Search Results (4,085)

Rhodium is a high-value strategic platinum-group metal extensively applied in automotive exhaust purification, fine chemicals, glass production and high-temperature materials. Restricted by uneven primary resource distribution and volatile market prices, recovering rhodium from secondary resources has become increasingly critical. Solvent extraction is regarded as a promising technology for continuous and selective separation of rhodium, yet direct extraction of Rh(III) from chloride media faces severe industrial limitations. These bottlenecks are mainly attributed to diversified chloro-aqua complexes, kinetic inertness of low-spin Rh(III), strong hydration capacity and polynuclear species generation, while solution aging and inconsistent thermodynamic-experimental results further complicate extraction behaviors. This review systematically summarizes recent advances in rhodium solvent extraction from chloride media, correlating aqueous speciation regulation, activation chemistry, extractant molecular structure and extraction-stripping mechanisms. Special emphasis is placed on SnCl₂-, ascorbic acid-, trichloroacetic acid- and malonate-assisted activation systems, as well as amine-, phosphorus-, sulfur-based, synergistic, ionic-liquid and deep-eutectic-solvent extractants. Key factors affecting extraction efficiency, distribution ratio, selectivity and stripping performance are clarified, and current challenges are outlined. Future research should focus on quantitative speciation analysis, in situ mechanistic characterization, targeted extractant design, and integrated evaluation of extraction, stripping, recyclability, cost and real-feed adaptability, so as to provide theoretical support for efficient and clean rhodium recovery. Full article

Water is fundamental to urban sustainability, structuring the urban water cycle from supply to wastewater treatment and discharge. Basic sanitation services are a core component of this system, directly influencing sustainable water use and environmental quality. Sanitation 4.0 applies Industry 4.0 technologies to enable real-time monitoring, data-driven management, and process optimization. This study investigates how the implementation of Industry 4.0 technologies transforms the management of basic sanitation services. A systematic literature review (SLR) was conducted to provide a theoretical foundation and identify research gaps. Articles were selected using a structured and reproducible method, and qualitative data were coded and analyzed with NVivo software. The results indicate that Sanitation 4.0 encompasses diverse applications, with artificial intelligence (AI), big data and data analytics, and internet of things (IoT) emerging as the most frequently implemented technologies in water distribution, wastewater treatment, and service management. IoT demonstrated broad versatility, while robots and augmented reality remain underexplored. Data security emerged as the area most in need of attention. This research concludes that Industry 4.0 technologies are reshaping the management and delivery of sanitation services, supporting innovation and progress toward universal access. Full article

One of the challenges apparent in the organisation of research projects is the uncertainties around the subject of demonstrators. A precise and detailed elicitation of the coverage for project demonstrators is often an afterthought and not sufficiently detailed during proposal writing. This practice leads to continuous confusion and a mismatch between targeted and achievable demonstration of results, hindering progress. The reliance on the Technology Readiness Level (TRL) scale as a loose descriptor does not help either. We propose a demonstrator requirements elaboration framework aiming to evaluate the feasibility of targeted demonstrations, making realistic adjustments, and assist in describing requirements. In doing so, we define five hierarchical levels of demonstration, clearly connected to expectations, e.g., work package interaction, and also connected to the project’s industrial use-cases. The considered application scope in this paper is the domain of software-intensive systems and industrial cyber-physical systems. A complete validation is not accessible, as it would require application of our framework at the start of a project and observing the results at the end, taking 4–5 years. Nonetheless, we have applied it to two research projects from our portfolio, one in the early stages and another in the final stages, revealing its effectiveness. Full article

