Search Results (813)

Aerogels (AGs) are highly porous, low-density, disordered, ultralight macroscopic materials with immense surface areas. Traditionally synthesized using aqueous sol–gel chemistry, starting by molecular precursors, the nanoparticles (NPs) dispersions gelation method is nowadays the most used procedure to obtain AGs with improved crystallinity and broader structural, morphological and compositional complexity. The Sol–gel process consists of preparing a solution by hydrolysis of different precursors, followed by gelation, ageing and a drying phase, via supercritical, freeze-drying or ambient evaporation. AGs can be classified based on various factors, such as appearance, synthetic methods, chemical origin, drying methods, microstructure, etc. Due to their nonpareil characteristics, AGs are completely different from common NPs, thus covering different and more extensive applications. AGs can be applied in supercapacitors, acoustic devices, drug delivery, thermal insulation, catalysis, electrocatalysis, gas absorption, gas separation, organic and inorganic xenobiotics removal from water and air and radionucleotides management. This review provides first an analysis on AGs according to data found in CAS Content Collection. Then, an AGs’ classification based on the chemical origin of their precursors, as well as the different methods existing to prepare AGs and the current optimization strategies are discussed. Following, focusing on AGs of inorganic origin, silica and metal oxide-based AGs are reviewed, deeply discussing their properties, specific synthesis and possible uses. These classes were chosen based on the evidence that they are the most experimented, patented and marketed AGs. Several related case studies are reported, some of which have been presented in reader-friendly tables and discussed. Full article

Polymorphism critically influences the solid-state properties of organic molecules, affecting stability, solubility, and functionality. We investigated the polymorphic behavior of enantiomerically pure (+)-(6aS,11aS)-medicarpin through combined experimental and computational analyses. Single-crystal X-ray diffraction revealed two distinct chiral polymorphs: the previously reported monoclinic P2₁ form and a newly identified orthorhombic P2₁2₁2₁ form with a fully chiral packing arrangement. The discovery of this previously unreported polymorph underscores the subtle yet decisive effects of solvent and conformational flexibility in directing crystallization. Detailed structural analysis reveals that, whereas the P2₁ form is only stabilized by a single dominant electrostatic interaction, the P2₁2₁2₁ form features a more complex network comprising C-H···π contacts, bifurcated C-H···O hydrogen bonds, and aromatic edge-to-face interactions. Further investigation of a functionalized p-nitrobenzoate derivative corroborates the critical influence of molecular substituents and crystallization conditions on packing motifs. Lattice energy DFT calculations confirm that each polymorph is stabilized by distinct electrostatic and dispersive interaction patterns, illustrating the complex energetic landscape of polymorph selection. Altogether, this work provides a framework for understanding and anticipating which polymorph is likely to form under specific solvent and crystallization conditions, offering insights for future strategies in materials design and guiding the pursuit of patentable crystalline forms in pharmaceutical applications. Full article

LED strips are increasingly used as lighting sources in public and private spaces. However, traditional photometric methods, such as integrating spheres, are unsuitable for measuring their light parameters, often resulting in significant errors and requiring expensive instrumentation or calibration. These errors are typically caused by non-uniform illumination of the internal surface or improper internal geometry, especially when measuring LED sources. This article presents the development of a low-cost integrating device specifically designed to measure LED strips’ light parameters. The device is a compact cube with a volume of less than 1.0 m³. It was tested against alternative methods using an integrating sphere and a goniophotometer in a professional photometric laboratory. The verification results confirmed its effectiveness. The device showed the maximum relative error of luminous flux measurement to be around 5% compared with the accurate, expensive goniophotometric method. For colorimetric measurements, the maximum Correlated Color Temperature (CCT) absolute error was about 35 K for an LED strip with a CCT of 4000 K, indicating a difference imperceptible to the human eye. These results demonstrate the device’s proper relevance in the research and development of LED strip-based lighting equipment to improve lighting equipment quality and control processes. The device is easy to replicate, significantly reducing production and transportation costs, making it an excellent solution for companies and research units seeking a cost-effective method for LED strip measurements. Additionally, the device can measure other light sources or luminaires with reasonably small sizes emitting light in only one hemisphere. The device is the basis of a patent application. Full article

