Abundant Greenhouse Gas Research Articles

A Synopsis on CO2 Capture by Synthetic Hydrogen Bonding Receptors.

Carbon dioxide (CO2) is one of the most abundant greenhouse gases in Earth's atmosphere and responsible for global warming. Therefore, aerial CO2 capture and sequestration has become a major task for human community. Though several adsorbents for CO2 including activated carbon, zeolites, metal-organic frameworks (MOFs), and other surface-modified porous materials are well developed, the supramolecular approaches using synthetic hydrogen-bonding receptors are less explored. This review article highlights the synthetic development of various artificial receptors and their properties toward fixation of aerial CO2 as carbonate (CO3 2-), bicarbonate (HCO3 -), or carbamate (-NHCOO-/>NCOO-) ions, induced by excess fluoride (F-) or hydroxide (OH-) ions as their tetrabutylammonium salts. The utilization of encapsulated carbonate/bicarbonate/carbamate complexes in anion exchange metathesis for separation of oxyanions from aqueous solutions are also discussed. In addition, the release of CO2 and regeneration of receptor molecules are described in a number of occasions. Most importantly, the formation of anion complexes as crystalline materials in solid-state is described in terms of supramolecular chemistry and correlated with their solution-state properties. Finally, the types of receptors containing various functional groups are scrutinized in CO2 uptake, storage, and release processes and hints of endeavours for future research are delineated.

First High‐Resolution Vertical Profiles of Methane in the Troposphere Over India

AbstractMethane (CH4) is the second most abundant greenhouse gas and affects the Earth's radiative balance. In some regions, the methane burden and budget are still not well understood due to the lack of in situ observations, especially vertical profile observations. Here, we present the first high‐resolution aircraft‐based tropospheric vertical profiles of CH4 across the Indian subcontinent. Observations show significant variability, with the largest variability seen in the Indo‐Gangetic Plain (IGP) during post‐monsoon (September). The IGP also shows the highest concentrations and a peak in the boundary layer. By contrast, observations over western India show lower variability, especially during the Asian Summer Monsoon (ASM) (July). During ASM, when CH4 emissions peak, the vertical updraft of CH4 and other tracers is observed, leading to a peak between 4 and 5 km. During winter, the peak occurs in the boundary layer, and a decrease with altitude is observed. Model simulations slightly overestimate CH4 at the surface during some seasons but underestimate it at higher altitudes during all seasons. Integrated over the observed column, model simulations slightly underpredict CH4 (0.5%–3.1%) during all seasons. Calculations made using the observed CO/CH4 enhancement ratios show that in addition to anthropogenic fossil fuel emissions, other sources, such as rice cultivation and wetlands, need to be considered to reproduce the observed CH4 concentrations.

An explainable MHSA enabled deep architecture with dual-scale convolutions for methane source classification using remote sensing

Methane is the second most abundant greenhouse gas after carbon dioxide. Anthropogenic sources are the dominant emitters of methane. The poor spatial resolution of satellite imagery, high interclass similarity, the multi-scalar nature of features, and the dominance of background limit the performance of the previous approaches. Further, the reliance on high-resolution imagery limits the cost-effective global application of the works introduced in the literature. To resolve this, the present work proposes a novel method for methane source classification based on open-source multi-spectral satellite imagery of Sentinel-1 and 2. The work utilizes deep dual-scale convolutions with scaled dot product self-attention calculated across the 15 composite bands of Sentinel-1 and 2 data. The incorporation of non-RGB bands along with the RGB bands further enables the model to learn the spectral differences essential for the classification. The experimental results witness the superior performance of the developed method against other considered state-of-the-art methods.

