Search Results (133)

Photocatalysis is an emerging technology that harnesses light energy to facilitate chemical reactions. It has garnered considerable attention in the field of catalysis due to its promising applications in environmental remediation and sustainable energy generation. Recently, researchers have been exploring innovative techniques to improve the surface reactivity of ferroelectric materials for catalytic purposes, leveraging their distinct properties to enhance photocatalytic efficiency. With their switchable polarization and improved charge transport capabilities, ferroelectric materials show promise as effective photocatalysts for various reactions, including carbon dioxide (CO₂) reduction. Through a blend of experimental studies and theoretical modeling, researchers have shown that these materials can effectively convert CO₂ into valuable products, contributing to efforts to reduce greenhouse gas emissions and promote a cleaner environment. An artificial neural network (ANN) was employed to analyze parameter relationships and their impacts in this study, demonstrating its ability to manage training data errors and its applications in fields like speech and image recognition. This research also examined changes in charge separation, light absorption, and surface area related to variations in band gap and polarization, confirming prediction accuracy through linear regression analysis. Full article

Onboard the MetOp satellite series, Infrared Atmospheric Sounding Interferometer (IASI) is a Fourier Transform spectrometer based on the Michelson interferometer. IASI acquires interferograms, which are processed to provide high-resolution atmospheric emission spectra. These spectra enable the derivation of temperature and humidity profiles, among other parameters, with exceptional spectral resolution. In this study, we evaluate a novel, rapid retrieval approach in the interferogram domain, aiming for near-real-time (NRT) analysis of large spectral datasets anticipated from next-generation tropospheric sounders, such as MTG-IRS. The Partially Sampled Interferogram (PSI) method, applied to trace gas retrievals from IASI, has been sparsely explored. However, previous studies suggest its potential for high-accuracy retrievals of specific gases, including CO, CO₂, CH₄, and N₂O at the resolution of a single IASI footprint. This article presents the results of a study based on retrieval in the interferogram domain. Furthermore, the optical pathway differences sensitive to the parameters of interest are studied. Interferograms are generated using a fast Fourier transform on synthetic IASI spectra. Finally, the relationship to the total column of carbon monoxide is explored using three different algorithms—from the most intuitive to a complex neural network approach. These algorithms serve as a proof of concept for interferogram classification and rapid predictions of surface temperature, as well as the abundances of H₂O and CO. IASI spectra simulations were performed using the LATMOS Atmospheric Retrieval Algorithm (LARA), a robust and validated radiative transfer model based on least squares estimation. The climatological library TIGR was employed to generate IASI interferograms from LARA spectra. TIGR includes 2311 atmospheric scenarios, each characterized by temperature, water vapor, and ozone concentration profiles across a pressure grid from the surface to the top of the atmosphere. Our study focuses on CO, a critical trace gas for understanding air quality and climate forcing, which displays a characteristic absorption pattern in the 2050–2350 ${\mathrm{cm}}^{-1}$ wavenumber range. Additionally, the study explores the potential of correlating interferogram characteristics with surface temperature and H₂O content, aiming to enhance the accuracy of CO column retrievals. Starting with intuitive retrieval algorithms, we progressively increased complexity, culminating in a neural network-based algorithm. The results of the NN study demonstrate the feasibility of fast interferogram-domain retrievals, paving the way for operational applications. Full article

