when a randomized experiment is not feasible. We discuss the conditions under which this method can identify a causal impact, and we find that carbon dioxide emissions are responsible for an increase in average global temperature of about 0.3 degrees Celsius between 1961 and 2011. We offer two main contributions. First, we provide one additional application of Machine Learning to answer causal questions of policy relevance. Second, by applying a methodology that relies on few directly testable assumptions and is easy to replicate, we provide robust evidence of the man-made nature of global warming, which could reduce incentives to turn to biased sources of information that fuels climate change skepticism.

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