Significant Increase in African Water Vapor over 2001–2020

Atmospheric water vapor is not only a key element of the global hydrological cycle but also the most abundant greenhouse gas. The phase transition and transportation of water vapor are essential for maintaining global energy balance and regulating hydrological processes. However, due to insufficient meteorological observational data, climate research in Africa faces significant limitations despite its substantial contribution to changes in global precipitable water vapor (PWV). In this study, we used MODIS near-infrared (NIR) PWV products and Berkeley temperature data to depict the spatial–temporal variability in PWV across Africa from 2001 to 2020. The results reveal a significant increasing trend in PWV over Africa, with an increase of 0.0158 cm/year. Nearly 99.96% of Africa shows an increase in PWV, with 88.95% of these areas experiencing statistically significant changes, particularly in central regions of Africa. The increase in PWV is more pronounced in high-value months compared to low-value months. The equatorial region of the Congo Basin exhibits higher PWV, which gradually decreases as latitude increases. Despite significant warming (0.0162 °C/year) in Africa, there is no consistent positive correlation between temperature and water vapor. A positive relationship between PWV and temperature is observed in western Africa, while a negative relationship is noted in eastern and southern Africa on an annual scale. Additionally, an increasing trend in precipitation (4.6669 mm/year) is observed, with a significant positive correlation between PWV and precipitation across most of Africa, although this relationship varies by month. These findings provide valuable insights into the comprehension of the hydrothermal variation in Africa amidst climate warming.

Metaheuristics-guided active learning for optimizing reaction conditions of high-performance methane conversion

Converting greenhouse gases into value-added chemical compounds has been widely studied in chemical science and engineering for sustainable industry. In particular, nonoxidative coupling of methane (NOCM) that transforms methane into useful chemical compounds has received great interests because methane is a naturally abundant greenhouse gas. NOCM has received significant attention in sustainable industry for addressing the climate crisis. However, there are complex optimization problems in maximizing the efficiency of the nonoxidative methane conversion. Although conventional data-driven methods have been successfully applied to various engineering problems, a data-driven optimization of NOCM remains a challenging problem because expensive chemical experiments should be performed to collect prior data for model training. To avoid the expensive costs of chemical experiments, we propose an active learning method that performs training data augmentation and model re-training without pre-defined unlabeled experimental data. To this end, we combine active learning with metaheuristic algorithms to perform active learning with statistically augmented data. We applied the proposed method to a high-throughput screening task to discover new reaction conditions of high-performance NOCM, and the high-throughput screening error was significantly reduced by 69.11%.

Opinion: New directions in atmospheric research offered by research infrastructures combined with open and data-intensive science

Abstract. The acquisition and dissemination of essential information for understanding global biogeochemical interactions between the atmosphere and ecosystems and how climate–ecosystem feedback loops may change atmospheric composition in the future comprise a fundamental prerequisite for societal resilience in the face of climate change. In particular, the detection of trends and seasonality in the abundance of greenhouse gases and short-lived climate-active atmospheric constituents is an important aspect of climate science. Therefore, easy and fast access to reliable, long-term, and high-quality observational environmental data is recognised as fundamental to research and the development of environmental forecasting and assessment services. In our opinion article, we discuss the potential role that environmental research infrastructures in Europe (ENVRI RIs) can play in the context of an integrated global observation system. In particular, we focus on the role of the atmosphere-centred research infrastructures ACTRIS (Aerosol, Clouds and Trace Gases Research Infrastructure), IAGOS (In-service Aircraft for a Global Observing System), and ICOS (Integrated Carbon Observation System), also referred to as ATMO-RIs, with their capabilities for standardised collection and provision of long-term and high-quality observational data, complemented by rich metadata. The ATMO-RIs provide data through open access and offer data interoperability across different research fields including all fields of environmental sciences and beyond. As a result of these capabilities in data collection and provision, we elaborate on the novel research opportunities in atmospheric sciences which arise from the combination of open-access and interoperable observational data, tools, and technologies offered by data-intensive science and the emerging collaboration platform ENVRI-Hub, hosted by the European Open Science Cloud (EOSC).