Carbon dioxide (CO₂) is the primary component contributing to anthropogenic greenhouse gas emissions, necessitating the adoption of effective mitigation strategies to promote environmental sustainability. Among the various carbon capture methodologies, chemical absorption is acknowledged as the most scalable solution for post-combustion applications. This investigation presents a thorough, comparative, and scenario-based evaluation of both singular and blended amine solvents for CO₂ capture within packed absorption–desorption columns. A validated rate-based model employing monoethanolamine (MEA) functions as the benchmark for executing process simulations. Three sequential scenarios are meticulously examined to switch the solvents and see the results. In the preliminary scenario, baseline performance is assessed by applying MEA to achieve the designated 73% removal target. Then the implementation of alternative solvents is examined—piperazine (PZ), a combination of methyldiethanolamine (MDEA) and PZ, and a blend of MEA and PZ—under uniform design parameters to ascertain their relative effectiveness and performance. In the second scenario, the design of the system is changed to reach a CO₂ removal efficiency for MEA of 90%, and then MEA is switched to other solvents. In the final scenario, critical design parameters, including column height and diameter, are adjusted for each solvent system that did not meet the 90% capture efficiency in Scenario 2 to achieve 90% CO₂ capture. A comprehensive sensitivity analysis is subsequently conducted on the adjusted systems to evaluate the influence of critical operational variables such as temperature, flue gas and solvent flow rates, and concentrations. Importantly, the MEA + PZ blend also demonstrated the lowest specific reboiler duty, as low as 4.28 MJ/kg CO₂, highlighting its superior energy efficiency compared to other solvents in the condition that the system in this study is pilot-scale, not commercial-scale, and due to this reason, the energy consumption of the system is slightly higher than the reported value for the commercial-scale systems. The results yield invaluable insights into the performance trade-offs between singular and blended amines, thereby facilitating the development of more efficient CO₂ capture systems that function within practical constraints. Full article

Although digitalization offers new pathways for carbon reduction, its underlying mechanisms have not been fully explored. Unlike previous studies, this research investigates the impact of digitalization on corporate carbon performance through both technological and structural effects while also revealing the boundary conditions under which digitalization contributes to carbon reduction in the context of corporate financing constraints. We conducted an empirical analysis using a fixed-effects model and a partially linear functional-coefficient model based on data from A-share listed companyies in China from 2008 to 2023. The results show that digitalization is significantly and positively associated with corporate carbon performance, confirming its potential for emission reduction. Mechanism tests indicated that digitalization improves corporate carbon performance by enhancing technological absorptive capacity, promoting factor substitution, and optimizing resource allocation. Further analysis revealed that, under financing constraints, the marginal effect of digitalization on corporate carbon performance follows an “inverted U-shaped” curve. Our study enriches the literature on the digital economy and carbon emissions and provides both theoretical and practical insights for promoting the coordinated transformation of enterprises toward digitalization and low-carbon development. Full article

In line with the goals of “peak carbon emissions and carbon neutrality”, this study aims to develop a market-coordinated operation mechanism to promote renewable energy adoption and consumption, addressing the challenges of integrating medium- and long-term trading with spot markets in power systems with high renewable energy penetration. A three-stage joint operation framework is proposed. First, a medium- and long-term trading game model is established, considering multiple energy types to optimize the benefits of market participants. Second, machine learning algorithms are employed to predict renewable energy output, and a contract decomposition mechanism is developed to ensure a smooth transition from medium- and long-term contracts to real-time market operations. Finally, a day-ahead market-clearing strategy and an incentive-compatible settlement mechanism, incorporating the constraints from contract decomposition, are proposed to link the two markets effectively. Simulation results demonstrate that the proposed mechanism effectively enhances resource allocation and stabilizes market operations, leading to significant revenue improvements across various generation units and increased renewable energy utilization. Specifically, thermal power units achieve a 19.12% increase in revenue, while wind and photovoltaic units show more substantial gains of 38.76% and 47.52%, respectively. Concurrently, the mechanism drives a 10.61% increase in renewable energy absorption capacity and yields a 13.47% improvement in Tradable Green Certificate (TGC) utilization efficiency, confirming its overall effectiveness. This research shows that coordinated optimization between medium- and long-term/spot markets, combined with a well-designed settlement mechanism, significantly strengthens the market competitiveness of renewable energy, providing theoretical support for the market-based operation of the new power system. Full article

