ABSTRACT Lithium is a much in‐demand element that is critical for the transition toward low‐carbon energy systems. Beyond hard‐rock mining, large quantities of lithium are extracted from brines of high‐Andean salars (salares). The functioning of these microbial ecosystems depends on a delicate equilibrium between mineralogy, water, physical conditions, and microbial life. They are polyextreme environments where life thrives under limiting conditions. In the Salar de Atacama, Chile, lithium is generally extracted through sequential brine evaporation, where lithium is enriched from ∼0.2 to ∼6% w/w. This process has been associated with significant environmental and social consequences, leading to growing interest in implementing new processes, such as direct lithium extraction (DLE), promoted as ‘environmentally friendly’ alternatives. However, this involves the use of chemical reagents and the reinjection of treated brines into the salar, which may have ecological implications that have not yet been fully evaluated. Here, we analyse the main DLE techniques and their potential consequences for microorganisms at community and cellular levels. We argue that DLE technologies, though presented as sustainable alternatives, could potentially have environmental impacts and that current lithium extraction practices continue to conflict with several Sustainable Development Goals.
ABSTRACT Flexible strain sensors face an unresolved trade‐off between sensitivity and sensing range. We present a meta‐material‐based sensor platform (meta‐sensor) that overcomes this by engineering local strain input to the sensing element. A strain‐stiffening curved spiral structure and a uniform‐stiffness kirigami‐patterned structure connected in series produce nonlinear strain partitioning: before mechanical transition, global strain concentrates at the sensor interface to amplify sensitivity; after transition, strain redistributes to extend sensing range. Integrating a kirigami crack‐based sensor (maximum applied strain range of 50%) onto this composite substrate extends the operational range to 125% applied strain‐a 2.5× range extension (from 50% to 125%) — while raising the mid‐range response to ≈3.5× that of a wide‐range reference sensor. The platform is validated in respiratory monitoring across rest and vigorous exercise, and knee rehabilitation tracking from early to late recovery. This meta‐material substrate approach offers a generalizable, material‐independent strategy for simultaneously achieving high sensitivity and wide sensing range.
Carbon dioxide conversion into value-added products offers a promising strategy to mitigate greenhouse gas emissions while enabling sustainable fuel production. Among the available approaches, photocatalytic CO2 reduction has attracted significant attention, particularly when combined with advanced catalyst design and reaction engineering. In this work, Cu-doped SrTiO3 is employed as a model photocatalyst to systematically investigate the influence of reaction phase on CO2 reduction performance across three distinct reaction configurations: solid-liquid (SL), solid-gas (SG), and pressurized solid-gas (PSG). The results demonstrate that the reaction phase strongly affects both activity and product selectivity. The SG configuration exhibits the highest performance, reaching accumulated yields of 11.13 µmol g-1 of CH4 and 2.32 µmol g-1 of CO after 4 h of reaction, owing to enhanced reactant accessibility and reduced mass-transfer limitations. In contrast, the SL configuration shows the lowest gaseous-product activity due to limited CO2 solubility and accumulation of dissolved intermediates, while favoring the formation of oxygenated liquid products such as methanol and ethanol. The PSG system displays intermediate performance, where increased CO2 availability is partially offset by hindered product desorption. These findings highlight reaction-phase engineering as a key factor governing photocatalytic CO2 conversion efficiency and selectivity.
The increasing burden of persistent organic pollutants, per- and polyfluoroalkyl substances, heavy metals, and emerging contaminants represents a global threat to human, animal, and ecosystem health. Current management strategies remain fragmented and reactive, limiting the effectiveness of the One Health paradigm. This review introduces an artificial intelligence (AI)-powered One Health framework designed to transition from surveillance to predictive prevention. Using a literature review across PubMed, Scopus, Google Scholar, and Web of Science (up to October 2025), recent advances in machine learning, deep learning, geospatial AI, and physics-informed models for chemical risk assessment are synthesized. The proposed framework integrates four core capabilities: (1) geospatial AI for real-time source tracking and fate modeling, (2) multispecies exposome reconstruction for unified exposure assessment, (3) predictive toxicology engines enabling cross-species risk extrapolation, and (4) AI-driven optimization of remediation and policy scenarios. Ethical and governance challenges, including algorithmic bias, transparency, and environmental justice, are critically examined. By integrating the holistic vision of One Health with the predictive capabilities of AI, this framework advances the concept of "Precision Environmental Health", supporting anticipatory governance and promoting equitable interventions at a global scale.