Weeds are among the primary factors limiting corn and bean productivity, accounting for up to 30% of yield losses. Although chemical herbicides remain the predominant weed control strategy, their toxicity poses significant risks to human health and the environment. In response, organic agriculture has gained prominence as a more sustainable production system, with an increasing interest in alternative weed management approaches. Plants that produce allelopathic compounds capable of inhibiting the growth of unwanted species have emerged as promising sources of natural bioherbicides. While recent reviews have primarily focused on bioherbicides derived from microorganisms, a notable gap remains regarding the production and application of bioherbicides based on plant extracts. This review addresses this gap by summarizing current knowledge on the use of plant extracts for weed control in corn and bean cultivation. It discusses extraction methods, key plant species and active compounds, target weed species, herbicidal effects, modes of action, and patented technologies. Promising plants include Cuscuta campestris, Cymbopogon citratus, Mentha spp., Eucalyptus spp., and Pinus spp., which are rich in bioactive compounds such as phenolics (i.e., flavonoids), quinones, aldehydes and ketones, lactones, terpenoids (i.e., 8-cineole), and steroids. Plant extract-based bioherbicides show promising potential as sustainable and effective alternatives for weed management in organic agriculture, contributing to reducing the synthetic chemical herbicides, avoiding more resistances of weeds resistance of control, and promoting more environmentally friendly agricultural practices. Full article

Lubricating oils are utilised in equipment and machinery to reduce friction and enhance material utilisation. The utilisation of oil leads to an increase in its thickness and density over time. Current methods for assessing oil life are slow, expensive, and complex, and often only applicable in laboratory settings and unsuitable for real-time or field use. This leads to unexpected equipment failures, unnecessary oil changes, and economic and environmental losses. A comprehensive review of the extant literature revealed no studies and no national or international patents on neural network algorithm-based oil life modelling and classification using green sensors. In order to address this research gap, this study, for the first time in the literature, provides a green conductivity sensor with high-accuracy prediction of oil life by integrating real-time field measurements and artificial neural networks. This design is based on analysing resistance change using a relatively low-cost, three-dimensional, eco-friendly sensor. The sensor is characterised by its simplicity, speed, precision, instantaneous measurement capability, and user-friendliness. The MLP and LVQ algorithms took as input the resistance values measured in two different oil types (diesel, bench oil) after 5–30 h of use. Depending on their degradation levels, they classified the oils as ‘diesel’ or ‘bench oil’ with 99.77% and 100% accuracy. This study encompasses a sensing system with a sensitivity of 50 µS/cm, demonstrating the proposed methodologies’ efficacy. A next-generation decision support system that will perform oil life determination in real time and with excellent efficiency has been introduced into the literature. The components of the sensor structure under scrutiny in this study are conducive to the creation of zero waste, in addition to being environmentally friendly and biocompatible. The developed three-dimensional green sensor simultaneously detects physical (resistance change) and chemical (oxidation-induced polar group formation) degradation by measuring oil conductivity and resistance changes. Measurements were conducted on simulated contaminated samples in a laboratory environment and on real diesel, gasoline, and industrial oil samples. Thanks to its simplicity, rapid applicability, and low cost, the proposed method enables real-time data collection and decision-making in industrial maintenance processes, contributing to the development of predictive maintenance strategies. It also supports environmental sustainability by preventing unnecessary oil changes and reducing waste. Full article

Green innovation is essential for the sustainable transformation of enterprises. This study investigates how equity and debt financing dependence influence green innovation using panel data from manufacturing firms listed in Shanghai and Shenzhen between 2012 and 2022. We construct dynamic indicators of equity and debt financing dependence based on firms’ external financing relative to internal capital and assess their effects using panel regressions. Both financing types significantly enhance green innovation. Equity financing dependence increases green patent applications and green invention patent applications by approximately 1.4%, while debt financing dependence leads to gains of 0.9% and 1.1%, respectively. Financial reporting quality positively moderates these effects, with a stronger influence on debt financing dependence. High-quality reporting amplifies the impact of debt financing dependence by about 27% for green patent applications and 22% for green invention patent applications, while its effect on equity financing dependence is limited. Heterogeneity analysis reveals that equity financing dependence is most effective in small and young firms, while debt financing dependence has the strongest impact in medium-sized firms, particularly on green patent applications. The findings highlight the long-term influence of financing behavior on green innovation and inform green finance policy. Full article

