Biochar is a promising agent for wastewater treatment, soil remediation, and gas storage and separation. This review summarizes recent research development on biochar production and applications with a focus on the application of biochar technology in wastewater treatment. Different technologies for biochar production, with an emphasis on pre-treatment of feedstock and post treatment, are succinctly summarized. Biochar has been extensively used as an adsorbent to remove toxic metals, organic pollutants, and nutrients from wastewater. Compared to pristine biochar, engineered/designer biochar generally has larger surface area, stronger adsorption capacity, or more abundant surface functional groups (SFG), which represents a new type of carbon material with great application prospects in various wastewater treatments. As the first of its kind, this critical review emphasizes the promising prospects of biochar technology in the treatment of various wastewater including industrial wastewater (dye, battery manufacture, and dairy wastewater), municipal wastewater, agricultural wastewater, and stormwater. Future research on engineered/designer biochar production and its field-scale application is discussed. Based on the review, it can be concluded that biochar technology represents a new, cost effective, and environmentally-friendly solution for the treatment of wastewater.
In an effort to improve operational efficiency, healthcare services around the world have adopted process improvement methodologies from the manufacturing sector, such as Lean Production. In this paper we report on four multi-level case studies of the implementation of Lean in the English NHS. Our results show that this generally involves the application of specific Lean 'tools', such as 'kaizen blitz' and 'rapid improvement events', which tend to produce small-scale and localised productivity gains. Although this suggests that Lean might not currently deliver the efficiency improvements desired in policy, the evolution of Lean in the manufacturing sector also reveals this initial focus on the 'tool level'. In moving to a more system-wide approach, however, we identify significant contextual differences between healthcare and manufacturing that result in two critical breaches of the assumptions behind Lean. First, the customer and commissioner in the private sector are the one and the same, which is essential in determining 'customer value' that drives process improvement activities. Second, healthcare is predominantly designed to be capacity-led, and hence there is limited ability to influence demand or make full use of freed-up resources. What is different about this research is that these breaches can be regarded as not being primarily 'professional' in origin but actually more 'organisational' and 'managerial' and, if not addressed could severely constrain Lean's impact on healthcare productivity at the systems level.
In this review, several cost-effective thin-film coating methods, which include dip-coating, spin-coating, spray-coating, blade-coating, and roll-coating, are presented. Each method has its own set of advantages and disadvantages depending on the proposed application. Not all of them are appropriate for large-scale production due to their certain limitations. That is why the coating method should be selected based on the type and size of the substrate, including the thickness and surface roughness of the required thin films. The sol–gel method offers several benefits, such as simplicity in fabrication, excellent film uniformity, the capacity to cover surfaces of any size and over vast areas, and a low processing temperature. Nevertheless, these coating methods are somewhat economical and well managed in low-budget laboratories. Moreover, these methods offer thin films with good homogeneity and low-surface roughness. Furthermore, some other thin-film deposition methods, for instance, physical vapor deposition (PVD) and chemical vapor deposition (CVD), are also discussed. Since CVD is not restricted to line-of-sight deposition, a characteristic shared by sputtering, evaporation, and other PVD methods, many manufacturing methods favor it. However, these techniques require sophisticated equipment and cleanroom facilities. We aim to provide the pros and cons of thin-film coating methods and let the readers decide the suitable coating technique for their specific application.
M. Fabris, Raffaela M. Abbriano, M. Pernice
et al.
Mankind has recognized the value of land plants as renewable sources of food, medicine, and materials for millennia. Throughout human history, agricultural methods were continuously modified and improved to meet the changing needs of civilization. Today, our rapidly growing population requires further innovation to address the practical limitations and serious environmental concerns associated with current industrial and agricultural practices. Microalgae are a diverse group of unicellular photosynthetic organisms that are emerging as next-generation resources with the potential to address urgent industrial and agricultural demands. The extensive biological diversity of algae can be leveraged to produce a wealth of valuable bioproducts, either naturally or via genetic manipulation. Microalgae additionally possess a set of intrinsic advantages, such as low production costs, no requirement for arable land, and the capacity to grow rapidly in both large-scale outdoor systems and scalable, fully contained photobioreactors. Here, we review technical advancements, novel fields of application, and products in the field of algal biotechnology to illustrate how algae could present high-tech, low-cost, and environmentally friendly solutions to many current and future needs of our society. We discuss how emerging technologies such as synthetic biology, high-throughput phenomics, and the application of internet of things (IoT) automation to algal manufacturing technology can advance the understanding of algal biology and, ultimately, drive the establishment of an algal-based bioeconomy.
