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Biobased barrier dispersion coating from solvent shifting of functionalized lignin inside cellulose nanofibers aqueous suspensions
No full textIn this study, we present a simple one-pot approach to formulate barrier dispersions by combining nanoscaled cellulose and lignin, while harnessing the hydrophobizing effect of tall oil fatty acid (TOFA) modification. Using an in situ solvent-shifting method, unmodified lignin and TOFA-esterified lignin solutions were directly incorporated into aqueous microfibrillated cellulose (MFC) suspensions, enabling the in situ formation of stable lignin nanoparticles (LNPs and TOFA-LNPs) within the MFC matrix. Nanopapers prepared from TOFA-LNP:MFC dispersion with a ratio of 1:2 exhibited excellent barrier properties, with a water vapor transmission rate of 6 g/m²·day (50 % RH, 23 °C), an oil Cobb1800 value of 0.3 g/m², and a water Cobb1800 value of 12 g/m². The results demonstrate that TOFA modification of lignin and its incorporation within the MFC matrix as nanoparticles, facilitates the formation of dense, uniform films with strong resistance to moisture and oil. To gain a deeper understanding of the system, surface-sensitive quartz crystal microbalance with dissipation monitoring (QCM-D) and atomic force microscopy (AFM) were used to analyze how the TOFA modification of lignin affected its physicochemical interactions within cellulose fibrils. Furthermore, humidity-controlled QCM-D measurements were used to analyze the effect of lignin-based nanoparticles on the water vapor adsorption behavior of MFC at different relative humidities providing new insights into their barrier performance, explored here for the first time. Finally, the successful application of dispersion coatings onto commercial fiber-based substrates demonstrates their industrial potential. This work introduces a versatile and scalable route to fully biobased coatings, advancing the transition toward circular and sustainable packaging solutions.</p
Surface characteristics and repulpability performance of cellulose-fiber-based packaging materials coated with aqueous dispersions of wood-bark-derived suberin
No full textRecyclability is an important feature of packaging materials. Although packaging materials made from cellulose fibers such as those found in paper or paperboard can typically be recycled through repulping, the application of coatings, especially polymeric plastic coatings, often impairs their recyclability, leading to increased amounts of rejects and low fiber yields. Herein, the surface properties and repulpability performance of paperboard substrates coated with aqueous dispersion of wood bark-derived suberin, stabilized using synthetic surfactants or bio-based surfactants, were investigated. The results were compared with commercial polyethylene coated material. Surface properties of the materials were investigated through surface imaging, water absorption, and wettability measurements. Repulpability was evaluated based on the amounts of rejects after two screening stages. Fiber analysis was performed for the materials that passed both the screenings. All suberin-coated materials showed hydrophilic surface characteristics and greater water absorbency than the reference material. Repulpability analysis revealed that the suberin coatings resulted in a lower amount of rejects than coated reference material. These results highlight the potential of suberin coatings in developing recyclable and sustainable packaging solutions for cellulose fiber substrates
High temperature fast pyrolysis of waste biomass in a solar-assisted quartz drop-tube reactor
No full textSolar-assisted pyrolysis is a sustainable process for converting biomass into syngas, bio-oil, and biochar using renewable solar thermal energy, with a potentially zero carbon footprint. It generates both high-value gaseous and liquid fuels while transforming the atmospheric CO2 captured in biomass in the form of solid carbon that can be long-term sequestrated or valorized. The EU's target of reducing net greenhouse gas emissions to at least 55% by 2030 sets the stage for effective measures to limit carbon emissions and achieve a sustainable future. This study presents the development of an innovative fast pyrolysis quartz drop-tube reactor using concentrated solar power and its performance for bio-waste valorization. Extensive raw material characterization was carried out, which provides valuable insights into demolition wood and rye straw feedstocks properties. Solar pyrolysis runs revealed key dependencies of product yields on operational parameters such as feedstock type, nitrogen gas flow rate (0.7-1.4 NL/min), and heating profile in the temperature range 800-900 °C. Operation at such high temperatures promoted gas production (>50% gas yield in mass) over liquid and solid products. In similar conditions, rye straw showed higher gas yield as compared to demolition wood. In addition, preheating or increasing the gas residence time favored gas production with negligible impact on gas composition. The solar drop tube pyrolysis reactor appears as a sustainable option to upgrade waste feedstocks into valuable products using concentrated solar energy with net zero CO2 emission