Lithium is widely portrayed as a cornerstone of the global energy transition. Yet prevailing narratives conceptualize lithium primarily as a mineral resource while treating water impacts as secondary externalities. This Perspective argues that in arid and hyper-arid salt-flat systems, lithium extraction is fundamentally constrained by hydrological boundaries rather than by mineral availability or technological efficiency. Using the Salar de Atacama in northern Chile as an emblematic global case, we contend that brine-based lithium extraction constitutes a large-scale intervention in coupled hydrosocial systems, directly implicating a boundary for freshwater. We challenge techno-optimistic expectations surrounding efficiency improvements and emerging technologies such as direct lithium extraction, emphasizing instead cumulative impacts, persistent uncertainty, and uneven distributions of risk and benefit. Building on the water boundaries framework, we propose a shift toward water-bounded lithium governance grounded in basin-scale extraction limits, precaution under uncertainty, legal recognition of brines as water, and meaningful indigenous participation linked to extraction thresholds. Without re-centering water as the defining constraint, the energy transition risks reproducing environmental degradation and social injustice under the guise of sustainability.
ABSTRACT Given the increasing volatility of global energy markets, identifying previously unexplored resources like rosehip (Rosa Canina L.) is essential to diversify and strengthen the sustainable biodiesel feedstock portfolio. In this study, rosehip seed oil methyl ester (RSOME) was produced via transesterification at 50°C for 60 min using a 6:1 methanol‐to‐oil molar ratio and 1 wt.% KOH catalyst, achieving a biodiesel yield of 93.63%, and was experimentally evaluated in a four‐cylinder common‐rail CI engine over an engine speed range of 1500–3000 rpm under fixed‐load conditions. Combustion, performance, exergy, and emission characteristics were evaluated. RSOME exhibited a cetane number of 52.36. Among the tested fuels, B5 achieved the best overall performance, improving BSFC by 1.2%–5.5%, increasing exergetic efficiency by 1.6%–5.9%, and reducing exergy destruction by 1.3%–8.5% relative to diesel. B5 also exhibited speed‐dependent combustion behavior, slightly decreasing Pmax (up to 2.6%) at lower speeds but increasing it by 6.0% while reducing NOx emissions by 6.8% at 3000 rpm. However, the large‐scale utilization of RSOME as a biodiesel feedstock may be constrained by the use of rosehip seed oil in high‐value health and cosmetic applications. Future studies should investigate RSOME under varying load conditions and long‐term engine durability scenarios.
ABSTRACT Carbon capture and storage (CCS) technology aims to decrease atmospheric carbon dioxide (CO2) levels. Subsurface mineral carbonation is the safest method of CCS, where water‐dissolved CO2 is injected into the subsurface and reacts with mafic or ultramafic minerals that release cations (Ca2+, Mg2+, Fe2+), resulting in the formation of carbonate minerals. However, more research is needed to effectively implement CCS in basaltic reservoirs. In these experiments, it is shown how two different samples (basaltic glass and basaltic crystals) react to carbonation under identical conditions: a pH of around 6.5, reaction times of 2 and 5 days, temperatures of 100°C and 200°C, and absolute pressures from 70 to 85 bar(a) at the operating temperatures. The results show partial dissolution of mineral phases, formation of new alteration phases, and carbonate precipitation. This study aims to identify key variables to analyze in such experiments to evaluate the formation of CO and CH4 as an alternative reaction pathway during mineral carbonation of injected CO2 in basalts. Ultimately, this research aims to lay the groundwork for future studies on basalt carbonation and on how the CCS process might be influenced by CO2 reduction to CO and CH4.
