Search Results (1,119)

Carbon capture technologies are largely considered to play a crucial role in meeting the climate change and global warming target set by Net Zero Emission (NZE) 2050. These technologies can contribute to clean energy transitions and emissions reduction by decarbonizing the power sector and other CO₂ intensive industries such as iron and steel production, natural gas processing oil refining and cement production where there is no obvious alternative to carbon capture technologies. While the progress of carbon capture technologies has fallen behind expectations in the past, in recent years there has been substantial growth in this area, with over 700 projects at various stages of development. Moreover, there are around 45 commercial carbon capture facilities already in operation around the world in different industrial processes, fuel transformation and power generation. Carbon capture technologies including pre/post-combustion, oxyfuel and chemical looping combustion have been widely exploited in the recent years at different Technology Readiness level (TRL). Although, a large number of review studies are available addressing different carbon capture strategies, however, studies related to the commercial status of the carbon capture technologies are yet to be conducted. In this review article, we summarize the state-of-the-art of different carbon capture technologies applied to different emission sources, focusing on emission reduction, net-zero emission, and negative emission. We also highlight the commercial status of the different carbon capture technologies including economics, opportunities, and challenges. Full article

Coastal blue carbon ecosystems (traditional types such as mangroves, salt marshes, and seagrass meadows; emerging types such as tidal flats and mariculture) play pivotal roles in capturing and storing atmospheric carbon dioxide. Reliable assessment of the spatial and temporal variation and the carbon storage potential holds immense promise for mitigating climate change. Although previous field surveys and regional assessments have improved the understanding of individual habitats, most studies remain site-specific and short-term; comprehensive, multi-decadal assessments that integrate all major coastal blue carbon systems at the national scale are still scarce for China. In this study, we integrated 30 m Landsat imagery (1992–2022), processed on Google Earth Engine with a random forest classifier; province-specific, literature-derived carbon density data with quantified uncertainty (mean ± standard deviation); and the InVEST model to track coastal China’s mangroves, salt marshes, tidal flats, and mariculture to quantify their associated carbon stocks. Then the GeoDetector was applied to distinguish the natural and anthropogenic drivers of carbon stock change. Results showed rapid and divergent land use change over the past three decades, with mariculture expanded by 44%, becoming the dominant blue carbon land use; whereas tidal flats declined by 39%, mangroves and salt marshes exhibited fluctuating upward trends. National blue carbon stock rose markedly from 74 Mt C in 1992 to 194 Mt C in 2022, with Liaoning, Shandong, and Fujian holding the largest provincial stock; Jiangsu and Guangdong showed higher increasing trends. The Normalized Difference Vegetation Index (NDVI) was the primary driver of spatial variability in carbon stock change (q = 0.63), followed by precipitation and temperature. Synergistic interactions were also detected, e.g., NDVI and precipitation, enhancing the effects beyond those of single factors, which indicates that a wetter climate may boost NDVI’s carbon sequestration. These findings highlight the urgency of strengthening ecological red lines, scaling climate-smart restoration of mangroves and salt marshes, and promoting low-impact mariculture. Our workflow and driver diagnostics provide a transferable template for blue carbon monitoring and evidence-based coastal management frameworks. Full article

Accurate regional-scale mapping of soil organic matter (SOM) is crucial for land productivity management and global carbon pool monitoring. Current remote sensing inversion of SOM faces challenges, including the underutilization of temporal information and low feature selection efficiency. To address these limitations, this study developed an integrated framework combining multi-temporal Landsat imagery, field-measured SOM data, intelligent feature optimization, and machine learning. The framework employs two novel image-processing strategies: the Maximum Annual Bare-Soil Composite (MABSC) method to extract background spectral information and the Multi-temporal Feature Optimization Composite (MFOC) method to capture seasonal and environmental dynamics. These features, along with topographic covariates, were processed using an improved Feature-Optimized and Interpretable XGBoost (FOI-XGB) model for key variable selection and spatial mapping. Validation across two subtropical coastal mountainous regions at different scales in southeastern China demonstrated the framework’s effectiveness and robustness. Key findings include the following: (1) Both the MABSC-derived spectral bands and the MFOC-optimized indices significantly outperformed traditional single-season approaches. Their combined use achieved a moderate SOM inversion accuracy (R² = 0.42–0.44). (2) The FOI-XGB model substantially outperformed traditional feature selection methods (Pearson, SHAP, and CorrSHAP), achieving significant regional R² improvements ranging from 9.72% to 88.89%. (3) The optimal model integrating the MABSC-derived features, MFOC-optimized indices, and topographic covariates attained the highest accuracy (R² up to 0.51). This represents major improvements compared with using topographic covariates alone (R² increase of up to 160.11%) or the combined spectral features (MABSC + MFOC) alone (R² increase of up to 15.91%). This study provides a robust, scalable, and practical technical solution for accurate SOM mapping in complex environments, with significant implications for sustainable land management and carbon monitoring. Full article

