Search Results (6,029)

As an important part of global carbon neutrality strategies, carbon dioxide (CO₂) capture, utilization, and storage technologies have emerged as critical solutions for reducing carbon emissions. However, conventional CO₂ applications, including food preservation, industrial synthesis, and enhanced oil recovery, face inherent limitations such as suboptimal gas–liquid mass transfer efficiency and inadequate long-term stability. Recent advancements in CO₂ micro-nanobubbles (CO₂ MNBs) have demonstrated remarkable potential across multidisciplinary domains, owing to their distinctive physicochemical characteristics encompassing elevated internal pressure, augmented specific surface area, exceptional stability, etc. In this review, we try to comprehensively explore the unique physicochemical properties of CO₂ MNBs and their emerging applications, including industrial, agricultural, environmental, and energy fields. Furthermore, we provide a prospective analysis of how these minuscule bubbles can emerge as pivotal in future technological innovations. We also offer novel insights and directions for research and applications across related fields. Finally, we engage in predicting their future development trends as a promising technological pathway for advancing carbon neutrality objectives. Full article

Cement production is a carbon-intensive process that contributes significantly to global greenhouse gas emissions. Approximately 50–60% of these emissions result from limestone calcination, while 30–40% result from fossil fuel combustion in kilns. This study presents a multi-objective optimization (MOO) framework that integrates raw mix design and alternative fuel blending to simultaneously reduce production costs and carbon dioxide (CO₂) emissions while maintaining clinker quality. A hybrid Genetic Algorithm–Linear Programming (GA-LP) model was developed to navigate the balance between economic and environmental objectives under stringent chemical and operational constraints. The approach models the impact of raw materials and fuel ash on critical clinker quality indices: the Lime Saturation Factor (LSF), Silica Modulus (SM), and Alumina Modulus (AM). It incorporates practical constraints such as maximum substitution rates and specific fuel compositions. A case study inspired by a medium-sized African cement plant demonstrates the utility of the model. The results reveal a Pareto front of optimal solutions, highlighting that a 20% reduction in CO₂ emissions from 928 to 740 kg/ton clinker is achievable with only a 24% cost increase. Optimal strategies include 10% fly ash and 30–50% alternative fuels, such as biomass, tire-derived fuel (TDF), and dynamic raw mix adjustments based on fuel ash contributions. Sensitivity analysis further illustrates how biomass cost and LSF targets affect clinker performance, emissions, and fuel shares. The GA-LP hybrid model is validated through process simulation and benchmarked against African case studies. Overall, the findings provide cement producers and policymakers with a robust decision-support tool to evaluate and adopt sustainable production strategies aligned with net-zero targets and emerging carbon regulations. Full article

This study aimed to develop an effective method to overcome the challenge of straw return in cold-region soil. We systematically investigated the synergistic mechanism of montmorillonite (MMT) and composite amino acid (CAA) on straw humification and carbon sequestration through a low-temperature litterbag field experiment. The results indicate that the combined treatment (MMT-CAA) significantly increased the decomposition rate of straw by 42.1% compared to the control (CK), with MMT showing particular efficacy in lignin degradation (28.3% reduction), while the CAA preferentially decomposed cellulose (19.7% reduction). An FTIR analysis of the decomposition products confirmed these findings. Water-soluble organic carbon (WEOC) and its three-dimensional fluorescence spectra exhibited a 25.0% increase in MMT-CAA and enhanced aromaticity of humic acid-like substances. Humic substances and their ¹³C-NMR revealed that MMT-CAA enhanced humic acid formation and molecular stability by 31.4% (with a 47.8% increase in aromaticity). A further redundancy analysis and symbiotic network of microorganisms demonstrated that MMT-CAA increased the abundance of lignocellulose-degrading phyla (Actinomycetes and Stramenomycetes) and the formation of a complex co-degradation network. Field corn planting trials indicated that MMT-CAA increased plant height by 55.1%, stem thickness by 58.7%, leaf area by 70.2%, and the SPAD value by 41.1%. Additionally, MMT significantly reduced CO₂ and N₂O emission fluxes by 35.6% and 15.8%, respectively, while MMT-CAA increased CH₄ uptake fluxes by 13.4%. This study presents an innovative strategy, providing mechanistic insights and practical solutions to synergistically address the challenges of slow straw decomposition and carbon loss in cold regions. Full article