This paper presents a comprehensive review on near-infrared (NIR) spectroscopy applied in food analysis, systematically elaborating its core principles, widespread industrial applications, advanced chemometric strategies, and cutting-edge technological progress. NIR spectroscopy (760–2500 nm), characterized by rapid, non-destructive detection and minimal sample preparation, has been widely implemented in quality evaluation and safety monitoring of grains, meat, fruits and vegetables, dairy, fermented products, tea, coffee, and other processed foods, realizing quantitative analysis of nutrients, freshness assessment, texture prediction, adulteration identification, origin tracing, and rapid preliminary screening of toxin/pesticide residues. A series of chemometric methods, including spectral preprocessing (SNV, MSC, S-G smoothing), feature extraction, and variable selection (CARS, PSO-CMW, ICPA), as well as linear/nonlinear modeling algorithms (PLS, SVM, BP-ANN, fuzzy clustering) significantly boost the accuracy and robustness of spectral analysis. Meanwhile, portable NIR devices and online monitoring systems promote on-site and real-time detection in food supply chains. Despite existing challenges such as calibration transfer, matrix interference, and model generalization, innovations like multimodal data fusion, deep learning integration, and intelligent algorithm optimization offer effective solutions. This review not only summarizes the latest research advances of NIR technology in the food field but also emphasizes its significant advantages as a rapid, non-destructive complementary tool to traditional destructive detection methods, providing theoretical support and technical reference for accelerating the industrial translation and standardized application of NIR spectroscopy, and ultimately safeguarding global food quality and safety. Full article

Background/Objectives: The increased prevalence of multidrug-resistant Escherichia coli and carbapenemase-producing Enterobacteriaceae poses a critical threat to global health and food safety. Antimicrobial Blue Light (aBL) in the 400–470 nm spectrum has emerged as a promising, chemical-free disinfection strategy that targets intracellular porphyrins and flavins to induce oxidative stress. However, the influence of wavelength, dosimetry, and environmental stressors on endogenous photoinactivation remains poorly standardized regarding optical parameters and biological exposure protocols. This systematic review aimed to evaluate the antimicrobial efficacy of pure blue light (400–470 nm) against E. coli across various phenotypes and environmental conditions, excluding the use of exogenous photosensitizers. Methods: PubMed, Scopus, and Web of Science were searched for studies that utilized 400–470 nm light as an antimicrobial agent against E. coli. Data extraction focused on spectral efficiency, total fluence (J/cm²), and log₁₀ reduction. The Risk of Bias was assessed using an adapted Office of Health Assessment and Translation tool for in vitro studies. Results: Synthesis of 11 high-quality studies indicated that wavelengths near 405 nm have the highest germicidal efficiency due to the Soret band absorption of endogenous porphyrins. Efficacy is highly dose-dependent: significant log₁₀ reductions were achieved in planktonic cells, although biofilms required substantially higher fluences. Sub-lethal environmental stressors such as acidic pH, high salinity, and thermal fluctuations demonstrated a synergistic effect, which significantly enhanced the rate of photoinactivation. Multidrug-resistant and carbapenemase-producing Enterobacteriaceae strains showed similar susceptibility to aBL relative to antibiotic-sensitive strains, suggesting no cross-resistance between light and traditional drugs. Conclusions: Endogenous blue light is a highly effective, non-thermal technology for E. coli decontamination. Its efficacy is modulated by the interplay between optical parameters and environmental conditions. These findings provide a framework for the development of standardized protocols for applying aBL to clinical wound care and food industry use cases. They also highlight the potential of aBL as a critical tool in the post-antibiotic era. This systematic review was registered in the International prospective register of systematic reviews (PROSPERO) under protocol CRD420261331871. Full article