With the advancement of integrated energy systems (IES) and the increasing penetration of variable renewable energy, IES confronts complex uncertainties that necessitate enhanced flexibility. Therefore, this study focuses on improving IES flexibility. To this end, multi-dimensional flexibility evaluation indexes for the “Source–Structure–Demand” dimensions were established, and a multi-objective optimization model considering flexibility and source–demand side uncertainties was developed. The flexibility evaluation indexes include the Grid Dependency Level (GDL) for the source side, Insufficient Flexible Resource Probability (IFRP) for the structure side, and Loss of Load Probability (LOLP) for the demand side. Moreover, considering the distinct adjustment response times and inertia of different energy flows during IES operation, thermal and electrical energy are optimized on separate time scales. Thus, the multi-objective optimization constitutes a multi-time scale, high-dimensional, non-convex nonlinear model targeting economy, flexibility, security, and low carbon emissions. This paper employs single-economy objective, single-flexibility objective, and multi-objective optimization to analyze IES configuration, operation, risk, carbon emissions, and flexibility. The results indicate that poor flexibility leads to high operational risk, while excessive pursuit of flexibility incurs high costs and destabilizes operations. By implementing this multi-objective optimization, IES flexibility is enhanced while ensuring system economic performance. It also addresses the flexibility deficiency in traditional single-economy objective optimizations. Additionally, the system increases the renewable energy absorption rate by approximately 10%. Full article

The main objective of this study is to improve heat transfer in hydrocarbon- and geothermal-energy coproduction systems using carbon quantum dots (CQDs). Two types of 0D nanoparticles (synthesized and commercial CQDs) were used for the formulation of nanofluids to increase the heat transfer from depleted wells for the coproduction of oil and electrical energy. The synthesized and commercial CQDs were characterized in terms of their morphology, zeta potential, density, size, and heat capacity. The nanofluids were prepared using brine from an oil well of interest and two types of CQDs. The effect of the CQDs on the thermophysical properties of the nanofluids was evaluated based on their thermal conductivity. In addition, a mathematical model based on heat transfer principles to predict the effect of nanofluids on the efficiency of the organic Rankine cycle (ORC) was implemented. The synthesized and commercial CQDs had particle sizes of 25 and 16 nm, respectively. Similarly, zeta potential values of 36 and 48 mV were obtained. Both CQDs have similar functional groups and UV absorption, and the fluorescence spectra show that the study CQDs have a maximum excitation–emission signal around 360–460 nm. The characterization of the nanofluids showed that the addition of 100, 300, and 500 mg/L of CQDs increased the thermal conductivity by 40, 50, and 60 %, respectively. However, the 1000 mg/L incorporated decreased the thermal conductivities of the nanofluids. The observed behavior can be attributed to the aggregate size of the nanoparticles. Furthermore, a new thermal conductivity model for CQD-based nanofluids was developed considering brine salinity, particle size distribution, and agglomeration effects. The model showed a remarkable fit with the experimental data and predicted the effect of the nanofluid concentration on the thermal conductivity and cycle efficiency. Coupled with an ORC cycle model, CQD concentrations of approximately 550 mg/L increased the cycle efficiency by approximately 13.8% and 18.6% for commercial and synthesized CQDs, respectively. Full article