City-scale methane emissions from the midstream oil and gas industry: A satellite survey of the Zhoushan archipelago

As the second most abundant greenhouse gas after carbon dioxide, methane (CH4) leakage and emissions pose potential climate threats and environmental problems in the midstream oil and gas storage and transportation (OGST) industry. In this study, the Tropospheric Monitoring Instrument on the Sentinel-5P satellite was adopted to investigate the coverage of atmospheric CH4 concentrations over the Zhoushan archipelago, which is China's largest OGST base. Specifically, the distribution of atmospheric CH4 and nitrogen dioxide (NO2) concentrations over Zhoushan in August 2022 and August 2023 were evaluated, where changes in concentrations around the OGST companies were monitored. Using surface temperature inversion and average temperature statistics, we analyzed the heat island effect in Zhoushan. The results showed that: (1) although there is limited valid data (>200 pixel/day) for Zhoushan, the amount of valid data increased from 3.2% in January 2020 to 32.3% in August 2023; (2) around the region of the OGST companies, the atmospheric CH4 concentration shown significant variation at both the spatial and temporal scales, which may be due to the storage, loading, and unloading of petroleum products; (3) under geographic isolation conditions, NO2 can be used as a tracer gas to track the CH4 emissions from OGST companies; and (4) Surface temperature analysis underscores OGST companies' contribution to Zhoushan's urban heat island effect, with local average temperature increases outstripping broader national trends, highlighting the intricate interplay of human and natural factors in the city-scale climate dynamics.

Spatiotemporal investigation of near-surface CH4 and factors influencing CH4 over South, East, and Southeast Asia

Methane (CH4) is the second most abundant greenhouse gas after CO2, which plays the most important role in global and regional climate change. To explore the long-term spatiotemporal variations of near-surface CH4, datasets were extracted from Greenhouse gases Observing SATellite (GOSAT), and the Copernicus Atmospheric Monitoring Service (CAMS) reanalyzed datasets from June 2009 to September 2020 over South, East, and Southeast Asia. The accuracy of near-surface CH4 from GOSAT and CAMS was verified against surface observatory stations available in the study region to confirm both dataset applicability and results showed significant correlations. Temporal plots revealed continuous inflation in the near-surface CH4 with a significant seasonal and monthly variation in the study region. To explore the factors affecting near-surface CH4 distribution, near-surface CH4 relationship with anthropogenic emission, NDVI data, wind speed, temperature, precipitation, soil moisture, and relative humidity were investigated. The results showed a significant contribution of anthropogenic emissions with near-surface CH4. Regression and correlation analysis showed a significant positive correlation between NDVI data and near-surface CH4 from GOSAT and CAMS, while a significant negative correlation was found between wind and near-surface CH4. In the case of temperature, soil moisture, and near-surface CH4 from GOSAT and CAMS over high CH4 regions of the study area showed a significant positive correlation. However significant negative correlations were found between precipitation and relative humidity with GOSAT and CAMS datasets over high CH4 regions in South, East, and Southeast Asia. Moreover, these climatic factors showed no significant correlation within the low near-surface CH4 areas in our study region. Our study results showed that anthropogenic emissions, NDVI data, wind speed, temperature, precipitation, soil moisture, and humidity could significantly affect the near-surface CH4 over South, East, and Southeast Asia.

Benchmarking nitrous oxide adsorption and activation in metal–organic frameworks bearing coordinatively unsaturated metal centers

Several MOFs are evaluated as adsorbents of anthropogenic N2O emissions, the third most abundant greenhouse gas, through complimentary experimental and DFT analysis. N2O activation in M2(dobdc) MOFs is also studied.