ABSTRACT Solid‐state batteries (SSBs) offer high energy density and improved safety but remain vulnerable to hidden electro‐chemo‐thermo‐mechanical degradation, lithium dendrite formation, and cyber‐physical attacks that cannot be reliably detected using conventional battery‐management systems. This work presents a cyber‐physical digital twin framework for real‐time state estimation, dendrite‐risk prediction, and resilient control of SSBs. A physics‐regularized reduced‐order model integrated with Moving Horizon Estimation reconstructs unmeasurable internal concentration, potential, temperature, stress, and interfacial degradation states from limited terminal measurements. A physics‐informed Dendrite‐Risk Forecast Index (DRFI) is developed by combining stress evolution, current‐density variance, interfacial impedance growth, and thermal‐gradient severity to provide early degradation warning. Physics‐consistent anomaly detection identifies measurement manipulation and cyber‐attacks, while a DRFI‐aware Model Predictive Controller adaptively regulates battery operation to mitigate degradation. Simulation studies under normal operation, accelerated degradation, and cyber‐attack scenarios demonstrate state‐estimation errors of 5%–7% during normal cycling and less than 10% under accelerated ageing, 0.8–1.2 cycles of early degradation prediction, 40%–55% reduction in stress concentration, and attack detection within 80–110 ms. These results demonstrate that the proposed physics‐guided cyber‐physical digital twin provides an interpretable and computationally efficient framework for predictive battery management, degradation forecasting, and resilient operation of next‐generation solid‐state batteries.
ABSTRACT Yoghurt cup packaging has received increasing attention regarding design‐for‐recycling. Packaging technologies like the three‐component (3C) cup, consisting of a plastic cup, carton wrap and aluminum cover, reduce the amount of plastic needed. While individual components are well recyclable if consumers correctly separate and dispose of them after consumption, no data regarding consumer separation behavior of yoghurt cups is available. This study offers a detailed characterization of 3C cups and other yoghurt cup decoration types (2C) regarding their dismantling stage and content in mixed municipal solid waste and lightweight packaging waste in Vienna, Austria. The results show that 3C cups represent one of the largest shares of yoghurt cup decoration types, totaling over 42 Mio cups/year (645 t/year) in Vienna. Both 2C and 3C cups are better dismantled when separately collected. However, 95% of all cups land in mixed municipal solid waste and are unavailable for recycling. Separately collected 3C cups with carton wrap additionally hinder the correct sorting of cups in a sorting facility. Lacking participation of consumers regarding dismantling and correctly disposing of yoghurt cups can therefore compromise design‐for‐recycling efforts. Continued monitoring of yoghurt cup quality and content in different waste streams is recommended to better understand consumer behavior.
ABSTRACT The escalating integration of variable renewable energy sources has underscored the need for scalable, long‐duration energy storage (LDES) solutions to ensure power system reliability and resilience. Hydrogen (H2) based LDES is a rapidly emerging technology that offers substantial advantages in both retaining large volumes of energy for extended periods and facilitating integration across sectors (electricity, heat, transport). This review provides an extensive overview of H2‐based LDES technologies, focusing on the respective production routes, storage and reconversion methods, integration into overall systems, and optimization using digital tools. A systematic analysis of literature published between 2018 and 2026 is conducted to assess techno‐economic performance, life‐cycle environmental impacts, and reliability metrics, including loss‐of‐load expectation (LOLE) and effective load‐carrying capability (ELCC). The investigation underscores pivotal challenges related to efficiency losses, infrastructure preparedness, and operational complexity, while highlighting the significance of digital twins, artificial intelligence (AI), and market mechanisms in enhancing system performance. Prospective research avenues and policy implications are examined to support the large‐scale implementation of H2‐based energy systems in alignment with Sustainable Development Goal 7.