Time-series forecasting is widely recognized as essential for integrating renewable energy, managing emissions, and optimizing demand across energy and environmental applications. Initially, traditional forecasting methods are hindered by limitations including poor interpretability, limited generalization to diverse scenarios, and substantial computational demands. Consequently, a novel minimum-variance self-tuning (MVST) method is proposed, grounded in adaptive control theory, to overcome these challenges. The method utilizes recursive least squares with self-tuning parameter updates, delivering high prediction accuracy, rapid computation, and robust multi-step forecasting without pre-training requirements. Testing is performed on CO₂ emissions (annual), transformer load (15 min), and building electric load (hourly) datasets, comparing MVST against LSTM, ARDL, fixed-PID, XGBoost, and Prophet across varied scales and contexts. Significant improvements are observed, with prediction errors reduced by 3–8 times and computational time decreased by up to 2000 times compared to these methods. Finally, these advancements facilitate real-time power system dispatch, enhance energy planning, and support carbon emission management, demonstrating substantial research and practical value. Full article

The presence of PET (polyethylene terephthalate) in the environment is a global problem due to soil and water microplastic contamination. There is a constant demand for new technologies that expand the possibilities of PET disposal or recycling while reducing energy consumption and anthropogenic carbon footprint. In this study, we developed a comprehensive zero-waste management system for PET recycling (rPET) to cyclic ketals and terephthalic acid. The developed method is based on the hydrolysis of rPET flakes in an inert environment with the separation and purification of terephthalic acid and the dehydration of ethylene glycol. For the first time, we present the use of cheap and readily available Cr/SiO₂ and Fe/SiO₂ nanocatalysts for direct acetalization of ethylene glycol without organic co-solvents. The catalysts were characterized by EDXRF, XPS and TEM techniques. The 2,2-dimethyl-1,3-dioxolane (DMD), a product of ethylene glycol’s direct acetalization with acetone, was tested as a solvent for polymers with satisfactory results in the solubility of epoxy resins. The addition of unpurified terephthalic acid and residues constituting post-production waste to concrete allows for a reduction in the mass of concrete in the range of 11.3–23.4% and the material modified in this way allows for a reduction in concrete consumption. This rPET waste management methodology is consistent with the assumptions of the circular economy and allows for a significant reduction of anthropogenic CO₂ emissions. Full article

Although peatland restoration has been widely promoted as a strategy for reducing carbon emissions and restoring hydrological function, its effectiveness remains context-dependent and highly variable across regions and methods. This study presents a systematic review and meta-analysis of 52 peer-reviewed studies from 2014 to 2024, synthesizing the ecohydrological impacts of restoration across multiple spatial scales and implementation types. In tropical peatlands, restoration frequently reduced CO₂ emissions by more than 65,000 kg·ha⁻¹·yr⁻¹ and increased carbon sequestration up to 39,700 kg·ha⁻¹·yr⁻¹, with moderate CH₄ increases (~450 kg·ha⁻¹·yr⁻¹). In boreal sites, CO₂ reductions were generally below 25,000 kg·ha⁻¹·yr⁻¹, with long-term carbon accumulation reported in other studies, typically around 2–3 tCO₂·ha⁻¹·yr⁻¹. Higher values in our dataset likely reflect the limited number of boreal studies and the influence of short-term measurements. Across all regions, restoration was also associated with an average rise in WTD up to 10 cm. These averages were derived from studies conducted across diverse climatic zones, showing high standard deviations, indicating substantial inter-site heterogeneity. These differences emphasize the need for region-specific assessments rather than global generalizations, highlighting the importance of adaptive restoration strategies that balance carbon dynamics with hydrological resilience in the face of climate change. Full article

