Abstract The purpose of this study is to review existing recycling technologies, standards and market situation for plastics recycling. The principal results show that mechanical recycling is the most well-developed recycling approach in terms of industrial feasibility . This approach enables development of plastic recyclates of various quality levels . At the same time, transfer of many research findings into practice is hindered due to the global plastic material flow, strongly differing regional waste management systems and lack of international recycling standards. This review shows that the development of a Circular Economy Model for plastics products requires close cooperation of scientists with standardization committees and industry.
Abstract Advanced materials were used and are being implemented in structural, mechanical, and high-end applications. Contemporary materials are used and being implemented in structural, mechanical, and high-end applications. Composites have several major capabilities, some of them being able to resist fatigue, corrosion-resistance, and production of lightweight components with almost no compromise to the reliability, etc. Nanocomposites are a branch of materials within composites, known for their greater mechanical properties than regular composite materials. The use of nanocomposites in the aerospace industry currently faces a research gap, mainly identifying the future scope for application. Most successes in the aerospace industry are because of the use of suitable nanocomposites. This review article highlights the various nanocomposite materials and their properties, manufacturing methods, and their application, with key emphasis on exploiting their advanced and immense mechanical properties in the aerospace industry. Aerospace structures have used around 120,000 materials; herein, nanocomposites such as MgB2, multi-walled carbon nanotubes, and acrylonitrile butadiene styrene/montmorillonite nanocomposites are discussed, and these highlight properties such as mechanical strength, durability, flame retardancy, chemical resistance, and thermal stability in the aerospace application for lightweight spacecraft structures, coatings against the harsh climate of the space environment, and development of microelectronic subsystems.
As the scaling technology in the silicon-based semiconductor industry is approaching physical limits, it is necessary to search for proper materials to be utilized as alternatives for nanoscale devices and technologies. On the other hand, carbon-related nanomaterials have attracted so much attention from a vast variety of research and industry groups due to the outstanding electrical, optical, mechanical and thermal characteristics. Such materials have been used in a variety of devices in microelectronics. In particular, graphene and carbon nanotubes are extraordinarily favorable substances in the literature. Hence, investigation of carbon-related nanomaterials and nanostructures in different ranges of applications in science, technology and engineering is mandatory. This paper reviews the basics, advantages, drawbacks and investigates the recent progress and advances of such materials in micro and nanoelectronics, optoelectronics and biotechnology.
Internet of Things (IoT) sensors are ubiquitous technologies deployed across smart cities, industrial sites, and healthcare systems. They continuously generate time series data that enable advanced analytics and automation in industries. However, challenges such as the loss or ambiguity of sensor metadata, heterogeneity in data sources, varying sampling frequencies, inconsistent units of measurement, and irregular timestamps make raw IoT time series data difficult to interpret, undermining the effectiveness of smart systems. To address these challenges, we propose a novel deep learning model, DeepFeatIoT, which integrates learned local and global features with non-learned randomized convolutional kernel-based features and features from large language models (LLMs). This straightforward yet unique fusion of diverse learned and non-learned features significantly enhances IoT time series sensor data classification, even in scenarios with limited labeled data. Our model's effectiveness is demonstrated through its consistent and generalized performance across multiple real-world IoT sensor datasets from diverse critical application domains, outperforming state-of-the-art benchmark models. These results highlight DeepFeatIoT's potential to drive significant advancements in IoT analytics and support the development of next-generation smart systems.
Object 6DoF (6D) pose estimation is essential for robotic perception, especially in industrial settings. It enables robots to interact with the environment and manipulate objects. However, existing benchmarks on object 6D pose estimation primarily use everyday objects with rich textures and low-reflectivity, limiting model generalization to industrial scenarios where objects are often metallic, texture-less, and highly reflective. To address this gap, we propose a novel dataset and benchmark namely \textit{Industrial Metallic Dataset (IMD)}, tailored for industrial applications. Our dataset comprises 45 true-to-scale industrial components, captured with an RGB-D camera under natural indoor lighting and varied object arrangements to replicate real-world conditions. The benchmark supports three tasks, including video object segmentation, 6D pose tracking, and one-shot 6D pose estimation. We evaluate existing state-of-the-art models, including XMem and SAM2 for segmentation, and BundleTrack and BundleSDF for pose estimation, to assess model performance in industrial contexts. Evaluation results show that our industrial dataset is more challenging than existing household object datasets. This benchmark provides the baseline for developing and comparing segmentation and pose estimation algorithms that better generalize to industrial robotics scenarios.