Capturing CO₂ emitted by coal chemical enterprises and injecting it into oil reservoirs not only effectively improves the recovery rate and development efficiency of tight oil reservoirs in the Ordos Basin but also addresses the carbon emission problem constraining the development of the region. Since initiating field experiments in 2012, the Ordos Basin has become a significant base for CCUS (Carbon capture, Utilization, and Storage) technology application and demonstration in China. However, over the years, projects have primarily focused on enhancing the recovery rate of CO₂ flooding, while issues such as potential reservoir damage and its extent have received insufficient attention. This oversight hinder the long-term development and promotion of CO₂ flooding technology in the region. Experimental results were comprehensively analyzed using techniques including nuclear magnetic resonance (NMR), X-ray diffraction (XRD), scanning electron microscopy (SEM), inductively coupled plasma (ICP), and ion chromography (IG). The findings indicate that under current reservoir temperature and pressure conditions, significant asphaltene deposition and calcium carbonate precipitation do not occur during CO₂ flooding. The reservoir’s characteristics-high feldspar content, low carbon mineral content, and low clay mineral content determine that the primary mechanism affecting physical properties under CO₂ flooding in the Chang 4 + 5 tight sandstone reservoir is not, as traditional understand, carbon mineral dissolution or primary clay mineral expansion and migration. Instead, feldspar corrosion and secondary particles migration are the fundamental reasons for the changes in reservoir properties. As permeability increases, micro pore blockage decreases, and the damaging effect of CO₂ flooding on reservoir permeability diminishes. Permeability and micro pore structure are therefore significant factors determining the damage degree of CO₂ flooding inflicts on tight reservoirs. In addition, temperature and pressure have a significant impact on the extent of reservoir damage caused by CO₂ flooding in the study region. At a given reservoir temperature, increasing CO₂ injection pressure can mitigate reservoir damage. It is recommended to avoid conducting CO₂ flooding projects in reservoirs with severe pressure attenuation, low permeability, and narrow pore throats as much as possible to prevent serious damage to the reservoir. At the same time, the production pressure difference should be reasonably controlled during the production process to reduce the risk and degree of calcium carbonate precipitation near oil production wells. Full article

Groundwater at petroleum-contaminated sites typically exhibits elevated dissolved inorganic carbon (DIC) levels due to hydrocarbon biodegradation; however, our prior field investigations revealed an enigmatic DIC depletion anomaly that starkly contradicts this global pattern and points to an unrecognized carbon sink. In a breakthrough demonstration, this study provides the first experimental confirmation that sulfur-oxidizing bacteria (SOB) drive substantial carbon sequestration via a coupled sulfur oxidation autotrophic assimilation process. Through integrated hydrochemical monitoring and 16S rRNA sequencing in an enrichment culture system, we captured the complete DIC transformation trajectory: heterotrophic acetate degradation initially increased DIC to 370 mg/L, but subsequent autotrophic assimilation by SOB dramatically reduced DIC to 270 mg/L, yielding a net consumption of 85 mg/L. The distinctive pH dynamics (initial alkalization followed by acidification) further corroborated microbial regulation of carbon cycling. Critically, Pseudomonas stutzeri and P. alcaliphila were identified as the dominant carbon-fixing agents. These findings definitively establish that chemolithoautotrophic SOB convert DIC into organic carbon through a “sulfur oxidation-carbon fixation” coupling mechanism, overturning the conventional paradigm of petroleum-contaminated sites as perpetual carbon sources. The study fundamentally redefines natural attenuation frameworks by introducing microbial carbon sink potential as an essential assessment metric for environmental sustainability. Full article