Sustainable aviation fuel as a pathway to mitigate global warming in the aviation industry

The extensive utilization of fossil fuels by humanity has led to notable ecological degradation alongside a surge in productivity. The ensuing climate change, a result of global warming, poses a grave threat to human survival. A significant contributor to global warming is the emission of abundant greenhouse gases, with carbon dioxide being the most prevalent. Addressing global warming necessitates the identification and adoption of cleaner, alternative fuels to diminish carbon dioxide emissions. Sustainable Aviation Fuel (SAF) emerges as a prime alternative in this context. Chemically akin to conventional and fossil fuels, SAF originates from cleaner sources, offering a reduction in carbon dioxide emissions upon combustion. This paper highlights the importance of SAF as a viable strategy to mitigate CO2 emissions resulting from fossil fuel combustion. The paper also examines different SAF synthesis approaches, such as Fischer-Tropsch, Hydrogenated fatty acid esters and fatty acids (HEFA), and Alcohol-to-Jet (ATJ) processes. In summary, challenges such as high production costs, raw material price fluctuations, and the need for supportive policies hinder SAF's widespread adoption. To address climate change and reduce aviation emissions, further research, technological advancements, government incentives, and collaborative efforts within the aviation industry are crucial.

Simulating CH4 emissions from MSW landfills in China from 2003 to 2042 using IPCC and LandGEM models

Anaerobic landfills have long been the primary means of municipal solid waste (MSW) disposal in China. Landfills are the third largest emission source of methane (CH4), which is the second most abundant greenhouse gas in the atmosphere and has a high greenhouse effect. To date, there have been no reliable model predictions of long-term CH4 emissions from landfills in China. In this study, two general models, IPCC and LandGEM, were introduced to simulate CH4 emissions from all landfills in China. By comparing the results of the Shuangkou landfill in Tianjin with the default and local parameters, the local parameters were fixed to simulate landfill CH4 emissions in 31 regions over 40 years (2003–2042). The MSW landfills were obtained from statistical data for 18 years (2003–2020). The total emissions in China predicted by LandGEM and IPCC were 2.42 E+07 Mg and 2.36 E+07 Mg, respectively. These data provide a reliable reference for determining the long-term CH4 emissions from landfills in China.

BIOCATALYTIC CARBON DIOXIDE CAPTURE PROMOTED BY CARBONIC ANHYDRASE

The rapid and steady increase in the concentration of CO2, the most abundant greenhouse gas in the atmosphere, leads to extreme weather and climate events. Due to the burning of fossil fuels (oil, coal and natural gas), the concentration of CO2 in the air has been increasing in recent decades by more than 2 ppm per year, and in the last year alone - by 3.29 ppm. To prevent the "worst" scenarios of climate change, immediate and significant reductions in CO2 emissions through carbon management are needed. Aim. Analysis of the current state of research and prospects for the use of carbonic anhydrase in environmental decarbonization programs. Results. Carbonic anhydrase (CA) is an enzyme that accelerates the exchange of CO2 and HCO3 in solution by a factor of 104 to 106. To date, 7 types of CAs have been identified in different organisms. CA is required to provide a rapid supply of CO2 and HCO3 for various metabolic pathways in the body, explaining its multiple independent origins during evolution. Enzymes isolated from bacteria and mammalian tissues have been tested in CO2 sequestration projects using carbonic anhydrase (CA). The most studied is one of the isoforms of human KAz - hCAII - the most active natural enzyme. Its drawbacks have been instability over time, high sensitivity to temperature, low tolerance to contaminants such as sulphur compounds and the impossibility of reuse. Molecular modelling and enzyme immobilisation methods were used to overcome these limitations. Immobilisation was shown to provide greater thermal and storage stability and increased reusability. Conclusions. Capturing carbon dioxide using carbonic anhydrase (CA) is one of the most cost-effective methods to mitigate global warming, the development of which requires significant efforts to improve the stability and thermal stability of CAs.

