Search Results (307)

Hydrogen produced through electrochemical water splitting is considered one of the most promising energy carriers for achieving a sustainable and carbon-neutral future. However, the practical implementation of water electrolysis remains limited by the sluggish kinetics of the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER), as well as the high cost and limited availability of conventional noble-metal catalysts. Single-atom catalysts (SACs), which feature isolated metal atoms anchored on suitable supports, have emerged as an attractive class of electrocatalysts owing to their nearly complete atomic utilization, well-defined active sites, and tunable electronic structures. This review provides a comprehensive overview of recent advances in SACs for electrochemical water splitting. The fundamental mechanisms of HER and OER are first discussed, followed by the influence of the unique electronic structure, coordination environment, and metal–support interactions on catalytic performance. Various bottom-up and top-down synthesis strategies, together with advanced characterization techniques for identifying atomically dispersed active sites and elucidating structure–activity relationships, are systematically summarized. Furthermore, recent progress in noble-metal, non-noble-metal, and dual-atom catalysts is critically reviewed, with emphasis on their roles in regulating electronic structure, reaction intermediate adsorption, catalytic activity, HER/OER kinetics, and long-term stability. Finally, the remaining challenges and future perspectives for the scalable and practical application of SACs in water electrolysis are discussed. Overall, this review highlights the potential of SACs to maximize metal utilization while maintaining high electrocatalytic performance and provides valuable insights for the rational design of next-generation electrocatalysts for sustainable hydrogen production. Full article

Atomic layer deposition (ALD) has become a versatile technique for atomic-level material synthesis and surface modification, with broad relevance in applications such as energy storage devices, integrated circuits, and membranes. The deposition is governed by a multitude of factors spanning multiple length/time scales, ranging from macroscopic transport phenomena to atomistic surface reactions. Owing to the intricate process coupling across these scales, a comprehensive evaluation of atomistic behavior under flow conditions remains highly challenging. To address this issue, a multiscale integrated simulation framework is developed in which precursor transport within high-aspect-ratio nanochannels is explicitly simulated using a finite volume method (FVM) and subsequently coupled with kinetic Monte Carlo (kMC) simulations to evaluate film growth characteristics. Based on the FVM results, the spatiotemporal distribution of precursor partial pressures along nanochannels is extracted and employed as input for a lattice-based kMC model, enabling atomistic resolution of the ALD process over the full deposition cycle. By coupling these two scales, key deposition metrics including step coverage and its temporal evolution under varying partial pressure conditions are quantitatively obtained, together with atomic-level smoothness and vacancy ratio. The kMC simulations further enable detailed visualization of film growth behavior on substrates under different precursor partial pressure environments. The results indicate that, within the microstructure, even when the overall precursor exposure is comparable, films formed near the entrance exhibit superior surface structure, as evidenced by higher atomic-level smoothness and reduced vacancy ratio, compared to those at the bottom. This study provides an engineering-feasible framework for evaluating atomistic film properties in ALD processes by consistently integrating fluid transport and surface reaction kinetics. Full article

Global environmental change, biodiversity loss, emerging infectious diseases, antimicrobial resistance, and widening social inequalities increasingly interact to create complex risks for human, animal, and ecosystem health. Although international One Health frameworks and funding mechanisms are essential, implementation at national and especially community levels often remains uneven, fragmented, and weakly evaluated. This Perspective presents a structured, reproducible networking model designed to strengthen community-level One Health implementation through voluntary global collaboration among civil society organisations. The model connects bottom-up community mobilisation with interdisciplinary scientific support, shared evaluation templates, and iterative learning cycles. It outlines a transparent process for identifying and engaging One Health-oriented organisations and for organising their collaboration through thematic Project Working Groups that support situation analysis, strategic planning, implementation, evaluation, and dissemination. The framework translates community action into seven intervention fields: surveillance and early warning, animal health and welfare, water/sanitation/hygiene, food safety and food systems, community awareness and education, environmental risks and ecosystem health, and multisectoral governance. By linking local practice to comparative synthesis, joint publication, and policy dialogue, the network aims to transform isolated initiatives into a coordinated learning system while preserving community and organisational autonomy. As a conceptual framework, the model is ready for empirical testing in diverse settings to assess feasibility, participation, and evaluation quality. Full article