The construction sector makes a major contribution to global greenhouse gas emissions, in which cement alone produces approximately 7–8% of global CO₂ emissions. To abate environmental impact and promote sustainable construction, alternative low-carbon cementitious materials are gaining attention. Biochar (BC), a carbon-rich material obtained from biomass sources through the process of pyrolysis, has surfaced as a capable supplementary cementitious material due to its carbon capture capabilities and positive impact on the characteristics of cement composites. This review investigates the role of BC in cement composites, including its effects on hydration kinetics, microstructural development, fresh-state properties, and its optimal utilisation. The study also highlights the internal curing capabilities of BC when used in cement composites, its role in promoting hydration product formation, and its dual function in enhancing mechanical performance while facilitating carbon capture. Despite the benefits, there are some challenges such as variable BC properties, optimal dosage, and scalability. The review highlights the need for standardisation and further research to fully harness BC’s potential as a sustainable component in low-carbon construction technologies. Full article

This study introduces an intrinsic Gaussian Process Regression (iGPR) model for the first time, which incorporates non-Euclidean spatial covariates via a Gaussian process prior to analyzing the relationship between digitalization and carbon emission efficiency. The iGPR model’s hierarchical design embeds a Gaussian process as a flexible spatial random effect with a heat-kernel-based covariance function to capture the manifold geometry of spatial features. To enable tractable inference, we employ a penalized maximum-likelihood estimation (PMLE) approach to jointly estimate regression coefficients and covariance hyperparameters. Using a panel dataset linking a national digitalization (modernization) index to carbon emission efficiency, the empirical analysis demonstrates that digitalization has a significantly positive impact on carbon emission efficiency while accounting for spatial heterogeneity. The iGPR model also exhibits superior predictive accuracy compared to state-of-the-art machine learning methods (including XGBoost, random forest, support vector regression, ElasticNet, and a standard Gaussian process regression), achieving the lowest mean squared error (MSE = 0.0047) and an average prediction error near zero. Robustness checks include instrumental-variable GMM estimation to address potential endogeneity across the efficiency distribution and confirm the stability of the estimated positive effect of digitalization. Full article

Hybrid materials based on porous organic polymers (POPs) and metal–organic frameworks (MOFs) are increasing attention for advanced separation processes due to the possibility to combine their properties. POPs provide high surface areas, chemical stability, and tunable porosity, while MOFs contribute a high variety of defined crystalline structures and enhanced separation characteristics. The combination (or hybridization) with PIMs gives rise to mixed-matrix membranes (MMMs) with improved permeability, selectivity, and long-term stability. However, interfacial compatibility remains a key limitation, often addressed through polymer functionalization or controlled dispersion of the MOF phase. MOF/COF hybrids are more used as biochemical sensors with elevated sensitivity, catalytic applications, and wastewater remediation. They are also very well known in the gas sorption and separation field, due to their tunable porosity and high electrical conductivity, which also makes them feasible for energy storage applications. Last but not less important, hybrids with other POPs, such as hyper-crosslinked polymers (HCPs), covalent triazine frameworks (CTFs), or conjugated microporous polymers (CMPs), offer enhanced functionality. MOF/HCP hybrids combine ease of synthesis and chemical robustness with tunable porosity. MOF/CTF hybrids provide superior thermal and chemical stability under harsh conditions, while MOF/CMP hybrids introduce π-conjugation for enhanced conductivity and photocatalytic activity. These and other findings confirm the potential of MOF-POP hybrids as next-generation materials for gas separation and carbon capture applications. Full article

Amidst global imperatives for sustainable energy and environmental remediation, carbon aerogels (CAs) present a transformative alternative to conventional carbon materials (e.g., activated carbon, carbon fibers), overcoming limitations of disordered pore structures, unmodifiable surface chemistry, and functional inflexibility. This review systematically examines CA-based electrochemical systems as its primary focus, analyzing fundamental charge-storage mechanisms and establishing structure–property–application relationships critical to energy storage performance. We critically assess synthesis methodologies, emphasizing how stage-specific parameters govern structural/functional traits, and detail multifunctional modification strategies (e.g., heteroatom doping, composite engineering) that enhance electrochemical behavior through pore architecture optimization, surface chemistry tuning, and charge-transfer kinetics acceleration. Electrochemical applications are extensively explored, including the following: 1. Energy storage: supercapacitors (dual EDLC/pseudocapacitive mechanisms) and battery hybrids. 2. Electrocatalysis: HER, OER, ORR, and CO₂ reduction reaction (CO₂RR). 3. Electrochemical processing: capacitive deionization (CDI) and electrosorption. Beyond this core scope, we briefly acknowledge CA versatility in ancillary domains: environmental remediation (heavy metal removal, oil/water separation), flame retardancy, microwave absorption, and CO₂ capture. Full article