Fungal contamination of food and animal feed, especially by mycotoxigenic fungi, is not only a global food quality concern for food manufacturers, but it also poses serious health concerns because of the production of a variety of mycotoxins, some of which present considerable food safety challenges. In today's mega-scale food and feed productions, which involve a number of processing steps and the use of a variety of ingredients, fungal contamination is regarded as unavoidable, even good manufacturing practices are followed. Chemical preservatives, to some extent, are successful in retarding microbial growth and achieving considerably longer shelf-life. However, the increasing demand for clean label products requires manufacturers to find natural alternatives to replace chemically derived ingredients to guarantee the clean label. Lactic acid bacteria (LAB), with the status generally recognized as safe (GRAS), are apprehended as an apt choice to be used as natural preservatives in food and animal feed to control fungal growth and subsequent mycotoxin production. LAB species produce a vast spectrum of antifungal metabolites to inhibit fungal growth; and also have the capacity to adsorb, degrade, or detoxify fungal mycotoxins including ochratoxins, aflatoxins, and Fusarium toxins. The potential of many LAB species to circumvent spoilage associated with fungi has been exploited in a variety of human food and animal feed stuff. This review provides the most recent updates on the ability of LAB to serve as antifungal and anti-mycotoxigenic agents. In addition, some recent trends of the use of LAB as biopreservative agents against fungal growth and mycotoxin production are highlighted.
The integration of extended reality (XR) into industrial robotics requires robust middleware solutions capable of bridging heterogeneous systems, protocols, and user interactions. This paper presents a novel middleware framework designed to connect industrial robots with XR devices such as the HoloLens. The architecture employs a hybrid communication layer that combines MQTT (Message Queuing Telemetry Transport) and ØMQ (Zero Message Queue), leveraging the Sparkplug Robotics API model for robot data and publisher–subscriber streaming for XR camera feeds. A Redis cache database is introduced to ensure efficient data handling and prevent data corruption. On the robot side, the system is built on ROS 2 (Robot Operating System) and connects to proprietary industrial protocols through dedicated bridges, enabling seamless interoperability. Spatial alignment between physical robots and XR overlays is achieved using ArUco marker-based synchronization, while real-time kinematic and process data are visualized directly in XR. The middleware further supports bidirectional interaction, allowing users to adjust parameters and issue commands through XR devices. Beyond functionality, safety considerations are incorporated by integrating human–robot interaction safeguards and ensuring compliance with industrial communication standards. The proposed solution demonstrates how middleware-driven XR integration enhances transparency, control, and safety in robotic manufacturing processes, laying the foundation for greater efficiency and adaptability in Industry 4.0 environments.
Abstract While reducing current collector thickness improves battery energy density, further thinning commercial collectors compromises mechanical integrity and increases manufacturing costs. Here, we overcome these limitations by developing a lightweight (1.23 mg·cm-2), cost-effective cellulose composite membrane (CCM) via solution casting from an alkali/urea solvent. This CCM, composed of modified carbon nanotubes and natural cellulose, exhibits high electrochemical stability and flexibility, serving as both cathode and anode current collectors. CCM-containing batteries show 99.40% capacity retention after 500 cycles at 3 C. Replacing commercial collectors with CCM reduced their battery proportion to 6.23% and increased gravimetric energy density by 41.32%, while also reducing current collector costs by 50.36%. The CCM, produced through regenerated cellulose technology, is suitable for industrial-scale production, offering a strategy to enhance battery energy density with lightweight, low-cost current collectors.
Materials of engineering and construction. Mechanics of materials
Yoshio Kobayashi, Atsushi Takaichi, Yuka Kajima
et al.