This study presents the development of a reliable predictive model for evaluating key physical and mechanical properties of cement-based composites modified with granite powder, a waste byproduct from granite rock cutting. The research addresses the need for more sustainable materials in the concrete industry by exploring the potential of granite powder as a supplementary cementitious material (SCM) to partially replace cement and reduce CO₂ emissions. The experimental program included standardized testing of samples containing up to 30% granite powder, focusing on compressive strength at 7, 28, and 90 days, bonding strength at 28 days, and packing density of the fresh mixture. A multilayer perceptron (MLP) artificial neural network was employed to predict these properties using four input variables: granite powder content, cement content, sand content, and water content. The network architecture, consisting of two hidden layers with 10 and 15 neurons, respectively, was selected as the most suitable for this purpose. The model achieved high predictive performance, with coefficients of determination (R) exceeding 0.9 and mean absolute percentage errors (MAPE) below 6% for all output variables, demonstrating its robustness and accuracy. The findings confirm that granite powder not only contributes positively to concrete performance over time, but also supports environmental sustainability goals by reducing the carbon footprint associated with cement production. However, the model’s applicability is currently limited to mixtures using granite powder at up to 30% cement replacement. This research highlights the effectiveness of machine learning, specifically neural networks, for solving multi-output problems in concrete technology. The successful implementation of the MLP network in this context may encourage broader adoption of data-driven approaches in the design and optimization of sustainable cementitious composites. Full article

The instability of the energy supply, growing demand and the need to reduce carbon emissions are priority challenges in developing countries such as Ecuador, where power outages affect productivity and generate economic losses. Therefore, solar energy is positioned as a sustainable alternative. The objective of this study is to evaluate a pilot photovoltaic (PV) system for residential housing in coastal areas in the Santa Elena province, Ecuador. The methodology included the following: (i) criteria for the selection of three representative residential housings; (ii) design of a distributed generation system using PVsyst software; and (iii) proposal of strategic guidelines for the design of PV systems. This proposed system proved to be environmentally friendly, achieving reductions of between 16.4 and 32 tonnes of CO₂ in the first 10 years. A return on investment (ROI) of 16 years was achieved for the low-demand (L) scenario, with 4 years for the medium-demand (M) scenario and 2 years for the high-demand (H) scenario. The sensitivity analysis showed that the Levelized Cost of Energy (LCOE) is more variable in the L scenario, requiring more efficient designs. It is proposed to diversify the Ecuadorian energy matrix through self-supply PV systems, which would reduce electricity costs by 6% of consumption (L scenario), 30% (M scenario), and 100% (H scenario). Although generation is concentrated during the day, the net metering scheme enables compensation for nighttime consumption without the need for batteries, thereby improving the system’s profitability. The high solar potential and high tariffs make the adoption of sustainable energy solutions a justifiable choice. Full article

Oil camellia (Camellia oleifera) residues from low-yield forests offer significant potential for carbon emission reductions across multiple product pathways—fibreboard, pulp, and bioelectricity. Life cycle assessments (LCAs) were conducted for these three products, revealing distinct carbon footprints driven by energy use, chemical inputs, and combustion processes. Fibreboard production showed a carbon footprint of 244.314 kg CO₂e/m³, primarily due to diesel use and electricity consumption. Pulp production exhibited the highest carbon intensity at 481.626 kg CO₂e/t, largely driven by chemical consumption and fossil fuels. Bioelectricity, with the lowest carbon footprint of 41.750 g CO₂e/kWh, demonstrated sensitivity to transportation logistics and fuel types. Substitution and scenario analysis showed that emission reductions can be achieved by optimizing energy structure, substituting high-carbon chemicals, and improving transportation efficiency. The findings highlight the substantial reduction potential when oil camellia residues replace conventional feedstocks in these industries, contributing to the development of low-carbon strategies within the bioeconomy. These results also inform the design of targeted mitigation policies, enhancing carbon accounting frameworks and aligning with China’s dual-carbon goals. Full article