In many biological systems localized mechanical information is transmitted by mechanically neutral chemical signals. Typical examples include contraction waves in acto-myosin cortex at cellular scale and peristaltic waves at tissue level. In such systems, chemical activity is transformed into mechanical deformation by distributed motor-type mechanisms represented by continuum degrees of freedom. To elucidate the underlying principles of chemo-mechanical coupling, we here present the simplest example, involving directional motion of a localized solitary wave in a distributed mechanical system, guided by a purely chemical cue. Our main result is that mechanical signals can be driven by chemical activity in a highly efficient manner.
The petrochemical industry faces significant technological, environmental, occupational safety, and financial challenges. Since its emergence in the 1920s, technologies that were once innovative have now become obsolete. However, factors such as the protection of trade secrets in industrial processes, limited budgets for research and development, doubts about the reliability of new technologies, and resistance to change from decision-makers have hindered the adoption of new approaches, such as the use of IoT devices. This paper addresses the challenges and opportunities presented by the research, development, and implementation of these technologies in the industry. It also analyzes the investment in research and development made by companies in the sector in recent years and provides a review of current research and implementations related to Industry 4.0.
The recent development of Agentic AI systems, empowered by autonomous large language models (LLMs) agents with planning and tool-usage capabilities, enables new possibilities for the evolution of industrial automation and reduces the complexity introduced by Industry 4.0. This work proposes a conceptual framework that integrates Agentic AI with the intent-based paradigm, originally developed in network research, to simplify human-machine interaction (HMI) and better align automation systems with the human-centric, sustainable, and resilient principles of Industry 5.0. Based on the intent-based processing, the framework allows human operators to express high-level business or operational goals in natural language, which are decomposed into actionable components. These intents are broken into expectations, conditions, targets, context, and information that guide sub-agents equipped with specialized tools to execute domain-specific tasks. A proof of concept was implemented using the CMAPSS dataset and Google Agent Developer Kit (ADK), demonstrating the feasibility of intent decomposition, agent orchestration, and autonomous decision-making in predictive maintenance scenarios. The results confirm the potential of this approach to reduce technical barriers and enable scalable, intent-driven automation, despite data quality and explainability concerns.
Matti Silveri, Tommi Mikkonen, Kimmo Halunen
et al.
Quantum computing is a disruptive technology with the potential to transform various fields. It has predicted abilities to solve complex computational problems beyond the reach of classical computers. However, developing quantum software faces significant challenges. Quantum hardware is yet limited in size and unstable with errors and noise. A shortage of skilled developers and a lack of standardization delay adoption. Quantum hardware is in the process of maturing and is constantly changing its characteristics rendering algorithm design increasingly complex, requiring innovative solutions. Project "Towards reliable quantum software development: Approaches and use-cases" TORQS has studied the dilemma of reliable software development and potential for quantum computing for Finnish industries from multidisciplinary points of views. Here we condense the main observations and results of the project into an essay roadmap and timeline for investing in quantum software, algorithms, hardware, and business.
Szymon Sawczyński, Anees Ahmed Vighio, Muhammad Yousaf Raza Taseer
Sustainable construction focuses on minimizing raw material consumption and optimizing construction processes in terms of both economy and ecology. Lintels are among the structural elements where material usage can be significantly reduced. Traditionally used prefabricated beams, while convenient, often exhibit excessive strength relative to actual loads, leading to unnecessary costs and an increased carbon footprint. This article examines the potential use of fiber-reinforced concrete – concrete strengthened with polymer and steel fibers – as an alternative to traditional lintels. Comparative strength tests were conducted on four beam variants, including those reinforced with fiber reinforcement. The results indicate that despite having a lower load-bearing capacity compared to prefabricated beams, fiber-reinforced concrete can be a viable option in certain scenarios, particularly where lintels do not serve a primary load-bearing function. Additionally, the article aligns with contemporary construction trends, such as the use of waste materials in concrete reinforcement, by considering the incorporation of plastic fibers as an alternative to traditional steel reinforcement. The use of recycled fibers – including plastics, reclaimed steel, and carbon fibers from the aerospace and automotive industries – can enhance the mechanical properties of concrete while reducing its environmental impact. These innovations align with the principles of the circular economy, providing both ecological and economic benefits. The research findings suggest that fiber-reinforced concrete lintels can contribute to reducing construction costs and limiting the consumption of natural resources, making them a compelling alternative to conventional solutions.