The laser-beam powder-bed fusion (PBF-LB) method enables the semi-automatic fabrication of complex three-dimensional structures, making it useful for dental prostheses. However, residual stress during fabrication can cause deformation. Herein, we applied the segmented-scan strategy to three-unit fixed dental prostheses (FDPs) and evaluated its effects on residual stress and fit accuracy compared to conventional methods. Three-unit FDPs consisting of two abutments and a pontic were fabricated using a PBF-LB machine with Co-Cr-Mo powder. In the segmented-scan group, pontics and abutments were scanned separately to shorten the scan vector. Fit accuracy was assessed by measuring the gap between the abutment and the FDPs. Residual stress was measured in the X and Y directions at three points using X-ray diffraction, while CT scans were used to count internal microstructures. The residual stress was lower in the X-direction in the segmented-scan group (24.61–217.17 MPa, respectively) than in the control group (187.70–293.71 MPa, respectively). However, no significant differences in fit accuracy were observed (<i>p</i> < 0.05). The segmented-scan strategy reduced residual stress in the X-direction but did not improve the fit accuracy. Applying this strategy to dental prosthetic devices can shorten the scan vector and reduce residual stress.
The prevalence of kidney stones, a significant urological health concern, necessitates advancements in the management and treatment methods, particularly in the domain of ureteral stents. This study explores the feasibility and potential benefits of utilizing three biodegradable polymers—Polylactic Acid (PLA), Tough Polylactic Acid (Tough PLA), and Polylactic Acid/Poly-hydroxybutyrate (PLA/PHB)—for the fabrication of 3D-printed ureteral stents tailored to patient-specific needs. Through the integration of CAD and Fused Deposition Modeling (FDM) 3D printing technology, ureteral stents were successfully produced, demonstrating key advantages in terms of biodegradability and mechanical properties. The study involved a rigorous evaluation of the biodegradability, tensile strength, and hardness of the stents. Biodegradability tests performed in a simulated physiological environment revealed that PLA/PHB and Tough PLA stents exhibited higher degradation rates compared to PLA, aligning with the requirements for temporary urinary tract support. Tensile strength testing indicated that while PLA showed the highest strength, PLA/PHB and Tough PLA stents provided beneficial ductility, reducing the risk of blockage due to material breakage. Hardness assessments classified PLA/PHB stents as medium soft, optimizing patient comfort during the stenting period. These findings demonstrate the potential of using biodegradable polymers to produce ureteral stents that could eliminate the need for removal procedures, thereby enhancing patient recovery and comfort.
Increasing microalgae biodiesel production through improvement of biomass production is reversible and is often considered economically unprofitable. This research aims to determine the effect of various levels of media salinity stress in producing microalgae strains that have higher oil content than the original population. Three species of microalgae are known to be capable of producing biodiesel, namely Chaeticheros amini, Nannochloropsis oculata and Nitzschia spp. isolated from Sekotong coastal waters, West Lombok, then cultivated in bioreactor systems for seven days with salinity stress treatment to produce strains. The resulting strains were cultivated again, but without salinity stress. Microalgae cell density was observed every day and harvested on the seventh day. The resulting microalgae biomass was extracted in stages to produce biodiesel oil. The results showed that after cultivation under salinity stress, S20, S25, S30, and S35 strains were produced from each of these species. Under salinity stress, all strains except S25 of Nitzschia spp. showed a higher maximum cell density compared to the original population and were reversible respectively. Each of the S25 strains of C. amini and N. oculata, S20 and S25 strains of Nitzschia spp., had a higher oil content than the original population, each of which was permanent
Maximilian Arras, Tim-Fabian Jeandey, Linwei Ma
et al.