Radiation-induced dry reforming: A negative emission process

The reaction between the most abundant greenhouse gases (GHG) to produce hydrogen might represent the most powerful and effective decarbonizing opportunity, if a low-carbon energy source is used to drive it. This is the case of methane (CH4) dry reforming (MDR) where its reaction with carbon dioxide (CO2) produces synthesis gas (syngas, a mixture of carbon monoxide and hydrogen). This study explores the feasibility of using ionizing radiation to induce the MDR reaction, at low temperatures and/or less energy demanding conditions. Additionally, the ionizing radiation is proposed to be supplied by nuclear power plants (NPPs), which are low-carbon reliable energy generation sources. Thus, the radiolysis of CO2, CH4 and their mixtures, under γ-irradiation was evaluated in the absence and presence of nickel catalysts. The radiation-induced MDR reaction and radiation-induced catalytic promotion were proven to take place at temperatures close to ambient though at low conversion, with yields below 1%. Since irradiation and heat can be provided by a nuclear power plant, this radiation-induced reaction establishes a connection between nuclear energy to renewable resources and enables a pathway for a decarbonized cleaner chemical industry, for producing green chemicals.

Stable isotope analysis of atmospheric CO2 using a Gasbench II-Cold Trap-IRMS setting.

The measurement of the stable carbon and oxygen isotope ratio of (atmospheric) carbon dioxide (CO2 ) is a useful technique for the investigation and identification of the sources and sinks of the most abundant greenhouse gases by far. For this reason, we are presenting a measuring system here that enables a wide range of users to carry out stable isotope analysis of atmospheric CO2 using off-the-bench hardware and software. The fully automated system uses cryogenic and gas chromatographic separation to analyse CO2 from 12-mL whole air samples and consists of an autosampler, a Gasbench II (GB), a downstream cryo trap and a continuous flow gas interface feeding into a sector field mass spectrometer (GC Pal/GB/Cold Trap/ConFlo IV/DeltaV Plus). The evaluation of the system performance was based on the analysis of samples prepared from eight CO2 sources (four CO2 reference gases and four artificial air tanks). The overall measurement uncertainty (averaged single standard deviation (1σ) of measurement replicates from each CO2 source) in the determination of the carbon and oxygen isotope ratio was 0.04‰ and 0.09‰ (n = 24). Furthermore, we were able to show that the measurement data also allowed for the quantification of the CO2 mole fraction, with a precision of 1.2μmol mol-1 in the analysis range of 400-500 μmol mol-1 . Our protocol provides a detailed description of the measurement set-up and the analysis procedure, how raw data should be evaluated and gives recommendations for sample preparation and sampling to enable a fully automated whole air sample analysis. The quantification limit of CO2 mole fractions and measurement precision for carbon and oxygen isotope ratios of CO2 should meet the requirements of a wide range of users.

Comparison of photoacoustic spectroscopy and cavity ring-down spectroscopy for ambient methane monitoring at Hohenpeißenberg

Abstract. With an atmospheric concentration of approximately 2000 parts per billion (ppbV, 10−9), methane (CH4) is the second most abundant greenhouse gas (GHG) in the atmosphere after carbon dioxide (CO2). The task of long-term and spatially resolved GHG monitoring to verify whether climate policy actions are effective is becoming more crucial as climate change progresses. In this paper we report the CH4 concentration readings of our photoacoustic (PA) sensor over a 5 d period at Hohenpeißenberg, Germany. As a reference device, a calibrated cavity ring-down spectrometer, Picarro G2301, from the meteorological observatory of the German Weather Service (DWD) was employed. Trace gas measurements with photoacoustic instruments promise to provide low detection limits at comparably low costs. However, PA devices are often susceptible to cross-sensitivities related to fluctuating environmental conditions, e.g. ambient humidity. The obtained results show that for PA sensor systems non-radiative relaxation effects induced by varying humidity are a non-negligible factor. Applying algorithm compensation techniques, which are capable of calculating the influence of non-radiative relaxation effects on the photoacoustic signal, increase the accuracy of the photoacoustic sensor significantly. With an average relative deviation of 1.11 % from the G2301, the photoacoustic sensor shows good agreement with the reference instrument.