Industrial sustainability assessment needs a transparent and operational framework that can manage multidimensional indicators, expert uncertainty, weighting complexity, and managerial interpretation. This research presents the Sustainable Industrial Measurement (SIM) Model, an AI-assisted sustainability intelligence architecture for manufacturing organizations. The model comprises literature-based indicator synthesis, Fuzzy Delphi Technique (FDT), Pareto 80/20 screening, Group Analytic Hierarchy Process (Group AHP), Utility Value Analysis and the web-based AI-enabled decision support system (AI-DSS) under the Triple Bottom Line (TBL) framework. In FDT validation by consensus, threshold and fuzzy score criteria, 33 experts accepted 64 indicators. The Pareto screening reduced the set to 50 high-impact indicators, consisting of 10 economic, 22 social and 18 environmental indicators. The priority weights were derived from the group AHP weighting by 21 experts and checked for consistency. The environmental and economic indicators represent the dominant sustainability priorities. The weighted structure was embedded in the web-based AI-DSS to enable automated scoring, visualization, gap diagnosis and AI-based managerial recommendations. Thirty industrial practitioners reported excellent perceived usability of the SIM Model, with a System Usability Scale score of 86.0. However, the evaluation assessed usability only, not the accuracy, effectiveness, or organizational impact of AI-assisted recommendations for manufacturing sustainability decisions and future implementation. Full article

Carbon dots (CDs) have been widely employed in diverse fields by virtue of their excellent water solubility, low toxicity, high fluorescence stability, and favorable biocompatibility. Nevertheless, traditional preparation methods for CDs generally suffer from drawbacks that run counter to the concept of green chemistry. This review comprehensively summarizes the green synthesis technologies, machine learning (ML)-assisted synthesis strategies, and diversified application fields of fluorescent CDs. Specifically, it discusses the characteristics of synthetic organic molecular/polymeric materials and natural sources (e.g., plants and fruit peels, etc.) and elaborates on the top-down and bottom-up green synthesis methods, analyzing their advantages. It also focuses on ML’s core role in precisely regulating CD emission wavelengths, enhancing and predicting fluorescence quantum yields to optimize synthesis processes. Additionally, this review highlights the representative biological applications of CDs, including biosensing and biomedicine (e.g., bioimaging, drug delivery, and photodynamic therapy), while briefly covering their applications in other fields. Finally, the review points out current challenges in green synthesis, ML-assisted applications and industrial translation, and puts forward future research directions, aiming to promote the greenization, intellectualization and large-scale development of CDs. Full article

The aim of this review is to provide a critical synthesis of peer-reviewed literature focusing exclusively on MSWI, rather than the broader field of Waste-to-Energy, based on a search in Scopus and a structured narrative synthesis. The methodology comprised eight Scopus queries defined for the main analytical axes of MSWI, deduplication, screening according to the established eligibility criteria, a layered corpus design, and domain-specific weighting of evidence within the framework of a structured narrative synthesis. This yielded 5435 unique records after deduplication, from which the main time window of 2010–2026 and a layer of publications from 2019 to 2026 were extracted. The review shows that the net balance of MSWI does not result from a single parameter or a single evaluation metric, but from the interplay between feedstock variability, combustion management, air pollution control (APC) configuration, residue management, and the utilisation of recovered heat and energy. Modern APC systems have reduced stack emissions, but do not eliminate the significance of transient states or the transfer of pollutants to fly ash and APC residues. Bottom ash exhibits conditional potential for material and metal recovery, whilst fly ash and APC residues remain the main constraint on recovery pathways. Environmental, climatic, health and economic assessments remain highly sensitive to system boundaries, functional units, counterfactual scenarios, the local energy mix, the quality of exposure reconstruction and integration with district heating. The added value of the review lies in maintaining MSWI as the sole analytical core and integrating the process, emissions, residues and system assessments within a single interpretative framework focused on comparability, trade-offs and the MSWI system balance. Full article