Carbon dioxide, the primary greenhouse gas responsible for global warming, represents today a critical environmental challenge for humans. Mitigating CO₂ emissions and other greenhouse gases is a pressing global concern. The primary goal of this study is to investigate the potential of particular ionic liquids (ILs) in capturing CO₂ for the sweetening of natural and other gases. The solubility of CO₂ was measured in three distinct ILs, which shared a common anion (triflate, TfO) but differed in their cations. The selected ionic liquids were {1-butyl-3-methylimidazolium triflate [BMIM][TfO], 1-butyl-1-methylpyrrolidinium triflate [BMP][TfO], and 1-butyl-4-methylpyridium triflate [MBPY][TfO]}. The solvents were screened based on results from a molecular computational study that predicted low CO₂ Henry’s Law constants. Solubility measurements were conducted at 303.15 K, 323.15 K, and 343.15 K and pressures up to 1.5 MPa using a gravimetric microbalance (IGA-003). The CO₂ experimental results were modeled using the Peng–Robinson Equation of state with three mixing rules: van der Waals one (vdWI), van der Waals two (vdWII), and the non-random two-liquid (NRTL) Wong–Sandler (WS) mixing rule. For the three ILs, the NRTL-WS mixing rule regressed the data with the lowest average deviation percentage of 1.24%. The three solvents had similar alkyl chains but slightly different polarities. [MBPY][TfO], with the largest size, exhibited the highest CO₂ solubility at all three temperatures. Calculation of its relative polarity descriptor (N) shows it was the least polar of the three ILs. Conversely, [BMP][TfO] showed the highest Henry’s Law constant (lowest solubility) across the studied temperature range. Comparing the results to published data, the study concludes that triflate-based ionic liquids with three fluorine atoms had lower capacity for CO₂ compared to bis(trifluoromethylsulfonyl) imide (Tf2N)-based ionic liquids with six fluorine atoms. Additionally, the study provided data on the enthalpy and entropy of absorption. A final comparison shows that the ILs had a lower CO₂ capacity than Selexol, a solvent widely used in commercial carbon capture operations. Compared to other ILs, the results confirm that the type of anion had a more significant impact on solubility than the cation. Full article

Against the backdrop of the steady advancement of the national rural revitalization strategy and the dual-carbon goals, the low-carbon transformation of rural energy systems is of critical importance. This study first proposes a comprehensive architecture for rural energy supply systems, incorporating four key dimensions: investment, system configuration, user demand, and policy support. Leveraging the abundant wind, solar, and biomass resources available in rural areas, a low-carbon optimization model for rural energy system operation is developed. The model accounts for diverse load characteristics and the integration of elevated water towers, which serve both energy storage and agricultural functions. The optimization framework targets the multi-energy demands of rural production and daily life—including electricity, heating, cooling, and gas—and incorporates the stochastic nature of wind and solar generation. To address renewable energy uncertainty, the Fisher optimal segmentation method is employed to extract representative scenarios. A representative rural region in China is used as the case study, and the system’s performance is evaluated across multiple scenarios using the Gurobi solver. The objective functions include maximizing clean energy benefits and minimizing carbon emissions. Within the system, flexible resources participate in demand response based on their specific response characteristics, thereby enhancing the overall decarbonization level. The energy storage aggregator improves renewable energy utilization and gains economic returns by charging and discharging surplus wind and solar power. The elevated water tower contributes to renewable energy absorption by storing and releasing water, while also supporting irrigation via a drip system. The simulation results demonstrate that the proposed clean energy system and its associated operational strategy significantly enhance the low-carbon performance of rural energy consumption while improving the economic efficiency of the energy system. Full article

Background: The design of multifunctional carbon based nanosystems exhibiting light-triggered hyperthermia, emission, low cytotoxicity, and drug delivery capability is of significant interest in the area of nanomaterials. In this study, we present red-emitting and photothermal carbon nanodots (Cdots-βCD/PTC) obtained by the encapsulation of hydrophobic pentacene (PTC) within Carbon nanodots (Cdots) synthesized from beta-cyclodextrin (βCD). Methods: The prepared nanostructures were investigated in terms of morphology, size, and optical properties, by absorption and emission optical spectroscopy, atomic force microscopy, dynamics light scattering, Z-potential, nuclear magnetic resonance, and infra-red spectroscopy. Molecular modelling simulation was used to investigate the geometry and the stabilization energy of the Cdots-βCD/PTC inclusion complex. Results: The as prepared Cdots-βCD/PTC demonstrated good water dispersibility, green-emission (ϕ_PL = 1.7%), and photothermal conversion (η = 17.4%) upon red-light excitation (680 nm). Furthermore, Cdots-βCD/PTC low cytotoxicity in the range 0.008 μg–0.8 μg and good interaction with albumin protein (K_SV = 2.78 ± 0.28 mL mg⁻¹) were demonstrated. Molecular simulation analysis revealed the formation of the inclusion complex with an energy of −5.32 kcal mol⁻¹, where PTC is orthogonally oriented in the βCD cavity. Conclusions: The results presented in this work highlight the potential of Cdots-βCD/PTC as a novel versatile nanosystem for biomedical applications, such as bioimaging and site-specific photothermal treatment of cancer cells. Full article