Biosynthetic optical waveguide interface integration using biomimetic - <i>de novo</i> design ELP for optoelectronic applications
Get PDFThe integration of biologically inspired materials into photonic device fabrication offers a promising route toward sustainable and biocompatible alternative to conventional in inorganic or petroleum based synthetic materials used in optoelectronic systems. In this work, we present a biosynthetic approach for waveguide fabrication utilizing a biomimetic - de novo designed elastin-like polypeptide (ELP) formulated into an all-water-based photoresist compatible with two-photon polymerization (2PP). The ELP was genetically engineered and recombinantly produced in microbes for enhanced molecular stability, a critical feature for withstanding both localized and bulk temperature increases that occur during high-intensity laser exposure during printing. The resulting ELP formulation supported direct writing of waveguide architecture without the need for organic solvents, harsh processing steps, or post-functionalization. This aqueous resist formulation exhibits high stability during printing and retains its structural integrity upon curing, making it a promising candidate for environmentally friendly, soft-material photonics. This work establishes a foundation for using biosynthetic polypeptides in the fabrication of functional photonic elements and demonstrates a step toward greener, protein-based optoelectronic manufacturing technologies
An Efficient Rescheduling Scheme for Prioritizing Safety Messages in VANETs
No full textThe growing demand among users of mobile devices to swiftly access information items, coupled with the swift creation of new services and applications in automotive environments has led to the introduction of road side units (RSUs) along the roads. These RSUs facilitate the broadcasting of data during communication between the infrastructure and the vehicle. Vehicular Ad-hoc Networks (VANETs) face challenges such as frequent connection changes, a sizeable topological region, location variations, and varying speed of vehicles. In vehicular scenarios, messages are typically categorized into safety and non-safety messages. Efficient broadcasting of safety messages in vehicular scenarios requires message scheduling, with the highest priority given to crucial messages. This paper proposes an Optimum efficient scheme for organizing messages in VANETs. This scheme reschedules messages based on parameters such as data size, quantity of data sought, speed of vehicle, and message deadline. To differentiate messages pertaining to safety with those from non-safety messages, a message factor is taken into consideration. Priorities for service requests or messages are established using these parameters, and messages are rescheduled accordingly. Simulation results demonstrate the superior performance of the proposed algorithm compared to recent and relevant schemes.</p
Comparative Analysis of YOLOv8 and YOLOv9 on a Unified Traffic Sign Dataset
No full textEmploying a unique set of traffic signs, this research examines the effectiveness of two versions of the You Only Look Once (YOLO) object detection framework: YOLOv8 and YOLOv9. The aim is to provide a detailed understanding of each version’s strengths and weaknesses in traffic sign detection, with implications for enhancing real-world object detection in dynamic traffic scenarios. Preliminary findings indicate that YOLOv9 outperforms YOLOv8, demonstrating higher precision and F1-score. This highlights YOLOv9’s potential for robust traffic sign detection solutions. Despite assessment, our research represents an essential contribution to the discipline of computer vision including applications to traffic sign recognition. The study’s results offer a useful resource for practitioners and researchers selecting optimal models for similar applications, ultimately contributing to developing smarter and more efficient transportation networks.</p
Enhancing Safety in Autonomous Vehicles Using Advanced Deep Learning-Based Pothole Detection