Acetylcholine (ACh) is an evolutionarily conserved neurotransmitter that is widely distributed in the central and peripheral nervous systems and plays essential roles in multiple physiological processes. This review summarizes the full biological cycle of ACh, including its synthesis, vesicular storage, release, degradation, and reuptake, and discusses the regulatory mechanisms underlying its functions in the nervous system and peripheral organs. Through nicotinic acetylcholine receptors (nAChRs) and muscarinic acetylcholine receptors (mAChRs), ACh is involved in central nervous system functions such as cognition, learning and memory, attention, arousal, reward, and decision-making, as well as peripheral processes including motor control, autonomic regulation, and immune modulation. In addition, ACh plays a pivotal role in the brain–body axis. At the central level, the nervous system regulates peripheral organ function through autonomic and neuroendocrine pathways. At the peripheral level, cholinergic signals derived from the enteric nervous system and immune cells convey information about the body’s internal state to the central nervous system through vagal and other afferent pathways, forming an important bottom-up regulatory network. Collectively, these findings indicate that ACh is not only a classical neurotransmitter but also a key molecular mediator of brain–body communication. A more comprehensive understanding of cholinergic signaling may provide new insights into physiological regulation and the pathogenesis of neurological, psychiatric, cardiovascular, and inflammatory diseases. Full article

Carbon dots (CDs), including carbon quantum dots (CQDs), are ultra-small carbon-based nanomaterials, typically below 10 nm, with tunable photoluminescence, high aqueous dispersibility, favorable biocompatibility, low toxicity, and abundant surface functional groups. These properties make CDs promising multifunctional platforms for nanomedicine, particularly in bioimaging, biosensing, targeted drug/gene delivery, photodynamic therapy (PDT), photothermal therapy (PTT), antimicrobial treatment, and theranostic applications. This review critically examines recent advances in CD fabrication, including top-down, bottom-up, green biomass-derived, microwave-assisted, hydrothermal, and emerging hybrid strategies, with emphasis on how precursor selection, heteroatom doping, surface passivation, and polymer/ligand functionalization regulate optical performance, biological interaction, and therapeutic efficiency. The review discusses structural classification, including CQDs, graphene quantum dots (GQDs), carbon nanodots, and carbonized polymer dots (CPDs), together with major characterization approaches such as ultraviolet–visible (UV–Vis) spectroscopy, Fourier-transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, and high-resolution transmission electron microscopy (HRTEM). Particular attention is given to red/near-infrared (NIR) emission, renal clearance, drug-loading behavior, reactive oxygen species (ROS) generation, toxicity mechanisms, biodistribution, and long-term biosafety. This review also highlights key translational barriers, including batch-to-batch variability, limited standardization, scalable manufacturing, regulatory uncertainty, and incomplete pharmacokinetic evaluation. It considers artificial intelligence (AI) and machine learning (ML) as emerging tools for reproducible CD design. CDs represent versatile and clinically promising nanoplatforms, but their translation requires standardized synthesis, rigorous safety assessment, and application-specific regulatory validation. Full article

The objective of this study was to examine the impact of using a rotary jet head (RJH) on the biosynthesis of byproducts of yeast metabolism and their role in shaping the flavor and aroma profile of bottom fermentation beer (lager style). The tests were conducted on an industrial scale, with fermentation in 3800 hL fermentation tanks. Experiments were conducted in a minimum of six replicates. The main quality indicators, including ethanol concentration and pH, were analyzed, along with key volatile compounds such as acetaldehyde, esters, higher alcohols, and DMS. Additionally, beer samples—both those fermented using forced mixing and those produced conventionally—were subjected to sensory evaluation. The study found that RJH did not cause changes in either the final ethyl alcohol concentration (6.74% in both samples) or the pH measurement results. The rotary jet head increased synthesis of certain volatile components, such as fusel alcohols by 5% and acetate esters by 14% for ethyl acetate and by almost 12% for isoamyl acetate. On the other hand, a more than threefold (8.23 to 2.54 mg/L) decrease in the undesirable acetaldehyde was observed in samples fermented with forced mixing. The resulting beers exhibited statistically significant differences in chemical composition; however, sensory analysis did not reveal these differences. This finding underscores the efficacy of the rotary jet head in expediting the beer production process without compromising its sensory quality. Full article