Hydrogen, as an energy carrier, is seen as a prominent candidate for the deep decarbonization of hard-to-abate sectors. Realizing the vision of a low-carbon hydrogen economy requires a complex transition pathway based on international collaboration, national efforts, and project practices. First, this paper aims to provide a comprehensive, multi-tier review of hydrogen development. The results indicated that an increasing number of countries are formulating ambitious hydrogen strategies. However, in China, the world's most important market for hydrogen, national-level strategic guidance remains insufficient. At the same time, provincial government plans far exceed the conservative national targets, resulting in significant project activity. China’s initial position is advantageous due to its capabilities in electrolyzer manufacturing capacity, cost-efficient supply chains, and effective project implementation. To address the mismatch between top-down coordination and bottom-up development, the objective of this paper is to propose an enhanced roadmap for China using a hydrogen-adapted Energy-Sustainability-Governance-Operation (H-ESGO) framework. This framework aligns quantitative targets with key actions over a long-term horizon, providing a balanced techno-centric perspective on scaling hydrogen supply chains and system integration synergies. For China, the findings show a roadmap to overcome strategic shortcomings by taking immediate action with decentralized hydrogen production. This is significant for guiding future policy and practical steps towards a low-carbon hydrogen economy.
Chemical engineering, Computer engineering. Computer hardware
Peng Yan, Ahmed Abdulkadir, Paul-Philipp Luley
et al.
Automating the monitoring of industrial processes has the potential to enhance efficiency and optimize quality by promptly detecting abnormal events and thus facilitating timely interventions. Deep learning, with its capacity to discern non-trivial patterns within large datasets, plays a pivotal role in this process. Standard deep learning methods are suitable to solve a specific task given a specific type of data. During training, deep learning demands large volumes of labeled data. However, due to the dynamic nature of the industrial processes and environment, it is impractical to acquire large-scale labeled data for standard deep learning training for every slightly different case anew. Deep transfer learning offers a solution to this problem. By leveraging knowledge from related tasks and accounting for variations in data distributions, the transfer learning framework solves new tasks with little or even no additional labeled data. The approach bypasses the need to retrain a model from scratch for every new setup and dramatically reduces the labeled data requirement. This survey first provides an in-depth review of deep transfer learning, examining the problem settings of transfer learning and classifying the prevailing deep transfer learning methods. Moreover, we delve into applications of deep transfer learning in the context of a broad spectrum of time series anomaly detection tasks prevalent in primary industrial domains, e.g., manufacturing process monitoring, predictive maintenance, energy management, and infrastructure facility monitoring. We discuss the challenges and limitations of deep transfer learning in industrial contexts and conclude the survey with practical directions and actionable suggestions to address the need to leverage diverse time series data for anomaly detection in an increasingly dynamic production environment.
Additive Manufacturing (AM) has emerged as an innovative technology that gives designers several advantages, such as geometric freedom of design and less waste. However, the quality of the parts produced is affected by different design and manufacturing parameters, such as the part orientation, the nozzle temperature and speed, the support material, and the layer thickness. In this context, the layer thickness is considered an important AM parameter affecting the part quality and accuracy. Thus, in this paper, a new adaptative slicing method based on the cusp vector and the surface deviation is proposed with the aim of minimizing the dimensional defects of FFF printed parts and investigate the impact on the dimensional part tolerancing. An algorithm is developed to automatically extract data from the STL file, select the build orientation, and detect intersection points between the initial slicing and the STL mesh. The innovation of this algorithm is exhibited via adapting the slicing according to the surface curvature based on two factors: the cusp vector and the surface deviation. The suggested slicing technique guarantees dimensional accuracy, especially for complex feature shapes that are challenging to achieve using a uniform slicing approach. Finally, a preview of the slicing is displayed, and the G-code is generated to be used by the FFF machine. The case study consists of the dimensional tolerance inspection of prototypes manufactured using the conventional and adaptive slicing processes. The proposed method’s effectiveness is investigated using RE and CMM processes. The method demonstrates its reliability through the observed potential for accuracy improvements exceeding 0.6% and cost savings of up to 4.3% in specific scenarios. This reliability is substantiated by comparing the resulting dimensional tolerances and manufacturing costs.
In this paper, we construct an $S^1$-equivariant version of the relative symplectic cohomology developed by Varolgunes. As an application, we construct a relative version of Gutt-Hutchings capacities and a relative version of symplectic (co)homology capacity. We will see that these relative symplectic capacities can detect the diplaceability and the heaviness of a compact subset of a symplectic manifold. We compare the first relative Gutt-Hutchings capacity and the relative symplectic (co)homology capacity and prove that they are equal to each other under a convexity assumption.