ABSTRACT As renewable energy deployment grows and silicon solar cells approach their efficiency limits, perovskite solar cells (PSCs) emerge as a promising next‐generation photovoltaic technology. PSCs environmental impacts are assessed via life cycle assessment (LCA), which depends on the availability of high‐quality life cycle inventories (LCIs). In this study, we systematically identified 101 LCIs related to PSC materials, aiming to recommend the most reliable among them. However, we found that all inventories rely on secondary data and frequently omit critical details such as production scale. We also reproduced reported inventories and found large discrepancies in environmental impacts—sometimes differing by several orders of magnitude across sources. These inconsistencies, coupled with poor documentation, prevented the identification of a single best inventory for any material. Instead, we recommend the use of the most detailed inventories characterized by the highest number of inventory flows as a basis to build more transparent inventories. Our findings demonstrate how gaps in transparency, documentation, and reproducibility in PSC materials inventories impede decision‐making and erode confidence in LCA results. To address these issues, eleven steps are proposed when developing LCIs for emerging materials.
ABSTRACT Energy poverty remains a critical barrier to sustainable development, particularly in emerging and developing economies. Whereas solar technologies enjoy a positive public perception compared to fossil fuel infrastructure, the phenomenon of ‘green first’ or support for renewable energy can mislead government officials or policymakers into believing that social acceptance is not a key issue when deploying innovative renewable energy projects. In spite of technological advances that have progressed in the field of perovskite solar cells (PSCs), critical ethical and environmental problems surrounding their application remain unresolved. Although, significant progress with PSCs, attention has focused on performance and scalability; few studies have integrated PSC toxicity, recycling, and environmental impacts within the energy justice framework applied to sub‐Saharan Africa, Indo‐Pacific regions, Latin America and other parts of the world. This review synthesizes evidence‐based PSC environmental risks, recycling feasibility, and circular economy models, and links to potential impacts on equitable energy access in the context of environmental vulnerability and eco‐safe resource constraints. The key findings show that while PSCs can dramatically reduce the cost of decentralised solar energy and enhance energy access, they can also support successful transitions that achieve the procedural, recognition, and equitable distribution and inclusivity necessary for energy justice globally.
ABSTRACT The sandalwood industry remains constrained by destructive, time‐intensive assays for essential oil (EO) yield, composition, moisture content, and wood fraction, which limit real‐time decision‐making. We report a unified near‐infrared spectroscopy‐artificial intelligence (NIRS‐AI) platform for non‐destructive analytics across the Santalum album L. value chain. Reflectance and transmittance spectra from solid matrices (disks, logs, chips, and powders), oils, ethanol extracts, and CID‐derived emulsions were acquired using benchtop (400–2500 nm) and portable (900–1700 nm) spectrometers and calibrated against hydrodistillation, gas chromatography, extraction, and moisture assays. Advanced chemometric modelling using AI‐based machine learning techniques, including regularised regression, ensemble learning, boosting algorithms, and neural networks, was used to capture nonlinear spectral–property relationships and benchmark application‐specific predictive performance. Independent external validation yielded R2 values of 0.97 for EO yield, 0.96 and 0.94 for α‐ and β‐santalol, 0.99 for the heartwood‐sapwood ratio, 0.86 for oil moisture, 0.98 for ethanol extract yield, and 0.94 for portable emulsion‐yield prediction. NIRS also outperformed visible spectra for classifying heartwood, sapwood, inner and outer bark, and transition wood. ΔR2 analysis showed that ensemble models were comparatively robust to preprocessing variation, whereas linear models were more sensitive to changes in the variance structure.