The DNDC-Rice model effectively simulates yield and greenhouse gas emissions within a paddy system, while its performance under upland conditions remains unclear. Using data from a long-term cover crop experiment (fallow [FA] vs. rye [RY]) in a soybean field, this study validated the DNDC-Rice model’s performance in simulating soil dynamics, crop growth, and C-N cycling processes in upland systems through various indicators, including soil temperature, water-filled pore space (WFPS), soybean biomass and yield, CO₂ and N₂O fluxes, and soil organic carbon (SOC). Based on simulated results, the underestimation of cumulative N₂O flux (25.6% in FA and 5.1% in RY) was attributed to both underestimated WFPS and the algorithm’s limitations in simulating N₂O emission pulses. Overestimated soybean growth increased respiration, leading to the overestimation of CO₂ flux. Although the model captured trends in SOC stock, the simulated annual values differed from observations (−9.9% to +10.1%), potentially due to sampling errors. These findings indicate that the DNDC-Rice model requires improvements in its N cycling algorithm and crop growth sub-models to improve predictions for upland systems. This study provides validation evidence for applying DNDC-Rice to upland systems and offers direction for improving model simulation in paddy-upland rotation systems, thereby enhancing its applicability in such contexts. Full article

Accurate quantification of above-ground biomass (AGB) and carbon sequestration is vital for monitoring terrestrial ecosystem dynamics, informing climate policy, and supporting carbon neutrality initiatives. However, conventional methods—ranging from manual field surveys to remote sensing techniques based solely on 2D vegetation indices—often fail to capture the intricate spectral and structural heterogeneity of forest canopies, particularly at fine spatial resolutions. To address these limitations, we introduce ForestIQNet, a novel end-to-end multimodal deep learning framework designed to estimate AGB and associated carbon stocks from UAV-acquired imagery with high spatial fidelity. ForestIQNet combines dual-stream encoders for processing multispectral UAV imagery and a voxelized Canopy Height Model (CHM), fused via a Cross-Attentional Feature Fusion (CAFF) module, enabling fine-grained interaction between spectral reflectance and 3D structure. A lightweight Transformer-based regression head then performs multitask prediction of AGB and CO₂e, capturing long-range spatial dependencies and enhancing generalization. Proposed method achieves an R² of 0.93 and RMSE of 6.1 kg for AGB prediction, compared to 0.78 R² and 11.7 kg RMSE for XGBoost and 0.73 R² and 13.2 kg RMSE for Random Forest. Despite its architectural complexity, ForestIQNet maintains a low inference cost (27 ms per patch) and generalizes well across species, terrain, and canopy structures. These results establish a new benchmark for UAV-enabled biomass estimation and provide scalable, interpretable tools for climate monitoring and forest management. Full article

This study presents a robust machine learning framework based on Gaussian process regression (GPR) to predict the tensile strength of polymer nanocomposites reinforced with various nanofillers and processed under diverse techniques. A comprehensive dataset comprising 25 polymer matrices, 22 surface functionalization methods, and 24 processing routes was constructed from the literature. GPR, coupled with Monte Carlo sampling across 2000 randomized iterations, was employed to capture nonlinear dependencies and uncertainty propagation within the dataset. The model achieved a mean coefficient of determination (R²) of 0.96, RMSE of 12.14 MPa, MAE of 7.56 MPa, and MAPE of 31.73% over 2000 Monte Carlo iterations, outperforming conventional models such as support vector machine (SVM), regression tree (RT), and artificial neural network (ANN). Sensitivity analysis revealed the dominant influence of Carbon Nanotubes (CNT) weight fraction, matrix tensile strength, and surface modification methods on predictive accuracy. The findings demonstrate the efficacy of the proposed GPR framework for accurate, reliable prediction of composite mechanical properties under data-scarce conditions, supporting informed material design and optimization. Full article