The construction industry is a major source of environmental degradation as it is responsible for a significant share of global CO₂ emissions, especially from cement and aggregate consumption. This study fills the need for sustainable construction materials by developing and evaluating a low-carbon fiber-reinforced concrete (FRC) made of steel slag powder (SSP), processed recycled concrete aggregates (PRCAs), and waste steel rivet fibers (WSRFs) derived from industrial waste. The research seeks to reduce dependency on virgin materials while maintaining high values of mechanical performance and durability in structural applications. Sixteen concrete mixes were used in the experimental investigations with control, SSP, SSP+RCA, and RCA, reinforced with various fiber dosages (0%, 0.2%, 0.8%, 1.4%) by concrete volume. Workability, density, compressive strength, tensile strength, and water absorption were measured according to the appropriate standards. Compressive and tensile strength increased in all mixes and the 1.4% WSRF mix had the best performance. However, it was found that a fiber content of 0.8% was optimal, which balanced the improvement in strength, durability, and workability by sustainable reuse of recycled materials and demolition waste. It was found by failure mode analysis that the transition was from brittle to ductile behavior as the fiber content increased. The relationship between compressive, tensile strength, and fiber content was visualized as a 3D response surface in order to support these mechanical trends. It is concluded in this study that 15% SSP, 40% PRCA, and 0.8% WSRF are feasible, specific solutions to improve concrete performance and advance the circular economy. Full article

Biomass gasification represents a pivotal technology for sustainable energy and chemical production, yet its efficiency and product quality are critically dependent on catalyst performance. This comprehensive review systematically synthesizes recent advancements in catalyst design, mechanistic insights, and process integration in biomass gasification. Firstly, it details the development and performance of catalysts in diverse categories, including metal-based catalysts, Ca-based catalysts, natural mineral catalysts, composite/supported catalysts, and emerging waste-derived catalysts. Secondly, this review delves into the fundamental catalytic reaction mechanisms governing key processes such as tar cracking/reforming, water–gas shift, and methane reforming. It further explores sophisticated strategies for catalyst structure optimization, focusing on pore structure/surface area control, strong metal–support interactions (SMSIs), alloying effects, nanodispersion, and crystal phase design. The critical challenges of catalyst deactivation mechanisms and the corresponding activation, regeneration strategies, and post-regeneration performance evaluation are thoroughly discussed. Thirdly, this review addresses the crucial integration of zero CO₂ emission concepts, covering in situ CO₂ adsorption/conversion, carbon capture and storage (CCS) integration, catalytic CO₂ reduction/valorization, multi-energy system synergy, and environmental impact/life cycle analysis (LCA). By synthesizing cutting-edge research, this review identifies key knowledge gaps and outlines future research directions towards designing robust, cost-effective, and environmentally benign catalysts for next-generation, carbon-neutral biomass gasification systems. Full article

CO₂ geological storage (CGS) is critical for mitigating emissions in hard-to-abate industries under carbon neutrality. However, its implementation faces significant challenges. This paper examines CO₂-trapping mechanisms and proposes key safety measures: the continuous monitoring of in situ CO₂ migration and formation pressure dynamics to prevent remobilization, and pre-injection lithological analysis to assess mineral trapping potential. CO₂ injection alters reservoir stresses, inducing surface deformation; understanding long-term rock mechanics (creep, damage) is paramount. Thermomechanical effects from supercritical CO₂ injection pose risks to caprock integrity and fault reactivation, necessitating comprehensive, multi-scale, real-time monitoring for leakage detection. Geostatistical analysis of well log and seismic data enables realistic subsurface characterization, improving numerical model accuracy for risk assessment. This review synthesizes current CGS knowledge, analyzes technical challenges, and aims to inform future site selection, operations, and monitoring strategies. Full article