The increasing demand for sustainable industrial practices has intensified the search for manufacturing processes that minimize environmental impacts without compromising technical performance or economic viability. In this context, powder metallurgy has emerged as a promising alternative in mechanical manufacturing due to its potential for raw material reuse, waste reduction, lower energy consumption, and near-net-shape production. However, despite the growing body of research on this topic, there is still a lack of a comprehensive and integrated framework that systematically organizes and correlates the factors influencing sustainability across environmental, economic, and social dimensions, which limits a holistic understanding of the process. Therefore, this study aims to analyze and classify the main factors affecting sustainability in powder metallurgy. A Systematic Literature Review was conducted following the PRISMA method, using the Scopus, Web of Science and Wiley databases. The initial search identified 1753 articles, of which 56 were selected after applying inclusion and exclusion criteria. The analysis considers the three pillars of sustainability and examines how variables related to raw materials, energy consumption, processing technologies, waste reuse, product performance, and operational conditions influence process sustainability. The results enable the identification of the most recurrent factors in the literature and support the development of a structured theoretical framework, contributing to a more integrated understanding of sustainability in powder metallurgy. Full article

Machine learning (ML) has become a key enabling technology for optimizing renewable energy systems and supporting global sustainability objectives. This paper presents a comprehensive review of recent advances in ML-based optimization techniques applied to clean and renewable energy systems, with particular emphasis on wind energy, hybrid energy systems, energy storage, and intelligent energy management. A systematic literature review covering peer-reviewed publications from 2021 to 2025 was conducted, resulting in the analysis of 138 high-quality journal and conference studies. The reviewed studies were categorized according to evolutionary algorithm-based hybrid models, classical neural networks, and deep learning architectures, including Convolutional Neural Network (CNN), LSTMs, GRUs, and attention-based models. The analysis demonstrates that hybrid ML–metaheuristic frameworks significantly enhance forecasting accuracy, system reliability, fault diagnosis, and multi-objective optimization compared to traditional methods. These intelligent approaches directly contribute to Sustainable Development Goals SDG-7 (Affordable and Clean Energy), SDG-9 (Industry, Innovation, and Infrastructure), and SDG-13 (Climate Action). Key challenges and future research directions are discussed, highlighting the need for scalable, explainable, and real-time ML solutions to enable resilient, low-carbon, and sustainable energy systems. Full article

Carbon capture and utilization (CCU) is imperative for industrial decarbonization. However, current life cycle assessment (LCA) methodologies often apply a static, one-size-fits-all approach, assuming a 99% CO₂ purity standard for all utilization pathways. This ignores the thermodynamic limits of capture technologies and the tolerance of certain endpoints for coarse gas, leading to severe over-purification energy penalties. To bridge this gap, we developed a quality-matched dynamic LCA framework targeting steam methane reforming (SMR) tail gas in industrial parks. A superstructure matrix was constructed, coupling 16 capture configurations (spanning chemical absorption to cryogenic separation across 85–99% purities) with five utilization pathways, under a dynamic grid decarbonization model (2024–2060). The baseline scenario shows that methanol is the most carbon-intensive pathway at 16.88 kg CO₂-eq per kg CO₂ utilized, whereas mineralization and concrete curing remain near break-even at 0.221 and 0.010 kg CO₂-eq, respectively. When low-purity demand is matched with PSA capture at 85–90% purity, the net GWP of mineralization and concrete curing decreases to 0.134 and 0.005 kg CO₂-eq, corresponding to capture-stage penalty reductions exceeding 60% relative to unnecessary 99% purification. Under the dynamic electricity scenario, concrete curing reaches the net-zero tipping point around 2031, and the coupled mineralization substitution strategy ultimately achieves −0.046 kg CO₂-eq per kg CO₂ utilized. These findings provide a compelling scientific basis for policymakers to design dual-grade CO₂ pipeline networks and prioritize low-purity, high-circularity building materials over carbon-intensive chemical synthesis in near-term industrial transitions. Full article