A novel fusion framework embedded with zero-shot super-resolution and multivariate autoregression for precipitable water vapor across the continental Europe

Precipitable water vapor (PWV), as the most abundant greenhouse gas, significantly impacts the evapotranspiration process and thus the global climate. However, the applicability of mainstream satellite PWV products is limited by the tradeoff between spatial and temporal resolutions, as well as some external factors such as cloud contamination. In this study, we proposed a novel PWV spatio-temporal fusion framework based on the zero-shot super-resolution and the multivariate autoregression models (ZSSR-ARF) to improve the accuracy and continuity of PWV. The framework is implemented in a way that the satellite-derived observations (MOD05) are fused with the reanalysis data (ERA5) to generate accurate and seamless PWV of high spatio-temporal resolution (0.01°, daily) across the European continent from 2001 to 2021. Firstly, the ZSSR approach is used to enhance the spatial resolution of ERA5 PWV based on the internal recurrence of image information. Secondly, the optimal ERA5-MOD05 image pairs are selected based on the image similarity as inputs to improve the fusion accuracy. Thirdly, the framework develops a multivariate autoregressive fusion approach to allocate weights adaptively for the high-resolution image prediction, which primely addresses the non-stationarity and autocorrelation of PWV. The results reveal that the accuracies of fused PWV are consistent with those of the GPS retrievals (r = 0.82–0.95 and RMSE = 2.21–4.01 mm), showing an enhancement in the accuracy and continuity compared to the original MODIS PWV. The ZSSR-ARF fusion framework outperforms the other methods with R2 improved by over 24% and RMSE reduced by over 0.61 mm. Furthermore, the fused PWV exhibits similar temporal consistency (mean difference of 0.40 mm and DSTD of 3.22 mm) to the reliable ERA5 products, and substantial increasing trends (mean of 0.057 mm/year and over 0.1 mm/year near the southern and western coasts) are observed over the European continent. As the accuracy and continuity of PWV are improved, the outcome of this paper has potential for climatic analyses during the land-atmosphere cycle process.

Catalytic partial oxidation of methane over bimetallic Ru–Ni supported on CeO2 for syngas production

Methane (CH4) is the second most abundant greenhouse gas (GHG) after carbon dioxide (CO2) and is widely recognized as one of the most destructive GHGs, exerting negative impacts on the Earth's atmosphere. In order to effectively reduce CH4 emissions, the conversion of methane into synthesis gas, which is a mixture of hydrogen (H2) and carbon monoxide (CO), has emerged as an appealing approach. Recent advancements have demonstrated that catalytic partial oxidation of methane (POM) holds great promise as a reaction pathway for syngas production. Here we study a series of catalysts consisting of monometallic Ru and Ni, and bimetallic Ru–Ni supported on CeO2. These catalysts were synthesized using both mechanochemical and conventional incipient wetness impregnation methods. Various preparation parameters were investigated, including the Ru:Ni metal ratio, the order of metal addition, and the milling energy and time for the mechanochemically prepared samples. The catalysts were subjected to low-temperature POM (350–600 °C) experiments to evaluate their performance. The experimental results reveal that the bimetallic Ru–Ni/CeO2 catalysts exhibit superior catalytic activity and stability compared to the monometallic Ru–CeO2 and Ni–CeO2 catalysts. Notably, the bimetallic Ru–Ni/CeO2 catalysts prepared through ball milling demonstrate a significantly lower temperature requirement for syngas production compared to the conventional catalysts prepared via incipient wetness impregnation.