This study focuses on enhancing the mechanical properties and flame retardancy of epoxy composites by incorporating durian peel fiber (DPF) treated with an eco-friendly chemical method using a Ca(OH)₂ suspension solution at three different concentrations (1.0%, 2.0%, and 3.0% w/v). The composite samples (DPF-1, DPF-2, and DPF-3) were evaluated through mechanical tests (tensile strength, flexural strength, compressive strength, and Izod impact strength) and flame resistance tests, including LOI (Limiting Oxygen Index) and UL 94HB (Horizontal Burning Test). The results indicate that DPF-2 (2.0% Ca(OH)₂) exhibits the best overall properties, with a densely packed char layer after combustion, reducing flame propagation. Higher Ca(OH)₂ content (3.0%) led to uneven fiber expansion, lowering some mechanical properties. The LOI and UL 94HB results of DPF-2 also demonstrated significantly improved flame retardancy. Research on the chemical treatment of durian peel fibers using green chemicals (Ca(OH)2) to protect the environment. Improving the efficiency of applying natural fibers from industrial waste in the fabrication of potential composite materials for structures in the automotive, construction and aerospace industries with sustainable value.
ABSTRACT Carbon dioxide removal (CDR) is indispensable for reaching the German climate neutrality target as a complementary strategy alongside reducing and avoiding greenhouse gas emissions. Biomass can be used in various ways to deliver bio‐based CDR, including Bioenergy with Carbon Capture and Storage (BECCS), natural sink enhancement, and biomass‐based construction materials. By focusing on bio‐based solutions, actions can be streamlined to achieve both CDR and a range of co‐benefits; for example, in terms of ecosystem services. The ramp‐up of bio‐based CDR in Germany is driven by a diverse set of factors. In this study, scenarios were developed that allow for exploring these factors in a set of narratives. The selection of key drivers followed the PESTEL approach (Policy, Environmental, Social, Technological, Economic, and Legal aspects), to which the Biomass category was added. Desirable net‐zero futures and drivers identified in stakeholder surveys, interviews, and workshops were translated into consistent scenario storylines. These represent diverse bio‐based CDR portfolios that differ in the implementation level of single concepts and in the overall contribution to negative emissions for Germany in 2045, considering the national potentials for different CDR options. The scenarios encompass (1) a focus on cost efficiency, (2) prioritizing decentralized options and natural sinks, (3) larger amounts of bio‐based CDR (skyrocketing), and (4) little support for bio‐based CDR (roadblock). The scenario storylines and drivers can inform modeling for cost‐optimized implementation and paint a picture of potential developments for stakeholders. They can also serve as a basis for compiling bio‐based value chains with maximum removal capacities that deliver a series of additional system benefits.
Renewable energy sources, Energy industries. Energy policy. Fuel trade
Abstract To improve the efficiency of hybrid energy storage double-layer capacity allocation in photovoltaic power distribution networks, this study proposes a hybrid energy storage double-layer capacity allocation model based on fundamental frequency equivalent energy steady-state gain control. The innovation of this model lies in constructing specific constraints and control parameters, using a multi-parameter fusion configuration method to analyze the equivalent energy fusion characteristics, and conducting an in-depth analysis of power flow constraints under optimal power flow. At the same time, an adaptive steady-state gain control mechanism is introduced, fully considering the diversity of distributed nodes in multi-photovoltaic power distribution networks and the differences in new energy output and load demand, achieving precise allocation of dual-layer capacity for hybrid energy storage. Through simulation experiments, the model demonstrates high reliability and minimizes network losses while ensuring the safety of the distribution network and the reliability of power supply. The experimental results show that the model achieves optimal operation, optimal charging and discharging power configuration, and energy storage state value optimization, effectively verifying its feasibility and superiority in the application of photovoltaic power distribution networks.
