Search Results (341)

In order to reach net-zero by 2050, we need to have strong decarbonization policies, especially in hard-to-abate clean-ups like steel (8% of the global emissions), cement (7%), and power generation (30%), and negative emissions through direct air capture (DAC) and bioenergy with carbon capture and storage (BECCS). This review paper summarizes the progress in CO₂ capture, compression, transportation, and storage technologies between 2020 and 2025, including energy penalty (20–40%) and cost (15–30%) reductions, with innovations such as metal–organic frameworks (MOFs), bio-inspired catalysts, ionic liquids, and artificial intelligence (AI)-based optimization. This paper, as a new input into the carbon capture and storage (CCS) field, uses the Weighted Sum Model (WSM) as a multi-criteria decision-making tool to rank the best technologies in the capture, storage, monitoring, and transportation sectors. The weights of the criteria are calculated based on Shannon entropy, and the assessment is performed in three conditions, namely, optimistic, pessimistic, and expected. The weights are computed with sensitivity analysis to make the assessment robust. The viability of key projects, such as Northern Lights (Norway, 1.5 MtCO₂/year), Porthos (The Netherlands, 2.5 MtCO₂/year), Quest (Canada, 1 MtCO₂/year), and Petra Nova (USA, 1.6 MtCO₂/year), is evident, and it is projected that, globally, CCS will reach 49 MtCO₂/year across 43 plants in 2025. The review incorporates socio-economic and environmental justice, including barriers such as high costs ($30–600/MtCO₂), energy penalties (1–10 GJ/tCO₂), and opposition between people (20–40% in EU/US). In comparison with previous reviews, this article has a more comprehensive focus, provides quantitative synthesis through WSM, and discusses the implications for researchers, policymakers, and stakeholders towards achieving faster CCS implementation on the path to net-zero. Full article

Hybrid renewable energy systems (HRES) combining photovoltaic, wind, fuel cell, and energy storage technologies are becoming established as viable options for reliable, environmentally friendly distributed electricity generation. In this review, we examine the key architectures, monitoring and forecast approaches, and control systems that improve the efficiency of HRES and facilitate the just-energy transition to low-carbon power generation systems. The main optimization and decision-aware approaches, particularly the evolutionary generation algorithms and machine learning-based prediction models, are addressed with a focus on improving energy allocation, cost minimization, and increased use of clean renewable energy sources. Technical, economic, and environmental performance indicators, such as the levelized cost of energy (LCOE), net present cost (NPC), renewable fraction (RF), and CO₂ emissions reduction, have been compared to demonstrate the feasibility of various system scenarios. This paper evaluates and summarizes recent case studies from around the world and presents the best practices and the challenges they encounter, including resource availability, governance, and economic drivers. The balance of the paper demonstrates that smart forecasting with advanced energy management approaches is crucial for developing sustainable and resilient hybrid distributed power systems for the future. Full article

Controlling carbon dioxide (CO₂) emissions remains a central challenge in Indonesia’s energy transition and its commitment to achieving net-zero emission targets. Carbon Capture and Storage (CCS) and Carbon Capture, Utilization, and Storage (CCUS) are widely recognized as important mitigation pathways, particularly for energy and industrial sectors where rapid decarbonization remains difficult. In parallel, cold plasma technology has emerged in the recent scientific literature as an early-stage, non-thermal approach for CO₂ activation under relatively low bulk temperature conditions, attracting interest as a potential long-term research pathway. This paper examines cold plasma technology within the broader CCS/CCUS landscape in Indonesia from a policy and technology perspective. The study adopts a qualitative and descriptive approach, synthesizing the selected academic literature on plasma-based CO₂ conversion, global CCUS development trends, and Indonesia’s regulatory, infrastructural, and energy system context. Rather than assessing techno-economic feasibility, the analysis focuses on identifying structural constraints, performance trade-offs, and policy-relevant considerations. The findings indicate that across plasma configurations, including dielectric barrier discharge, gliding arc, microwave, and radio frequency plasmas, current research outcomes remain constrained by low energy efficiency, limited scalability, and low technology readiness for large-scale applications. Reported performance metrics are largely derived from laboratory-scale studies under controlled conditions and cannot yet be extrapolated to real-world emission sources without a comprehensive system-level evaluation. Compared with established CCS and CCUS pathways, cold plasma technologies remain exploratory and lack the maturity required for near-term deployment. From a policy and research perspective, cold plasma should therefore be regarded as a long-term research option rather than an implementable mitigation solution for Indonesia, with its potential contribution lying in informing future research agendas, technology monitoring, and innovation planning, particularly in relation to CO₂ utilization concepts and decentralized energy systems, contingent upon significant advances in energy performance, system integration, and standardized evaluation frameworks. Full article