Digital city planning increasingly relies on data-driven approaches to address complex urban sustainability challenges through innovative network analysis methodologies. This study introduces a comprehensive spatiotemporal network framework to examine digital innovation networks as fundamental infrastructure for urban sustainable development, focusing on the Yellow River Basin as a representative case study. Utilizing digital patent data as innovation indicators across 57 urban centers, we employ advanced network analysis techniques including Social Network Analysis (SNA) and the Quadratic Assignment Procedure (QAP) to investigate the spatiotemporal evolution patterns and underlying driving mechanisms of regional digital innovation networks. The methodology integrates big data analytics with urban planning applications to provide evidence-based insights for digital city planning strategies. Our empirical findings reveal three critical dimensions of urban sustainable development through digital innovation networks: First, the region demonstrated significant enhancement in digital innovation capacity from 2012 to 2022, with accelerated growth patterns post 2020, indicating robust urban resilience and adaptive capacity for sustainable transformation. Second, the spatial network configuration exhibited increasing interconnectivity characterized by strengthened urban–rural linkages and enhanced cross-regional innovation flows, forming a hierarchical centrality pattern where major metropolitan centers (Xi’an, Zhengzhou, Jinan, and Lanzhou) serve as innovation hubs driving coordinated regional development. Third, analysis of network formation mechanisms indicates that spatial proximity, market dynamics, and industrial foundations negatively correlate with network density, suggesting that regional heterogeneity in these characteristics promotes innovation diffusion and strengthens inter-urban connections, while technical human capital and governmental interventions show limited influence on network evolution. This research contributes to the digital city planning literature by demonstrating how data-driven network analysis can inform sustainable urban development strategies, providing valuable insights for policymakers and urban planners implementing AI technologies and big data applications in regional development planning. Full article

Nitrogen-containing heterocycles are essential compounds in nature, and their structural and functional diversity inspired the synthesis of a wide range of derivatives with diverse applications as pharmaceuticals, agrochemicals, dyes, polymers, cosmetics, etc. Among them, N-fused heterocycles represent an important category, due to their high potential as biologically active agents. Pyrazolo[5,1-c][1,2,4]triazoles, a class of nitrogen heterobicycles, have multiple applications as dyes and pigments. Also, a number of compounds containing this structure have been investigated for their biological activities. All the main experimental results published in the literature (both articles and patents) regarding the latter are summarized in this review. Full article

High-value patents are a key indicator of new product development, the emergence of innovative technology, and a source of innovation incentives. Multiple studies have shown that patent value exhibits a significantly skewed distribution, with only about 10% of patents having high value. Identifying high-value patents from a large volume of patent data in advance has become a crucial problem that needs to be addressed urgently. However, current machine learning methods often rely on manual hyperparameter tuning, which is time-consuming and prone to suboptimal results. Existing optimization algorithms also suffer from slow convergence and local optima issues, limiting their effectiveness on complex patent datasets. In this paper, machine learning and intelligent optimization algorithms are combined to process and analyze the patent data. The Fire Hawk Optimization Algorithm (FHO) is a novel intelligence algorithm suggested in recent years, inspired by the process in nature where Fire Hawks capture prey by setting fires. This paper firstly proposes the Enhanced Fire Hawk Optimizer (EFHO), which combines four strategies, namely adaptive tent chaotic mapping, hunting prey, adding the inertial weight, and enhanced flee strategy to address the weakness of FHO development. Benchmark tests demonstrate EFHO’s superior convergence speed, accuracy, and robustness across standard optimization benchmarks. As a representative real-world application, EFHO is employed to optimize Random Forest hyperparameters for high-value patent recognition. While other intelligent optimizers could be applied, EFHO effectively overcomes common issues like slow convergence and local optima trapping. Compared to other classification methods, the EFHO-optimized Random Forest achieves superior accuracy and classification stability. This study fills a research gap in effective hyperparameter tuning for patent recognition and demonstrates EFHO’s practical value on real-world patent datasets. Full article