Fe-Promoted Alumina-Supported Ni Catalyst Stabilized by Zirconia for Methane Dry Reforming

The dry reforming of methane is a highly popular procedure since it can transform two of the most abundant greenhouse gases, methane and carbon dioxide, into useful syngases that can be further processed into valuable chemicals. To successfully achieve this conversion for the effective production of syngas, an optimal catalyst with advantageous physicochemical features must be developed. In this study, a variety of Ni-based catalysts supported by zirconia alumina (5Ni-10Zr + Al) were prepared by using the impregnation approach. Different loadings of Fe promoter were used, and the performances of the resulting catalysts in terms of activity and stability were investigated. The catalyst used in this study had an active metal component made of 5% Ni and x% Fe supported on 10ZrO2 + Al2O3, where x = (1, 2, 3, and 4). The physicochemical characteristics of both freshly calcined and used catalysts were studied using a range of characterization techniques, such as: N2 adsorption–desorption isotherms, XRD, H2-TPR, Raman spectroscopy, TGA, and TEM. An investigation of the effects of the Fe promoter on the catalytic activity of the catalyst (5Ni + xFe-10Zr + Al) was conducted. Amongst the studied catalysts, the 5Ni + 3Fe-10Zr + Al catalyst showed the best catalytic activity with CH4 and CO2 conversions of 87% and 90%, respectively, and had an H2/CO ratio of 0.98.

Machine Learning Model-Based Estimation of XCO2 with High Spatiotemporal Resolution in China

As the most abundant greenhouse gas in the atmosphere, CO2 has a significant impact on climate change. Therefore, the determination of the temporal and spatial distribution of CO2 is of great significance in climate research. However, existing CO2 monitoring methods have great limitations, and it is difficult to obtain large-scale monitoring data with high spatial resolution, thus limiting the effective monitoring of carbon sources and sinks. To obtain complete Chinese daily-scale CO2 information, we used OCO-2 XCO2 data, Carbon Tracker XCO2 data, and multivariate geographic data to build a model training data set, which was then combined with various machine learning models including Random Forest, Extreme Random Forest, XGBoost, LightGBM, and CatBoost. The results indicated that the Random Forest model presented the best performance, with a cross-validation R2 of 0.878 and RMSE of 1.123 ppm. According to the final estimation results, in terms of spatial distribution, the highest multi-year average RF XCO2 value was in East China (406.94 ± 0.65 ppm), while the lowest was in Northwest China (405.56 ± 1.43 ppm). In terms of time, from 2016 to 2018, the annual XCO2 in China continued to increase, but the growth rate showed a downward trend. In terms of seasonal effects, the multi-year average XCO2 was highest in spring (407.76 ± 1.72 ppm) and lowest in summer (403.15 ± 3.36ppm). Compared with the Carbon-Tracker data, the XCO2 data set constructed in this study showed more detailed spatial changes, thus, can be effectively used to identify potentially important carbon sources and sinks.

Modeling Adsorption of CO2 in Rutile Metallic Oxide Surfaces: Implications in CO2 Catalysis

CO2 is the most abundant greenhouse gas, and for this reason, it is the main target for finding solutions to climatic change. A strategy of environmental remediation is the transformation of CO2 to an aggregated value product to generate a carbon-neutral cycle. CO2 reduction is a great challenge because of the large C=O dissociation energy, ~179 kcal/mol. Heterogeneous photocatalysis is a strategy to address this issue, where the adsorption process is the fundamental step. The focus of this work is the role of adsorption in CO2 reduction by means of modeling the CO2 adsorption in rutile metallic oxides (TiO2, GeO2, SnO2, IrO2 and PbO2) using Density Functional Theory (DFT) and periodic DFT methods. The comparison of adsorption on different metal oxides forming the same type of crystal structure allowed us to observe the influence of the metal in the adsorption process. In the same way, we performed a comparison of the adsorption capability between two different surface planes, (001) and (110). Two CO2 configurations were observed, linear and folded: the folded conformations were observed in TiO2, GeO2 and SnO2, while the linear conformations were present in IrO2 and PbO2. The largest adsorption efficiency was displayed by the (001) surface planes. The CO2 linear and folded configurations were related to the interaction of the oxygen on the metallic surface with the adsorbate carbon, and the linear conformations were associated with the physisorption and folded configurations with chemisorption. TiO2 was the material with the best performance for CO2 interactions during the adsorption.