Lithium-ion batteries (LIBs) may experience thermal runaway (TR) under thermal abuse conditions, posing significant safety risks to energy storage systems, electric vehicles, and portable electronics. To ensure the safety of LIB-powered applications, developing an effective TR early warning method is crucial. This study employs polyimide-coated femtosecond fiber Bragg grating (FBG) sensors to investigate TR characteristics in 18,650 LIBs (LiNi_1/3Mn_1/3Co_1/3O₂/graphite), including TR onset temperature determination and the evolution of temperature and radial strain at different states of charge (SOCs). Compared with existing studies, the polyimide-coated femtosecond FBGs employed here offer superior breakage resistance and high-temperature tolerance, enabling more precise temperature and strain measurements. For radial strain monitoring obtained during high-temperature-induced LIBs thermal runaway experiments, temperature compensation was achieved using polyimide-coated femtosecond FBG temperature sensors, yielding higher-accuracy strain evolution profiles. Experimental results demonstrate that the higher-SOC LIBs exhibit more severe TR eruptions, with 1.76× higher peak temperatures and 1.3× greater mass loss than low-SOC LIBs. The proposed scheme pioneers an new approach to effective active safety warning of LIBs thermal runaway. Full article

Fresh bluefin tuna is highly susceptible to quality deterioration during refrigerated storage due to lipid oxidation and microbial activity, creating a need for effective clean-label preservation strategies. This study evaluated the efficacy of natural astaxanthin as an antioxidant treatment to improve the refrigerated stability of fresh bluefin tuna (Thunnus thynnus) fillets stored under vacuum packaging (VP) or modified atmosphere packaging (MAP; 70% N₂/30% CO₂). Tuna fillets were treated by short immersion in astaxanthin solutions (10–20 mg/L), applied alone or in combination with other natural antioxidants, including ascorbic acid, and compared with a rosemary–ascorbic acid reference system. Selected treatments incorporated microencapsulated astaxanthin to enhance antioxidant stability. Quality changes were monitored during refrigerated storage (4 °C) through sensory evaluation (appearance, colour, and odour), total volatile basic nitrogen (TVBN), histamine determination, and microbiological analyses. Astaxanthin-treated samples exhibited improved colour stability, delayed sensory deterioration, and significantly lower TVBN accumulation compared with the rosemary–ascorbic acid reference treatment. Under MAP conditions, astaxanthin reduced TVBN values by approximately 20% after 12 days of storage, while microencapsulated astaxanthin combined with ascorbic acid achieved reductions of up to 30% under vacuum packaging. All selected treatments complied with regulatory microbiological and histamine limits throughout storage. These results indicate that natural astaxanthin, particularly in microencapsulated formulations, can enhance quality stability of fresh bluefin tuna when applied in combination with oxygen-limiting packaging systems under controlled refrigerated conditions. The findings provide a scientific basis for further investigation of astaxanthin-based preservation strategies in high-value seafood products. Full article

Submarine gas emissions represent a key expression of fluid migration processes in volcanic and hydrothermal marine environments and provide valuable analogues for monitoring strategies relevant to sub-seabed carbon storage. This study investigates the feasibility of using marine Distributed Acoustic Sensing (DAS) to detect natural CO₂ bubble emissions in a shallow-water setting offshore Panarea (Aeolian Islands, Italy). A 1.1 km armored fiber-optic cable was deployed on the seabed and interrogated using two different DAS systems to acquire continuous passive acoustic data. The DAS recordings were complemented by controlled gas releases from scuba tanks to provide reference signals, as well as by independent high-resolution boomer seismic survey and side-scan sonar imaging to characterize the shallow subsurface and seabed morphology. The results show that DAS is sensitive to acoustic signals associated with both artificial and natural bubble emissions, despite the complex acoustic conditions typical of shallow marine environments. The integration of passive DAS monitoring with independent geophysical observations provides a robust framework for interpreting gas-related signals and seabed processes. These findings demonstrate that marine DAS represents a promising geophysical tool for monitoring of submarine volcanic–hydrothermal systems and offers important insights for the development of sub-seabed CO₂ leakage detection in offshore CCS contexts. Full article