Tokamak technology, as the cornerstone of nuclear fusion energy, holds immense potential in achieving efficient plasma confinement and high energy densities. To comprehensively map the rapidly evolving landscape of this field, this study employs bibliometric analysis to systematically examine the research and development trends of tokamak technology from 2014 to 2024. The data are drawn from 7702 academic publications in the Scopus database, representing a global research effort. Additionally, the study incorporates 2299 tokamak-related patents from Google Patents over the same period, analyzing their technological trends to highlight the growing significance of tokamak devices. Using the R language and the Bibliometric package, the analysis explores research hotspots, institutional influences, and keyword evolution. The results reveal a multifaceted global landscape: China leads in publication output, and the United States maintains a leading role in citation impacts and technological innovation, with other notable contributions from Germany, Japan, South Korea, and various European countries. Patent trend analysis further reveals the rapid expansion of tokamak applications, particularly with significant innovations in high-temperature superconducting magnets and plasma control technologies. Nevertheless, the study identifies major challenges in the commercialization process, including plasma stability control, material durability, and the sustainability of long-term operations. To address these, the study proposes concrete future directions, emphasizing international collaboration and interdisciplinary integration. These efforts are crucial in accelerating tokamak commercialization, thereby providing a strategic roadmap for researchers, policymakers, and industry stakeholders to advance the global deployment of clean energy solutions. Full article

Electrospun alginate nanofibers are emerging as versatile materials for biomedical, environmental, and packaging applications due to their biocompatibility, biodegradability, and functional tunability. However, the direct electrospinning of alginate remains a significant challenge, mainly due to its polyelectrolytic nature, rigid chain structure, and limited chain entanglement. This review provides a comprehensive analysis of recent strategies developed to overcome these limitations, including polymer blending, chemical modification, the addition of surfactants, multi-fluid techniques, and process optimization. We systematically discuss the integration of nanofibers with functional agents such as microorganisms, bioactive compounds, plant extracts, and nanoparticles, highlighting their potential in wound healing, active packaging, bioremediation, and controlled release systems. This review also examines the scalability of alginate electrospinning, summarizing recent patents, industrial solutions, and challenges related to the standardization of the process. Key knowledge gaps are identified, including the need for long-term stability studies, structure–function correlations, green processing approaches, and expansion into novel application domains beyond healthcare. Addressing these research directions will be crucial to unlocking the full potential of alginate nanofibers as sustainable, high-performance materials for industrial use. Full article

Extreme high-temperature environments pose challenges for human thermal comfort and safety. This study introduces a compact portable liquid cooling garment weighing 3.6 kg in total with an integrated 1.99 kg vapor compression refrigeration unit (172 mm × 80 mm × 130 mm). This system innovatively integrates a patented evaporator-pump module and an optimized miniature rotary compressor, achieving a 151 W cooling capacity at 55 °C ambient temperature, surpassing existing portable systems in compactness and performance. Human trials with eight male participants at 35 °C (walking) and 40 °C (sitting) demonstrated that the liquid cooling garment system significantly improved thermal comfort. The mean thermal comfort vote decreased from 2.63 (uncomfortable) to 1.13 (slightly uncomfortable) while walking and from 3.88 (very uncomfortable) to 1.25 (slightly uncomfortable) while sitting. The mean skin temperature in the final stable state was reduced by 0.34 °C in walking trials and 1.09 °C in sitting trials, and heart rate decreased by up to 10.2 bpm in sedentary conditions. Comprehensive human trials under extreme heat further validate this system’s efficacy. This lightweight, efficient system offers a practical solution for personal thermal management in extreme high-temperature environments, with potential applications in industrial safety, military operations, and emergency response. Full article