As global temperatures continue to rise, surpassing the +2.5 °C threshold under current emissions scenarios, the urgency for sustainable, effective carbon management strategies has intensified. Geological carbon storage (GCS) has been explored as a potential mitigation tool; however, its large-scale feasibility remains highly uncertain due to concerns over storage permanence, leakage risks, and economic viability. This study proposes three advanced deep learning models—DeepDropNet, GateSeqNet, and RecurChainNet—to predict the Residual Trapping Index (RTI) and Solubility Trapping Index (STI) with enhanced accuracy and computational efficiency. Using a dataset of over 2000 high-fidelity simulation records, the models capture complex nonlinear relationships between key reservoir properties. Results indicate that GateSeqNet achieved the highest predictive accuracy, with an R² of 0.95 for RTI and 0.93 for STI, outperforming both DeepDropNet and RecurChainNet. Ablation tests reveal that excluding post injection and injection rate significantly reduced model performance, decreasing R² by up to 90% in RTI predictions. The proposed models provide a computationally efficient alternative to traditional numerical simulations, which makes them viable for real-time CO₂ sequestration assessment. This work advances AI-driven carbon sequestration strategies, offering robust tools for optimizing long-term CO₂ storage performance in geological formations and for achieving global sustainability goals. Full article

This study aimed to investigate the effects of diesel/ethanol/n-butanol mixed fuel on the marine diesel engine combustion and emissions at different ethanol blending ratios, different single injection times, and pre-injection times. In addition, this study takes the injector fault phenomenon as an example, simulates the three fault phenomena of the injector, and uses a variety of algorithms to optimize the probabilistic neural network model to achieve the fault state identification and diagnosis of the injector. The results of research showed that, with the increase in the ethanol blending ratio, the peak cylinder pressure shows a decreasing trend. The ignition delay period is extended, and the peak instantaneous heat release rate increases. Compared with D100, the nitrogen oxide (NO_x) emissions of D50E40B10 mixed fuel are reduced by 12.3%, soot emissions are reduced by 29.18%, and carbon monoxide (CO) emissions are increased by 5.7 times. With the injection time advances, the peak values of cylinder pressure and heat release rate show an increasing trend, soot emissions gradually decrease, and NO_x and CO emissions gradually increase. The peaks of the cylinder pressure and heat release rate in the pilot injection stage gradually decrease as the pilot injection time advances, while the peak heat release rate in the main injection stage increases. In terms of emissions, NO_x emissions first decrease and then increase as the pilot injection time advances, while soot emissions gradually increase. The average accuracy of the PSO-PNN neural network model reaches 90%, and the average accuracy of the WOA-PNN neural network model reaches 95%. Therefore, the WOA-PNN neural network model is determined to be the optimal injector fault diagnosis model, which can be applied to the identification and diagnosis of injector fault states of diesel engines. Full article

Food loss and waste (FLW) impose major nutritional and environmental costs globally. This comprehensive China-wide study quantifies FLW-driven nutrient depletion and greenhouse gas (GHG) emissions across the entire supply chain. Using national-scale modeling with China-specific data, we found that FLW in 2022 reached 415 million tons (i.e., 21.4% of total production was lost/wasted), generating 281 Mt CO₂-eq. Daily per capita FLW at 757 kcal (29.7% of recommended intake lost/wasted), 28.4 g protein (43.7%), and 114 mg vitamin C (14%) dissipated significant nutrients. Using the Wasted Nutrient Days metric, 72–416 days of varying nutrient adult needs were lost, worsening malnutrition burdens. The key node along the supply chain leading to high FLW is postharvest handling and storage (responsible for 49% of FLW mass and emissions), while vegetables/cereals (mass loss quantities) and meat-based foods (high emission intensity) were the most lost/wasted food types. Scenario analysis shows that combining optimized diets and supply chain improvements could reduce FLW by 503 g/capita/day and emissions by 62.2%, closing nutritional gaps and supporting carbon neutrality. Full article