This study evaluated the effect of water vapor generated by fresh-cut mango (Mangifera indica) on the release of β-carotene from β-cyclodextrin complexes (β-C:β-CD) under stored Modified Atmosphere Packaging (MAP) and to demonstrate β-carotene stabilization and passive–active packaging behavior under MAP conditions. Containers with fresh-cut mangoes, with and without MAP (4% O₂, 6% CO₂, 90% N₂), were prepared for monitoring over 6 days at 4 °C. β-C:β-CD complexes were incorporated into the lids of containers. The physicochemical, relative humidity, antioxidant, erythroprotective, microbiological, and biofunctional qualities of freshly cut mangoes during storage were analyzed. Active metabolic respiration of plant tissue led to a progressive decrease in O₂ and an increase in CO₂ in sealed containers, a phenomenon intensified by cutting, high humidity, and the system’s limited gas permeability. Application of MAP effectively modulated this microenvironment, reducing respiration rate, water loss, acidification, and the degradation of bioactive compounds. Compared to treatments without MAP, mangoes stored under modified atmosphere showed greater color stability, a slower rate of change in pH and titratable acidity, less loss of antioxidant activity and phenolic compounds, and significant preservation of erythroprotective capacity. Furthermore, MAP maintained microbial counts within the limits established by current regulations until the sixth day of storage. The encapsulation of β-C in β-CD effectively protected its bioactivity from oxidation, especially under MAP, although its release into the food matrix was limited, suggesting a predominantly passive behavior of the active packaging system. Overall, the results demonstrate that the combination of MAP constitutes a promising strategy for extending the shelf life and biofunctional stability of fresh-cut mangoes and β-C into the complex. Full article

Ensuring product integrity across Malaysia’s East Malaysian states (Sabah and Sarawak) requires a cold chain that is resilient to tropical heat, long multimodal routes, intermittent power, and dispersed rural populations. This paper proposes a sustainability-first architecture for vaccine and blood component logistics that combines World Health Organization and the United Nations International Children’s Emergency Fund Effective Vaccine Management (EVM 2.0) criteria with energy-aware transport planning, solar-hybrid edge refrigeration, phase-change materials, and digital temperature monitoring compliant with ISO 23412 for temperature-controlled delivery services. In this study, a mixed-methods methodology was employed, including (1) route and mode optimization under temperature risk and carbon intensity constraints; (2) equipment right-sizing using duty-cycle energy models and IEC 60068 environmental tests as design baselines; (3) governance with real-time earned value management (EVM) and key performance indicators (KPIs); and (4) scenario analysis for riverine, road, air, and drone last-mile segments relevant to remote East Malaysian communities. Results from realistic logistic scenarios indicate a 45–65% reduction in dose-weighted temperature-excursion minutes, 28–41% reduction in CO₂e per successful dose delivered, and 35–52% reduction in product loss compared with status quo planning. For blood components, solar-hybrid storage and mixed-mode routing reduced breach risk by 37% while maintaining red cells (2–6 °C), platelets (20–24 °C, continuous agitation surrogate), and fresh frozen plasma (≤−18 °C) requirements aligned with WHO guidance and Malaysia’s national transfusion policies. We provide a reference architecture, implementation bill of materials, and an EVM-aligned KPI dashboard to guide scale-up. Full article

Securing mine sites is a challenging task due to the complexity of the infrastructure, the variety of physical and digital components, the distribution of assets and machineries, and the large number of stakeholders involved. Given the risks that are present in Tailings Storage Facilities (TSFs), mine operators are seeking technologies to accurately monitor the state of their dams. The latest developments implement evolutive monitoring and responsive risk management systems by adapting accurate Internet of Things technologies, automated mathematical model calculation to continually monitor the structural/geotechnical aspects of TSF, and a portfolio of innovative applications to support decision-making. Within this study, a comprehensive methodology is developed for assessing the environmental sustainability of a smart monitoring solution combining the life cycle assessment (LCA) method with the environmental risk assessment, which quantifies risk reduction potential. The use case scenario is identified based on real industrial data, also aligned with the common characteristics of tailing dams in Europe. Environmental sustainability of the smart monitoring solution is assessed through a cradle-to-grave LCA based on the ReCiPe 2016 (v1.1 Midpoint (H)) method. Monitoring impact alone is reduced primarily by the 40% reduction in monitoring visits, while the results show the environmental improvement of the TSF life cycle by 24% for CO₂-eq., as a step in-line with the EU’s long-term strategy for total decarbonisation in 2050, and Sustainable Development Goal 9 for Industry by the United Nations. Additionally, the 27% freshwater ecotoxicity reduction, 20% human toxicity (cancer) decrease, and the rest of the studied categories indicate an overall footprint improvement for the monitoring solution application on TSFs. The findings demonstrate clearly theoretical, practical and policy implications, not only for the benefit of such solutions for environmental protection, but also for the necessity of integrating risk in sustainability analysis approaches. Full article