ABSTRACT Biocarbons are carbon‐rich materials produced from biomass and are increasingly used as sustainable alternatives to fossil‐based materials for energy and environmental applications. Their production for commercial usages requires high temperature (above 2000°C), which calls for particular attention on energy consumption and environmental impact. Hydrothermal carbonization, selected as prestructuration step, produces small graphitic units with 6 stacked layers of 2.34 nm diameter. Further pyrolysis at 800°C increases layer length and the number of small graphitic units, giving an ideal graphitization precursor. Conventional and concentrated solar carbonization are compared in the graphitization step. At 1800°C, conventional carbonization leads to a turbostratic structure with a quality of graphitic domains characterized by low interlayer spacing (d002 = 0.357 nm) and long defective graphene layers (La(XRD) = 5.81 nm, La(Raman) = 3.17 ± 0.46 nm). Solar carbonization at 1800°C also yields long defective graphene layers, but the structure is heterogeneous with both a turbostratic (Lc = 2.41 nm, d002 = 0.368 nm) and a graphitic (Lc = 5.58 nm, d002 = 0.345 nm) phase. Even though solar carbonization relies on an intermittent energy source, it enables higher graphene layer stacking biocarbon as compared to conventional carbonization route.
ABSTRACT Exploring mitigation solutions is important for supporting heat‐resilient urban designs. Shading stations offer a practical means of blocking direct solar exposure and improving the thermal environment of the station. This study aimed to compare the heat mitigation performance of fabric and photovoltaic (PV) canopy shading in terms of microclimate regulation and outdoor thermal comfort improvement based on field experiments in subtropical Jiangsu, China. The results indicate that artificial shading could significantly reduce the universal thermal climate index (UTCI), with average daytime reductions of 3.5°C for fabric shading and 3.1°C for PV canopy shading. Although both types of shading stations were effective, they exhibited distinct mechanisms of action. Fabric shading provided more consistent cooling and radiation reduction at the ground level (0–0.5 m). During the full‐shading period (10:30–13:30), PV shading showed stronger downward longwave radiation, resulting in a weaker mitigation performance at pedestrian height (1.0–1.5 m) than that of fabric shading. Furthermore, the shading‐induced mitigation performance can be affected by underlying surfaces. Grass surfaces enhanced cooling via evapotranspiration, whereas high‐albedo hard surfaces sometimes contributed to heat accumulation. The differences in vertical temperatures verified the different mitigation mechanisms of the two types of shading stations along their height. Overall, this study provides a reference for designing artificial shading.
ABSTRACT Resource availability, poverty, and underdevelopment are bottlenecks of modernization in rural communities. This study proposes a rural electrification and development strategy through deploying agrivoltaics (AV) systems on rainfed maize farms in rural Mexico. As an alternative to maize monoculture, we proposed lettuce, tomato, spinach, and potato in a crop rotation system. The strategy was evaluated through techno‐economic and sustainability assessments under different adoption scenarios (5%, 10%, and 15% conversion rates of rainfed maize fields) and household energy demand profiles. Under the baseline energy demand growth rate, the results demonstrate that AV adoption can substantially enhance the net present value (NPV) of the studied municipality. Specifically, compared to the business‐as‐usual scenario's value of 86.7 million MXN, NPV increases to 162.4, 230.0, and 297.4 million MXN under the 5%, 10%, and 15% AV adoption scenarios, respectively, driven by the combined revenues generated from power sales and agricultural production. The photovoltaic component generates clean energy with a levelized cost of electricity (LCOE) of 837 MXN/MWh (47.4 USD/MWh), while diversifying the cropping system enhances resilience against market and climate shocks.
ABSTRACT Trade cooperation is crucial for socioeconomic development and environmental sustainability. Countries connected by the Belt and Road Initiative (BRI) account for 54% of the world's primary energy supply, and most countries along the Belt and Road (B&R) region are developing economies, typically facing the dilemma of economic growth and environmental protection. Based on the global multi‐regional dynamic computable general equilibrium model (C3IAM3.0/GEEPA), this study systematically simulates the impact of trade cooperation in the B&R region on the distribution of economic and environmental effects from a global economy‐wide perspective. Results show that trade liberalization fosters trade growth and enhances residents' welfare. However, it is also found that foreign direct investment has positive effects on economic growth in the mid‐and‐long term but also exacerbates environmental losses. Finally, the effectiveness of a customized carbon reduction policy is verified in this study, providing an opportunity to achieve the balance between trade cooperation and environmental protection, inspiring governments to develop a more desirable combination of policies, instead of treating economic growth and environmental protection as mutually exclusive.