In accordance with growing environmental pressures and the demand for sustainable industrial practices, membrane technologies have emerged as key enablers for increasing efficiency, reducing emissions, and supporting circular processes across multiple sectors. This review focuses on the integration among microalgae-based systems, offering innovative and sustainable solutions for biomass production, carbon capture, and industrial wastewater treatment. In cultivation, membrane photobioreactors (MPBRs) have demonstrated biomass productivity up to nine times greater than that of conventional systems and significant reductions in water (above 75%) and energy (approximately 0.75 kWh/m³) footprints. For carbon capture, hollow fiber membranes and hybrid configurations increase CO₂ transfer rates by up to 300%, achieving utilization efficiencies above 85%. Coupling membrane systems with industrial effluents has enabled nutrient removal efficiencies of up to 97% for nitrogen and 93% for phosphorus, contributing to environmental remediation and resource recovery. This review also highlights recent innovations, such as self-forming dynamic membranes, magnetically induced vibration systems, antifouling surface modifications, and advanced control strategies that optimize process performance and energy use. These advancements position membrane-based microalgae systems as promising platforms for carbon-neutral biorefineries and sustainable industrial operations, particularly in the oil and gas, mining, and environmental technology sectors, which are aligned with global climate goals and the UN Sustainable Development Goals (SDGs). Full article

Understanding the adsorption mechanism is essential for developing efficient technologies to capture carbon dioxide from industrial flue gases. In this work, laboratory measurements, density functional theory calculations, and molecular dynamics simulations were employed to study CO₂ adsorption and diffusion behavior in LTA-type zeolites. The CO₂ adsorption isotherms measured in zeolite 5A are best described by the Toth model. Thermodynamic analysis indicates that the adsorption process is spontaneous and exothermic, with an enthalpy change of −44.04 kJ/mol, an entropy change of −115.23 J/(mol·K), and Gibbs free energy values ranging from −9.68 to −1.03 kJ/mol over the temperature range of 298–373 K. The isosteric heat of CO₂ adsorption decreases from 40.35 to 21.75 kJ/mol with increasing coverage, reflecting heterogeneous interactions at Ca²⁺ and Na⁺ sites. The adsorption kinetics follow a pseudo-first-order model, with an activation energy of 2.24 kJ/mol, confirming a physisorption mechanism. The intraparticle diffusion model indicates that internal diffusion is the rate-limiting step, supported by a significant reduction in the diffusion rate. The DFT calculations demonstrated that CO₂ exhibited a −35 kJ/mol more negative adsorption energy in zeolite 5A than in zeolite ITQ-29, attributable to strong interactions with Ca²⁺/Na⁺ cations in 5A that were absent in the pure silica ITQ-29 framework. The molecular dynamics simulations based on molecular force fields indicate that CO₂ diffuses more rapidly in ITQ-29, with a diffusion coefficient measuring 2.54 × 10⁻⁹ m²/s at 298 K, whereas it was 1.02 × 10⁻⁹ m²/s in zeolite 5A under identical conditions. The activation energy for molecular diffusion reaches 5.54 kJ/mol in zeolite 5A, exceeding the 4.12 kJ/mol value in ITQ-29 by 33%, which accounts for the slower diffusion kinetics in zeolite 5A. There is good agreement between experimental measurements and molecular simulation results for zeolite 5A across the studied temperature and pressure ranges. This confirms the accuracy and reliability of the selected simulation parameters and allows for the study of zeolite ITQ under similar simulation conditions. This research provides insights into CO₂ adsorption energetics and diffusion within LTA-type zeolite frameworks, supporting the rational design of high-performance adsorbents for industrial gas separation. Full article

This perspective explores the use of biochar, a carbon-rich material derived from biomass, as a sustainable solution for mitigating volatile organic compounds (VOCs) emitted during asphalt production and use. VOCs from asphalt contribute to ozone formation and harmful secondary organic aerosols (SOAs), which negatively impact air quality and public health. Biochar, with its high surface area and capacity to adsorb VOCs, provides an effective means of addressing these challenges. By tailoring biochar’s surface chemistry, it can efficiently capture VOCs, while also offering long-term carbon sequestration benefits. Additionally, biochar enhances the durability of asphalt, extending road lifespan and reducing maintenance needs, making it a promising material for sustainable infrastructure. Despite these promising benefits, several challenges remain. Variations in biochar properties, driven by differences in feedstock and production methods, can affect its performance in asphalt. Moreover, the integration of biochar into existing plant operations requires the further development of methods to streamline the process and ensure consistency in biochar’s quality and cost-effectiveness. Standardizing production methods and addressing logistical hurdles will be crucial for biochar’s widespread adoption. Research into improving its long-term stability in asphalt is also needed to ensure sustained efficacy over time. Overcoming these challenges will be essential for fully realizing biochar’s potential in sustainable infrastructure development Full article