This work presents a compact, self-powered wireless CO₂ sensing node for autonomous environmental monitoring. The system integrates a high-efficiency multijunction photovoltaic (PV) module, a 4000 F hybrid supercapacitor operating at 3.6–4.2 V, and a custom power management system in a LiPo-sized form factor. The PV module, composed of nine parallel triple-junction solar cells, achieves an average efficiency of 27% and delivers peak power at 4.26 V under 600 W/m² irradiance. The sensing unit includes miniaturized CO₂, humidity, and temperature sensors with LoRa-based wireless communication. The low-power NDIR CO₂ sensor provides a resolution of 15–20 ppm and a response time of ~45 s. Week-long tests demonstrated fully autonomous operation with reliable 5 min data transmission, capturing diurnal CO₂ variations associated with plant activity even under low irradiance. Energy storage occurs for irradiance levels ≥65 W/m², and long-term simulations confirm stable supercapacitor voltage over yearly cycles. This work demonstrates a compact multijunction solar–hybrid supercapacitor platform capable of sustaining WSN for long-term, maintenance-free CO₂ monitoring under real-world and low-irradiance conditions. Our results demonstrate that the sensing node can reliably monitor plant-driven CO₂ dynamics, clearly resolving the expected photosynthesis–respiration cycles and their dependence on incident solar radiation, while simultaneously sustaining its energy budget under highly challenging illumination and transmission conditions. Full article

Geologic carbon storage (GCS) is expanding rapidly as a cornerstone decarbonization option, but its climate value depends on maintaining long-term containment of CO₂ and displaced formation brine. Legacy wells—many drilled and abandoned before modern barrier standards—remain one of the most credible and controllable pathways for unintended upward migration. To support transparent, fit-for-purpose risk screening, this study benchmarks three leakage-modeling philosophies across a common six-layer scenario: (i) a reservoir-scale analytical solution for layered aquifers, (ii) a semi-analytical pressure-transient model that captures rock–fluid compressibility and breakthrough time, and (iii) a new mechanistic wellbore-scale model that explicitly represents dominant annular failure pathways (micro-annuli, cement fractures, casing breaches, and cement–formation interface flow) with pathway-specific hydraulic losses. Results show that model choice and physics assumptions drive order-of-magnitude differences in predicted brine rates: after 1000 days, the analytical model predicts ~1.7 bbls/day, the pressure-transient model exceeds 8 bbls/day, whereas the mechanistic model yields damage-dependent outcomes (~0.2–0.4 bbls/day for moderate–severe cement damage and up to ~3.5 bbls/day for open-channel conditions). These findings demonstrate that neglecting wellbore hydraulic resistance can systematically overstate leakage risk, while mechanistic pathway representation enables more realistic, condition-dependent screening. Future work will focus on model calibration to field/monitoring data, probabilistic parameterization of defect geometries, and extension to multiphase/reactive leakage to support operational decision-making and regulatory assurance. Full article

Accurate state-of-health (SOH) estimation is a cornerstone of safe, reliable, and cost-effective operation of lithium-ion batteries (LIBs) in electric vehicles and energy storage systems. In recent years, rapid advances in artificial intelligence technology have led to the widespread adoption of neural-network-based SOH estimation methods, offering strong nonlinear modeling capability and improved adaptability compared with traditional model-based approaches. However, the growing diversity of neural architectures and learning strategies has led to fragmented development and inconsistent evaluation, hindering their practical deployment. This paper presents a critical and systematic review of the most recent representative studies on neural-network-based SOH estimation for LIBs between 2024 and 2025. A unified taxonomy is introduced to distinguish neural architectures from learning strategies. The neural architectures include artificial neural networks, convolutional and recurrent networks, attention-based models, Transformers, and physics-informed neural networks. The learning strategies encompass transfer learning, physics-constrained/physics-informed learning, robustness-oriented training and efficiency-aware design. The reviewed methods are analyzed in terms of modeling capability, generalization across operating conditions and chemistries, data efficiency, interpretability and deployability within battery management systems. Key challenges including nonlinear degradation, degradation diversity, data scarcity, and limited observability are critically examined. The roles of architecture-strategy co-design in addressing these issues are highlighted. Finally, open research directions and practical recommendations are discussed to guide the development of reliable, scalable and physically consistent SOH estimation frameworks. This review provides a structured reference for researchers and practitioners seeking to advance data-driven battery health monitoring toward real-world applications. Full article