The rapid evolution of Internet-enabled smart systems has accelerated the adoption of the Internet of Things (IoT), Cyber–Physical Systems (CPS), Big Data, Artificial Intelligence (AI), and Human–Computer Interaction (HCI/AR–VR) across distributed digital ecosystems. Despite these advances, the architectural integration of sensing, information processing, and system-level reasoning remains fragmented, limiting system coherence and accountability. This study derives an architectural foundation through a systematic synthesis of smart system architectures. An activity-mediated socio-technical perspective is employed to analyze diverse paradigms—including IoT-centric frameworks, AI-driven infrastructures, digital twins, Big Data pipelines, and cyber–physical systems—as well as reference architectures such as RAMI 4.0, IIRA, and other representative smart system architectures. Here, activity-mediated denotes an architectural mediation mechanism that coordinates sensing, data-driven reasoning, and human–AI interaction. The synthesis reveals a lack of explicit mechanisms for vertical integration and alignment between bottom-up data flows and top-down goal propagation. In response, this study derives three architectural pillars that integrate interaction, governance, and smart technologies. Their operationalization reveals a structured transformation process in which activity-derived signals are translated into actionable intelligence and adaptive interventions, enabling feedback-driven behavior and cross-layer traceability. Full article

The agricultural sector in Mexico, specifically the coffee and sugarcane supply chains, faces the critical challenge of reconciling economic profitability with environmental sustainability and rural social progress. This study presents a critical literature review and conceptual framework that evaluates existing analytical models and proposes methodological integration pathways to simultaneously optimize Triple bottom line (TBL) dimensions in vulnerable smallholder systems. Unlike prior reviews that focus on generic Sustainable Supply chain management (SSCM) practices, this work explicitly addresses the suitability and limitations of multi-objective optimization (MOO) and Life cycle assessment (LCA) for regenerative supply chain objectives in the Mexican coffee and sugarcane context. A critical review of 76 core articles published between 2020 and 2025 was conducted, employing comparative evaluation criteria and narrative synthesis to assess trade-offs, data requirements, and scalability constraints. The review reveals that while agricultural intensification often exacerbates environmental degradation, the adoption of sustainable practices can impose significant financial burdens on vulnerable smallholders. However, analytical models like MOO and LCA serve as robust decision-support systems that effectively evaluate trade-offs and balance competing economic, environmental, and social objectives by identifying optimal production scenarios. The contribution of this work is threefold: (1) a critical synthesis distinguishing regenerative from sustainable supply chain paradigms, (2) a comparative assessment of analytical model applicability to smallholder contexts, and (3) a conceptual framework integrating local socioeconomic realities, traditional knowledge, and modern technological approaches. Fostering resilient supply chains in Mexico requires customized analytical frameworks that explicitly operationalize social indicators, address data limitations, and enable cross-sector collaboration. Ultimately, localized models are essential to simultaneously enhance rural livelihoods, reduce carbon footprints, and maintain economic viability. Full article

Graphene quantum dots (GQDs) have emerged as a promising class of carbon-based nanomaterials owing to their unique optical properties, tunable surface chemistry, excellent biocompatibility, and high physicochemical stability. These features make GQDs particularly attractive for the development of advanced biosensing platforms. This review provides a comprehensive overview of recent progress in the design, synthesis, and functionalization of GQDs, with a primary focus on their applications in biomedical and biosensors. Various synthesis approaches, including top-down, bottom-up, and chemical methods, are critically discussed in relation to their impact on structural and optical properties. The role of surface engineering and heteroatom doping in modulating sensitivity, selectivity, and signal transduction mechanisms is also highlighted. Furthermore, recent advances in GQD-based biosensors for the detection of clinically relevant biomarkers, environmental analytes, and pathogens are systematically summarized, with emphasis on analytical performance metrics such as sensitivity, selectivity, and limit of detection. In addition, complementary biomedical applications, including bioimaging and therapeutic platforms, are briefly discussed to provide a broader context for the multifunctionality of GQDs. Finally, current challenges and future perspectives toward the rational design of high-performance GQD-based biosensors are outlined. Full article