As a crucial vehicle for green technological innovation, cooperative networks significantly promote resource integration and knowledge sharing. Yet, their dynamic evolution and micro-mechanism remain underexplored. Drawing on data from the joint applications of green invention patents between 2006 and 2021, this study constructed a multi-agent GTCIN involving multiple stakeholders, such as enterprises, universities, and research institutions, and analyzed the topological structure and evolutionary characteristics of this network; an exponential random graph model (ERGM) was introduced to elucidate its endogenous and exogenous driving mechanisms. The results indicate that while innovation connections increased significantly, the connection density decreased. The network evolved from a “loose homogeneity” to “core aggregation” and then to “outward diffusion”. State-owned enterprises in the power industry and well-known universities are located at the core of the network. Preferential attachment and transitive closure as endogenous mechanisms exert strong and continuous positive effects by reinforcing local clustering and cumulative growth. The effects of exogenous forces exhibit stage-specific characteristics. State ownership and regional location become significant positive drivers only in the mid-to-late stages. The impact of green innovation capability is nonlinear, initially promoting but later exhibiting a significant inhibitory effect. In contrast, green knowledge diversity exerts an opposite pattern, having a negative effect in the early stage due to integration difficulties that turns positive as technical standards mature. Geographical, technological, social, and institutional proximity all have a positive promoting effect on network evolution, with technological proximity being the most influential. However, organizational proximity exerts a significant inhibitory effect in the later stages of GTCIN evolution. This study reveals the shifting influence of endogenous and exogenous mechanisms across different evolutionary phases, providing theoretical and empirical insights into the formation and development of green innovation networks. Full article

In recent years, experts have associated forecasts of global energy consumption with energy transitions. This paper presents the research results of the paths and trajectories of the global transformations of world energy, including demographic, technological, energy, transport, and communication changes. After demonstrating the long-term trends in global energy consumption, fossil and renewable energy sources, and nuclear energy using neuroforecasting methods, this study explains global demographic development and its relationship with global innovation and technological processes as explained by the flow of global patent applications. The relationship between energy transition and the previously mentioned two factors is also justified based on the trajectories developed by the neural network forecasting. By leveraging the fundamental laws of energy conservation, robust patterns in the evolution and development of global energy could be identified. It is demonstrated that mankind has entered the era of four closely interconnected global transitions: demographic, energy, technological, and political–economic, all at once. According to the results, civilizational changes are currently taking place in global energy advancement, indicating an energy transition to a new quality of energy development. The permanent growth patterns of the energy density of energy sources used and their impact on labor productivity and the speed of movement of people and goods in the economy are also discussed. Finally, the contour of future developments in energy technologies is determined. It is also forecast that future energy technologies are expected to be largely associated with the exploration of outer space, development of robotics, and the expansion of artificial intelligence capabilities. Full article

This paper deals with the design and the robustness analysis of a model predictive control (MPC) for the position tracking of primary flight movables driven by electro-mechanical actuators. This study is, in particular, focused on a rotary electro-mechanical actuator (EMA) by UMBRAGROUP, employing a patented mechanical transmission based on a differential ball-screw mechanism characterized by a huge gear ratio. To obtain a baseline reference, conventional PID regulators were initially optimized by using multi-objective cost functions based on tracking accuracy, load disturbance rejection, and power consumption. The position regulator was then replaced by an MPC regulator, designed to balance performance, computational resources, and safety constraints. A nonlinear physics-based simulation model of the EMA, entirely developed in the Matlab–Simulink environment and validated with experiments, was used to compare the two control strategies. The simulation results in both the time and frequency domains highlight that the MPC solution provides faster and more accurate position tracking, improved dynamic stiffness, and reduced power absorption. Finally, the robustness against model uncertainties of the MPC was addressed by imposing random and combined deviations of model parameters from the nominal values (via Monte Carlo analysis). The results demonstrate that the implementation of MPC control laws could enhance the stability and the reliability of EMAs, thus supporting their application for safety-critical flight control functions. Full article