The pollution of water, including salt and fresh water, has become an emergency problem. Pollutants come from different sources and have various characteristics, starting from industry and fertilizers used in agriculture, sewage related to human living, and other sources. Diverse sources of pollution require a comprehensive approach to water purification. One possible approach may be the use of appropriate sorbents. Currently, one of the most promising materials used is zeolites. This is because they can come from various sources, including waste raw materials such as fly ash, and, therefore, allow for the use of a circular economy approach. Moreover, these materials can be modified, which enables their selective use for selected types of pollutants. Eventually, these materials become economically viable options. The main aim of this article is to present and analyze possible solutions to water pollution based on zeolite materials. For this purpose, a critical literature review was prepared. The review reveals that zeolites perform particularly well in ion-exchange-driven removal of inorganic contaminants, while their effectiveness for organic micropollutants under realistic conditions is often limited. The identified trade-offs between removal efficiency, regeneration stability, and scalability indicate that zeolites are best applied as function-specific rather than universal sorbents. From a sustainability perspective, this targeted applicability is supported by advantages, such as low material cost, long service life, and the possibility of using naturally occurring or waste-derived precursors, which, together, enable resource-efficient water treatment processes, reduced reliance on energy-intensive technologies, and the valorization of industrial byproducts within circular economy frameworks. Full article

Understanding how digital technology innovation affects the resilience of the forestry economy and the mechanisms at play is a critical step in resolving the “resource–development” contradiction and advancing the sustainable development of the forestry sector amid the ongoing technological revolution. This study uses panel data from 31 Chinese provinces, municipalities, and autonomous regions covering the period from 2004 to 2023. This study applies the entropy weight method to quantitatively assess forestry economic resilience and uses a two-way fixed effects model to empirically test the impact of digital technology innovation on resilience. It also examines how industrial structure upgrading and the digital divide influence this relationship. This study found that digital technology innovation significantly enhances forestry economic resilience. The industrial structure upgrading of forestry plays a minor mediating role in this effect. The first- and second-level digital divides negatively moderate the impact of digital technology innovation on resilience, while the third-level divide has no significant effect. Additionally, the impact of digital technology innovation varies across different quantiles and climate risk levels. Full article

Industry 4.0 (I4.0) is transforming industrial systems through interconnected, data-driven technologies, raising questions about how these developments affect Occupational Health and Safety (OHS). This study investigates research trends, thematic structures, and knowledge gaps at the intersection of I4.0 and OHS using a multilevel bibliometric framework applied to Scopus records published from 2011 to 2025. The analysis moves from a broad overview of the I4.0 landscape to more focused examinations of specific I4.0–OHS publications, prevention-oriented studies, and emerging-risk research. The results show that OHS has limited visibility in the general I4.0 literature and is more prominent mainly in targeted subsets, where digital sensing, artificial intelligence, machine learning, and immersive technologies drive prevention-focused research. Conversely, emerging risks such as cognitive load, psychosocial stressors, and human–autonomy interaction appear in smaller, more dispersed clusters. Overall, the findings suggest that the relationship between I4.0 and OHS is unevenly developed, with established prevention mechanisms and early-stage conceptualization of new risks. Strengthening this field will require integrating human factors with digital indicators, better characterizing emerging risks, and ensuring that digital transformation supports SDG 8 by fostering safe and healthy working environments. Full article

Hot extrusion tools operate under severe thermal and mechanical conditions, which significantly limit their service life. During operation, the punch and die absorb large amounts of heat from the hot billet while being subjected to high pressures and intense friction, leading to severe abrasive wear and progressive hardness reduction. In practice, the punch generally exhibits a shorter service life than the die. The present study proposes a technological solution for reconditioning worn extrusion punches using vibration-assisted welding (VAW). A wear-resistant layer was deposited by MIG welding using DUR 600 filler material, while mechanical vibrations were introduced through a vibrating welding table. The applied vibration regime consisted of a frequency of 50 Hz–108 Hz and acceleration components of a_x = 30–60 m/s² and a_z = 35–70 m/s². The experimental investigations included macroscopic analysis, hardness and microhardness measurements, microstructural observations, and SEM-EDS line scanning analysis of the dilution zone between the cladding material and the base metal. The results suggest that vibration-assisted welding may influence the microstructural characteristics, hardness distribution, and dilution behavior of the cladded layer. The vibrated specimens exhibited higher hardness values in the range of 702 to 908 HV5–10. Under the investigated conditions, the process did not require additional hardening treatment, and only a stress-relief annealing stage was applied. The proposed VAW approach appears to be a promising option for the reconditioning of hot extrusion tools; however, further investigations are required to validate its performance under industrial conditions. Full article