The transition toward active distribution networks requires advanced control solutions capable of handling the rapid dynamics of distributed energy resources. This paper proposes a low-cost, intelligent IoT architecture designed for the real-time optimization and analysis of energy systems within low-voltage networks. Unlike centralized monitoring approaches constrained by communication latency, the proposed solution leverages Intelligent Edge Processing (IEP) implemented on ESP32 embedded nodes to optimize data flow and decision-making. This architecture executes stability assessments directly at the network edge, calculating critical analysis indicators such as the Voltage Deviation Index (VDI) and Rate of Change of Frequency (RoCoF). The system was validated on the CIGRE European LV benchmark under severe stress scenarios, including rapid solar transients and voltage sags. The results demonstrate that the proposed architecture effectively coordinates storage interventions, ensuring voltage recovery within 300 ms and maintaining power quality within EN 50160 limits even during severe voltage sags. The study validates the feasibility of using industrial IoT edge computing as a resilient, non-wire alternative for modernizing complex energy systems. Full article

Passion fruit deteriorates rapidly after harvest owing to fungal decay and quality loss. This study examined whether acidic electrolyzed water (AEW, pH 2.5) could strengthen host defense responses and thereby prolong the marketable storage period of passion fruit. Freshly harvested yellow passion fruits (without any prior storage) were immersed for 20 min in AEW containing 0 (control), 30, 60 or 90 mg/L available chlorine concentration (ACC) and then packaged in polyethylene film bags and stored at 25 °C for 15 days to simulate typical ambient handling/marketing conditions, where polyethylene packaging is commonly used to maintain a high-humidity microenvironment and reduce moisture loss; physicochemical attributes, decay parameters and disease-resistance-related enzyme activities were then monitored. AEW—particularly at 60 mg/L ACC—significantly lowered decay incidence, disease index and cell membrane permeability while preserving pericarp color (hue angle h, L*) and pulp titratable acidity, vitamin C, total soluble solids, and total soluble sugars. The same treatment elevated the concentrations of disease-resistant metabolites (total polyphenolics, flavonoids and lignin) and up-regulated the activities of peroxidase, cinnamate-4-hydroxylase, 4-coumarate CoA ligase, phenylalanine ammonia-lyase, cinnamyl alcohol dehydrogenase, chitinase, and β-1,3-glucanase. These findings demonstrate that AEW mitigates postharvest deterioration of passion fruit by activating the metabolism of disease-resistant substances, highlighting its potential as an eco-friendly technology for maintaining quality during ambient handling/marketing conditions. Full article

In the context of the energy transition and the increasing deployment of low-carbon gases (hydrogen, biomethane), reliable analytical monitoring is required to support integrity assessment and traceability of gas infrastructures under diverse on-site conditions while limiting analytical costs through standardized sampling and a single analytical system. We developed and validated integrated workflows combining sampling and laboratory analysis for chemical and compound-specific isotope analysis (CSIA) of natural gas and associated gaseous effluents in underground storage. An original quantification approach was implemented, linking sampling pressure to the amount of each compound collected in vials, and coupled with δ¹³C and δ²H measurements of alkanes (C₁–C₃), CO₂ and H₂. Two complementary sampling modes were optimized and compared: conventional high-pressure cylinders and direct collection into vacuum-sealed vials suitable for a broad range of pressures and field conditions. Using reference gas mixtures and operational samples, both approaches showed good reproducibility and isotopic accuracy during laboratory validation and over two years of monitoring. In particular, δ²H determinations for alkanes and H₂ remained robust under low-pressure sampling typical of annular spaces (~1–2 bar), despite gas-composition fluctuations. These validated methodologies provide a flexible basis for routine, standardized monitoring of stored and circulating gases, including emerging low-carbon components. Full article