Renewable energy sources, Energy industries. Energy policy. Fuel trade
Masoud Aman Mohammadi, Adel Mirza Alizadeh, Samira Dakhili
et al.
ABSTRACT Bacterial nanocellulose (BNC) is an eco‐friendly biomaterial celebrated for its exceptional physicochemical properties, making it valuable across diverse industries. Produced through bacterial fermentation, BNC exhibits high mechanical strength, biocompatibility, and biodegradability, ideal for applications in biomedicine, environmental remediation, and food science. In the food sector, BNC serves as a sustainable alternative to synthetic additives and packaging. It functions as a thickening, stabilizing, and gelling agent, improving texture, consistency, and shelf life in products like sauces, dairy, and gluten‐free baked goods. Additionally, BNC's role as an edible coating and biodegradable packaging material offers innovative solutions for food preservation, reducing spoilage, and addressing plastic waste concerns. This review outlines BNC's production processes, emphasizing bacterial strain selection, culture media optimization, and fermentation control. It also highlights its multifaceted applications in enhancing food safety, packaging, and quality. Despite challenges such as high production costs, scalability issues, and regulatory compliance, future directions, including genetically engineered BNC, nanocomposites, and smart technology integration, suggest promising advancements. BNC is poised to transform food production and packaging by fostering more sustainable, innovative practices.
Food processing and manufacture, Toxicology. Poisons
The solar parabolic trough collector technology is one of the most reliable technologies in the field of solar thermal. This is due to the fact that temperatures as high as 300-400°C can be achieved using this technology. This technology is used for hot water production, process steam requirement, power generation and many more. In the present work a thermal study on a parabolic trough collector is performed to observe the range of steam temperatures to be useful for small scale industry applications. The paper presents the steam temperatures, temperature profiles for the solar collector components and the solar radiation variation over the day. On the basis of several experiments it was found that in the parabolic trough collector the maximum pressure of 221 bar and the maximum steam temperature of around 374°C is achieved. From the experimental data obtained, the variation in temperatures with solar radiation on clear and intermittent cloud cover is discussed. From the results it can be concluded that this system may be used successfully for production of hot water and steam for use in many different industries such as dairy, textile, paper, timber, bricks, chemicals, plastics etc. Hot water and steam from solar system can be used in small scale industries for rose water making, cooking, drying, sterilization, food processing etc. In this paper a design for rose water making process through parabolic trough collector has been proposed. Keywords: Solar concentrator, parabolic trough, cylindrical parabolic collector, steam, temperature profile, industry applications.
Mir Imtiaz Mostafiz, Eunseob Kim, Adrian Shuai Li
et al.
Cutting state monitoring in the milling process is crucial for improving manufacturing efficiency and tool life. Cutting sound detection using machine learning (ML) models, inspired by experienced machinists, can be employed as a cost-effective and non-intrusive monitoring method in a complex manufacturing environment. However, labeling industry data for training is costly and time-consuming. Moreover, industry data is often scarce. In this study, we propose a novel adversarial domain adaptation (DA) approach to leverage abundant lab data to learn from scarce industry data, both labeled, for training a cutting-sound detection model. Rather than adapting the features from separate domains directly, we project them first into two separate latent spaces that jointly work as the feature space for learning domain-independent representations. We also analyze two different mechanisms for adversarial learning where the discriminator works as an adversary and a critic in separate settings, enabling our model to learn expressive domain-invariant and domain-ingrained features, respectively. We collected cutting sound data from multiple sensors in different locations, prepared datasets from lab and industry domain, and evaluated our learning models on them. Experiments showed that our models outperformed the multi-layer perceptron based vanilla domain adaptation models in labeling tasks on the curated datasets, achieving near 92%, 82% and 85% accuracy respectively for three different sensors installed in industry settings.