ABSTRACT Internal combustion engine (ICE) is still the major source of energy for transportation systems worldwide. However, their environmental impacts continue to be a major issue, especially in developing countries like India. Since there is a lack of environmental evaluation of ICE technologies under Indian operating conditions, this study presents a comprehensive Life Cycle Assessment (LCA) of a newly developed diesel engine under Indian operating conditions. The assessment follows the ISO 14040 framework and employs the ReCiPe Life Cycle Impact Assessment (LCIA) methodology across production, operation, and end‐of‐life (EoL) phases. The operational phase is considered the major contributor to environmental degradation, mainly because of the consumption of diesel fuel. When neat diesel is used as the fuel, the highest impact is observed for Climate Change Potential (CCP) with 363,513.60 kg CO2‐eq, followed by fossil resource scarcity with 58,358.51 kg oil‐eq and Acidification Potential (AP) with 18,193.93 kg SO2‐eq. Photochemical Ozone Formation Potential (POFP) and Fine Particulate Matter Formation (FPMF) also present significant impacts with 259.16 kg NOx‐eq. and 501.45 kg PM‐eq., respectively. The results emphasize the need for new fuel alternatives like biodiesel, technological solutions like hybridization and proper policy measures for successfully reducing environmental emissions associated with ICE.
ABSTRACT As Denmark continues to move towards a more decarbonized future, Power‐to‐X (PtX) technologies are becoming an important part of green energy and fuel. These technologies also have significant impacts on land and social equity. This study combines spatial analysis, qualitative case studies, and stakeholder mapping to analyze how PtX expansion interacts with land allocation and existing socio‐economic inequalities within Denmark. Spatial mapping using geographical information systems (GIS) reveals that PtX infrastructure is more heavily sited in rural areas, heightening competition for land, environmental impacts, and economic impacts on local communities. These burdens affect rural communities more than urban ones. Case studies of three PtX facilities highlight how different planning and community engagement strategies produce varying outcomes. Stakeholder analysis identifies key tensions between developer actions and public response. Findings show that, while PtX can improve Denmark's energy security, a proactive policy is needed to ensure benefits are shared equally and community concerns are addressed. Recommendations include mandating early community engagement, prioritizing existing industrial sites, strengthening local benefit‐sharing programs, enhancing environmental and social impact assessments, and expanding community education. This approach is crucial for Denmark to integrate PtX at scale while addressing spatial and socio‐economic tradeoffs.
Environmental sampling of fungal spores is critical for assessing exposure risks, but current methods often miss low-abundance or spatially dispersed spores, highlighting the need for more sensitive sampling methods. This study explores the use of plasma polymerization to chemically modify air filters for enhanced fungal spore capture. Polyethylene terephthalate (PET) filters are coated with nanothin films from four monomers -acrylic acid, 2-methyl-2-oxazoline (POX), 1,7-octadiene, and perfluorooctane (PFO)- and characterized using ellipsometry, X-ray photoelectron spectroscopy, and contact angle measurements to evaluate film thickness, chemistry, and wettability. A custom aerosolization chamber was used to test the capture efficiency of plasma-modified filters for airborne spores from four species: Aspergillus niger, Cladosporium sp., Penicillium roqueforti, and Rhodotorula glutinis. Quantitative analysis using hemocytometry and dry biomass measurement reveals species-specific adhesion patterns that are predominantly driven by surface chemistry. Hydrophobic PFO-coated filters achieved the highest capture of filamentous fungi, while hydrophilic POX coatings best captured the tested yeast. Coating thickness had no significant effect, highlighting the primacy of surface chemistry over film depth. These findings establish plasma polymerization as an effective strategy to tailor filter surfaces for selective fungal spore capture, providing a proof-of-concept for functionalized air filters that support improved bioaerosol monitoring in built environments.