The challenges of limited cropland resources and ecological degradation in grain-producing areas were addressed in this study within the broader context of China’s ecological civilization and dual carbon goals. An integrated framework was employed, applying the three-dimensional ecological footprint (EF_3d) model, enhanced by carbon footprint improvement, to assess cropland at the provincial, municipal, and county levels. The analysis indicated a rise in both carbon absorption and emissions, resulting in a carbon surplus. Since 1984, chemical fertilizers have been identified as the predominant source of carbon emissions. Carbon absorption was found to vary distinctly among the four crops. Additionally, carbon fluxes displayed notable spatial and temporal variability. The ecological deficit persisted, showing distinct spatial clustering. Moreover, the cropland ecological footprint breadth (EF_size) was found to exhibit a pattern of decrease–increase–decrease, while cropland occupation remained high. The ecological footprint depth (EF_depth) consistently surpassed the threshold of 1. Spatially, the distribution pattern of cropland EF_size was opposite to that of EF_depth; the centroid of per capita cropland EF_depth underwent a significant spatial shift. The cropland EF_3d was observed to experience a downward trend, with considerable regional disparities. Furthermore, unsustainable use of cropland was observed across multiple scales. This research provides an empirical foundation for promoting advancing ecological agriculture and sustainable cropland use practices. Full article

Emissions caused by polluting gases, such as carbon dioxide, are one of the main contributors to the generation of the greenhouse effect that leads to global warming, responsible for climate change. An alternative to mitigating these emissions is the use of adsorbents capable of capturing CO₂. Zeolites are considered one of the most effective adsorbents in gas adsorption and separation technologies due to their high specific area and pore size and, consequently, greater adsorption capacity when compared to other commonly used materials. Despite this, reagents used in syntheses as the source of silica often make obtaining these materials more expensive. Seeking to overcome this limitation, in this work, materials (for CO₂ capture) were developed with a zeolitic structure using a low-cost alternative source of silica from beach sand called MPI silica to make the synthesis process eco-friendly. The crystallization time of the materials was studied, obtaining an LTA zeolite with MPI silica in a period of 1 h (ZAM 1 h), with a relative crystallinity of 74.26%. The materials obtained were characterized using the techniques of X-ray diffraction (XRD), X-ray fluorescence (XRF), absorption spectroscopy in the infrared region with Fourier transform (FTIR), scanning electron microscopy (SEM), and thermal analysis. The evaluation of the experimental adsorption isotherms showed that the zeolite LTA Aerosil^®200 (standard zeolite) and MP had adsorption capacities of 5.25 mmol/g and 4.83 mmol/g of CO₂, respectively. The evaluation of mathematical models indicated that the LTA zeolites fit the Temkin model best and had the same trend, with calculated adsorption capacities of 3.97 mmol/g and 3.75 mmol/g, respectively. Full article