This study evaluates the adoption and efficacy of the 5th Dimension Building Information Modelling (5D-BIM) as a cost dimension for mega rail projects, extending the discussion beyond just technological implementation to consider broader policy and practical implications. The purpose of this article is to understand the governance context of 5D-BIM implementation for rail and transport projects and evaluate the effectiveness of the 5D-BIM framework as currently applied by conducting semi-structured interviews with key stakeholders. Drawing on semi-structured interviews with 22 stakeholders across government, industry, and technology providers, the research examines current 5D-BIM practices. While the primary focus of the research is 5D BIM implementations within the state of Victoria, Australia, which is currently experiencing a surge in rail projects, interviews were also conducted with additional stakeholders from international rail projects for context. The findings reveal fragmented adoption, varying levels of organisational maturity, and significant policy and implementation gaps, particularly in the role of government as the primary client of transport infrastructure. The results of the interviews emphasise the centrality of government and regulatory context in driving the adoption and implementation of 5D-BIM as the primary client of transportation infrastructure and identify actionable recommendations for policymakers and practitioners towards a more integrated approach to 5D-BIM in mega rail projects. While 5D-BIM demonstrates clear benefits in enhancing cost estimation, coordination, and decision-making, its effectiveness is constrained by the absence of clear standards, limited BIM literacy, and inconsistent regulatory guidance. This study provides one of the first empirical validations of the 5D-BIM governance framework, demonstrating that its success is driven less by technological capability and more by policy alignment, standardisation, and institutional leadership. Full article

This systematic literature review explores the intersection of Artificial Intelligence (AI) and Predictive Maintenance (PdM) within Industry 4.0. Using a PRISMA-based methodology, 123 studies published between 2014 and April 2024 were analyzed to characterize technological trends, algorithmic choices, industrial applications, and evaluation practices. The review reveals a consistent growth of research interest, driven by the widespread adoption of Internet of Things (IoT) devices and increased data availability. The manufacturing sector dominates the literature, although most studies rely on standardized datasets rather than real industrial environments. Among the identified AI methods, Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT) and K-Nearest Neighbors (KNNs) represent the most frequently applied algorithms for tasks such as failure prediction, fault detection, and remaining useful life (RUL) estimation. Model performance is commonly evaluated with Accuracy (Acc), Precision, Recall, F1-Score, and Root Mean Square Error (RMSE), reflecting the prevalence of both classification and regression-based PdM analyses. Despite significant advances, this review identifies persistent gaps, including limited domain diversity, scarce long-term real-world validation, and insufficient use of eXplainable AI (XAI) techniques. The findings highlight the need for broader domain coverage, improved interpretability, and validation under realistic industrial conditions. Overall, this review consolidates current knowledge on AI-enabled PdM and outlines critical directions to enhance reliability, transparency, and industrial relevance in the context of Industry 4.0. Full article

Waste management has become a critical issue in industry due to its environmental and economic impact. This urges societies to move towards circular economy and sustainability in managing wastes to achieve economic, social and environmental benefits that cannot be achieved by using traditional disposal methods. Artificial intelligence technologies can play an effective role in developing systems and applications that help in realizing circular economy approaches. This research explores using circular economy in the carpet industry with a focus on being eco-friendly and achieving operational excellence. Circular economy aims to use resources efficiently within a reduce, reuse, recycle paradigm. Critical literature review was performed to address the concepts of waste management, circular economy and how artificial intelligence can be applied in this area to make a business more sustainable. A conceptual framework (AICE Framework) is proposed to integrate artificial intelligence and circular economy to develop a smart and sustainable waste management system for industrial applications. Full article