Methane (CH₄) is a potent greenhouse gas, and accurately estimating its global budget is essential for climate change mitigation. This review provides a comparative synthesis of top-down, bottom-up, and integrated approaches for quantifying methane emissions and sinks, with a particular focus on the role of remote sensing. Top-down methods, leveraging satellite observations from instruments like GOSAT and TROPOMI within atmospheric inversion frameworks (Bayesian, 4D-Var), provide observationally constrained, spatially integrated fluxes, reducing global budget uncertainty to ±5–10%. However, they face challenges in source attribution and rely heavily on transport model accuracy. Conversely, bottom-up approaches, including process-based models (e.g., CLM, DNDC) and emission inventories (e.g., EDGAR), offer detailed, sector-specific insights but are prone to underestimating emissions from super-emitters and diffuse sources like wetlands, with uncertainties often exceeding ±20–40% for individual sectors. Key persistent discrepancies between the two approaches are largest for natural sources (e.g., a 20–40 Tg yr⁻¹ gap for tropical wetlands). Integrated approaches, which synergize top-down atmospheric constraints with bottom-up inventory data, are emerging as the most robust methodology, effectively narrowing the global budget gap and improving confidence. Recent advancements in satellite missions (e.g., MethaneSAT), machine learning algorithms for plume detection, and high-resolution inversion models are transforming monitoring capabilities. However, challenges remain in harmonizing datasets, representing complex microbial processes in models, and expanding observational coverage in data-scarce tropical regions. This review concludes by outlining a future path centered on hybrid inversion frameworks, AI-driven source attribution, and cross-disciplinary collaboration to deliver the actionable methane budgets needed for effective climate policy. Full article

The Rio Grande Rise (RGR) is the largest oceanic plateau in the South Atlantic and represents a key natural laboratory for understanding oceanic plateau formation, deep-sea circulation, ecosystem functioning, and ferromanganese crust development. This study presents a critical synthesis of current scientific knowledge on the RGR, integrating geological, geophysical, oceanographic, biological, and geochemical evidence published over the last two decades. Geophysical data reveal a complex tectono-magmatic evolution involving Late Cretaceous plume-related volcanism, crustal thickening, rifting, and subsequent subsidence. The structural framework of the plateau is dominated by the Cruzeiro do Sul Rift, which plays a central role in controlling sedimentation, magmatism, and seawater circulation. Oceanographic studies demonstrate that the interaction between the southern branch of the South Equatorial Current and the complex topography of the RGR generates intense internal tides and bottom currents, strongly influencing sediment transport and benthic habitats. Biological investigations indicate that the RGR hosts diverse deep-sea communities, including sponge grounds, cold-water corals, and associated fauna, whose distribution is tightly linked to geomorphology and hydrodynamics. Ferromanganese crusts occurring on the plateau preserve valuable geochemical records of oceanographic and redox conditions, although their spatial distribution, thickness, and metal budgets remain incompletely constrained. Despite major advances, significant knowledge gaps persist regarding crustal structure, sedimentary evolution, ecosystem functioning, and mineral formation processes. This review highlights these uncertainties and outlines research priorities necessary to improve understanding of oceanic plateaus and deep-sea systems in the South Atlantic. Full article

We report the synthesis of Fe/N/C ORR electrocatalysts by an original collinear CO₂ laser pyrolysis of liquid aerosol droplets in various configurations and compared them to a catalyst synthesized in the classical perpendicular one. While the precursors were always injected at the bottom side of the reactor, two collinear configurations of the laser entry into the reactor are considered: by the Top Side (T.S.) or by the Bottom Side (B.S.). The two corresponding catalysts sets show significant different ORR performances. An in-depth XPS analysis and fitting of the N1s spectra allowed for drawing the ORR performance as a function of FeN_x sites components. An original approach considering the energy delivered to a quantity of precursors in J.g⁻¹, linked to the flame temperature feature, evidenced very different conditions for perpendicular CO₂ laser pyrolysis and each of the two collinear configurations. This mass-weighted energy delivered in the classical perpendicular configuration is too low to allow for the formation of FeN_x sites and the resulting ORR performance is extremely poor, suggesting a marginal role of nitrogen species without interaction with iron atoms. In contrast, the delivered mass-weighted energies are sufficient in both collinear configurations to produce FeN_x sites. The ORR performance for catalysts produced in these both configurations is positively correlated with the amount of energy deposited on the precursors. The ORR performance in the T.S. laser configuration is positively correlated to the amount of FeN_x sites. The best performing catalysts obtained in the B.S. configuration show an opposite variation. These trends, and the ORR performance degradation of B.S. catalysts under prolonged chronoamperometry are discussed in light of the effect of temperature on the formation of the various kind of FeN_x sites. A tentative explanation is given, considering that N1s XPS fitting with a single FeN_x component may hinder the fact that Pyridinic sites components may contain a part of FeN_x sites, as suggested by theoretical calculation from the literature. The best catalysts obtained in this work by collinear configuration show similar performances to those obtained by double stage perpendicular pyrolysis previously reported with an ORR onset potential of ~860 mV. Full article