Global climate change and changes in land use structures during rapid urbanization have profoundly impacted ecosystem carbon storage. Previous studies have not combined different climate scenarios and land use patterns to predict carbon storage. Using scenarios from both the InVEST-PLUS model and SSP-RCP, combined with multi-source remote sensing data, this study takes the Lijiang River Basin as the study area to explore the dynamic changes in land use and carbon storage under different climate scenarios. The findings are as follows: (1) From 2000 to 2020, cultivated and construction land increased, while forest land significantly decreased, lowering from 4331.404 km² to 4111.936 km². This land use change mainly manifests in the significant transformation of forest land into cultivated and construction lands. Under different climate scenarios, the cultivated and construction lands will continue to expand, the forest land will decrease, and the grassland area will increase. (2) Total carbon storage decreased significantly from 2000 to 2020, with forest carbon storage changing the most significantly, for a total reduction of 5,540,612.13 tons, followed by grassland and water area. Regardless of the future scenario, the total carbon storage in the Lijiang River Basin will experience a decreasing trend; the decline in carbon reserves is most significant in the SSP585 scenario and smallest in the SSP126 scenario, with slight increases even appearing in some regions. (3) From the perspective of land use change, the large-scale expansion of construction land in the process of rapid urbanization has occupied a large amount of ecological land, such as forests and grasslands, and this is the main reason for the reduction in total carbon storage in the basin. From the perspective of climate change scenarios, a global temperature increase caused by a high-emission scenario (SSP585) may exceed the optimal growth temperature for some plants, inhibit the carbon absorption capacity of vegetation, and thus reduce the carbon fixation capacity of forest land and grassland. Therefore, to maintain long-term climate goals and sustainable development, the SSP126 scenario should be prioritized to strengthen the protection of forest resources in the northern and central regions of the Lijiang River Basin, balance the relationship between ecological protection and urbanization, avoid the occupation of ecological land by excessive urbanization, and improve the carbon sink potential of the basin. These research results can provide a scientific basis for the optimization of land spatial patterns, ecological restoration and protection, and the enhancement of carbon sink potential in the Lijiang River Basin under the “double carbon” goal. Full article

Global CO₂ concentrations continue to rise despite significant efforts to decarbonize and reduce greenhouse gas emissions. This paper examines the role of sustainable business in reducing and limiting global CO₂ concentrations based on daily CO₂ data from the Mauna Loa Observatory. Based on the theory of the carbon cycle, factors considered significant in determining global CO₂ concentrations include emissions, affected by economic variables like the crude oil price and Dow Jones Sustainability Index but also absorption capacity, affected through biomass growth by astronomical variables such as total solar irradiance and cosmic rays. Considering pair-wise correlations between variables, particular attention is drawn to the fact that in the COVID-19 pandemic, when everyone was working from home, cars were not allowed on the roads, and planes were not flying, the correlation between the Dow Jones Sustainability Index and the global CO₂ concentration was negative. The article tests the hypothesis that business can be a force for good and make a meaningful contribution towards reducing global CO₂ concentrations. To this end, it offers an integrated model of global CO₂ concentrations built according to the theory of the carbon cycle based on 2195 daily observations, including all the variables outlined above. The results confirm the hypothesis that business, expressed in the form of Dow Jones Sustainability Index, can play a role in reducing the global CO₂ concentrations. A range of policy conclusions is drawn. Full article

Urbanization has greatly altered Earth’s surface form, and land use changes can lead to significant changes in carbon emissions. However, how these changes affect ecosystems remains unclear. Therefore, this study calculated the carbon absorption and emissions in 31 Chinese provinces using high-resolution (300 m) land use data. Subsequently, a carbon flow model was used to evaluate the carbon transfer that occurred from the changes in land use in every province between 2000 and 2020. The standard deviation ellipse analytic techniques were also employed to research the spatiotemporal evolution features of carbon flow in various economic zones. Furthermore, the flux and utility analysis approaches in ecological network analysis were used to quantitatively examine the interaction relationship between two carbon metabolism land uses. The results revealed that the continuous expansion of China’s construction land has reduced the area of agricultural land, resulting in industrial land (53.14%) and urban land (39.38%) being the main contributors to the total carbon emissions. Among them, the five eastern provinces of Hebei, Jiangsu, Zhejiang, Shandong, and Guangdong had carbon emissions of more than 100 million tons. From 2000 to 2020, the center of gravity of the carbon flow in construction land had shifted significantly from Henan Province to Gansu Province. The ecological relationship of exploitation and control dominated the two land use types. It is mostly found in Xinjiang, Qinghai, Gansu, Inner Mongolia, and Ningxia provinces. The findings could provide relevant policy implications for the Chinese government to mitigate carbon metabolism on land. Full article