No full text Autonomous vehicles possess the potential to revolutionize transportation by significantly enhancing safety and efficiency. However, their success hinges on overcoming numerous challenges including the detection of potholes which pose significant risks to vehicles and passengers. Consequently the identification and remediation of these obstacles are crucial for the safety of autonomous systems. This research introduces YOLO v8 as a formidable solution for pothole detection predicated on the latest You Only Look Once (YOLO) algorithm. Utilizing deep learning techniques this system identifies potholes in real-time enabling autonomous vehicles to circumvent potential hazards and diminish the risk of accidents. Extensive testing with publicly accessible datasets reveal that this approach surpasses contemporary state-of-the-art methodologies in both precision and speed. Various data augmentation strategies are also examined to further enhance detection performance. Empirical evidence indicates that the YOLO v8-based pothole detection system exhibits superior efficacy compared to other analogous systems. This advancement signifies that autonomous driving can be rendered safer and more reliable marking a pivotal milestone in the enhancement of road safety.</p
Developing EU-CEM:A Common Evaluation Methodology for Evaluating Co-operative, Connected and Automated Mobility
No full textCo-operative, Connected and Automated Mobility (CCAM) is of increasing interest to the transport community across the world, though is still maturing. The Horizon Europe project FAME is developing a European framework for testing CCAM on public roads. As part of this, a common evaluation methodology (EU-CEM) is being developed, which provides guidance on how to set up and carry out an evaluation or assessment of direct and indirect impacts of CCAM solutions on different user groups and wider society. Objectives include ensuring that evaluations can be complementary planned with results that are easy to compare, as well as establishing a common vocabulary to support projects in the CCAM community. This paper sets out how the EU-CEM is being developed and embedded into CCAM research in Europe, with a particular emphasis on how the project has adopted an agile and iterative approach to the CEM development alongside meaningful and sustained engagement with stakeholders.</p
Insights into microbial sampling of ultra-low biomass, ultra-deep, hypersaline fluids fluids in Otaniemi, Finland
No full textIn the efforts to find new, environmentally friendly means for district heat production, two ultra-deep production wells were drilled in Otaniemi, Finland, reaching depths of approximately 6 km. At this depth, the fluids are saline, reaching 200 g L-1 TDS, anoxic, approximately 100°C hot and experiencing a hydrostatic pressure of 500 bar. The fluids are slightly alkaline and oligotrophic but contain CH4 and H2. In an effort to study the microbiological composition of these fluids, samples were collected using a positive displacement sampler operated by ICDP’s Operational Support Group (OSG) in September, 2024. The sample volumes were ~600 mL/sample. The sampler was cleaned with 70% ethanol and rinsed with sterile MilliQ water. Contamination control samples were collected by filling the samplers with sterile water and collecting and treating the control sample in the same way as the actual samples. Contamination control samples were also collected from the water-glycol solution and mineral oil used for the sampler operation. The samples were studied by epifluorescence microscopy, DNA was extracted by first collecting the biomass on 0.1 m pore-size filters and the filtrate was additionally precipitated with PEG. The microbial communities were characterized by qPCR, amplicon sequencing and metagenomics. In addition, cultures targeting heterotrophic and autotrophic thermophiles were set up from all samples in anaerobic infusion bottles. The first results indicate that even low-level contamination from reagents, equipment and sample handling is detrimental to the study. The amount of indigenous microorganisms was < 5 bacterial 16S rRNA gene copies mL-1 measured from the 0.1 m pore-size filter DNA extraction, whereas the contamination control (MilliQ water) contained between 160 – 380 gene copies mL-1. The filtrate, i.e. <0.1 m sized cells, the copy numbers were 17 – 86 mL-1, whereas the MilliQ contamination control samples contained 15 – 21 bacterial 16S rRNA gene copies mL-1. Epifluorescence microscopy corroborated the results. Amplicon sequencing showed a high proportion of contamination from sampling equipment, sample handling and laboratory reagents. Stringent curation of the data by removing all typical human contaminants and all taxa present also in the contamination controls, revealed a bacterial community mostly consisting of members of the Patescibacteria phylum. These bacteria are ultra-small and are often found in deep groundwater environments. Our study is the first to show Patescibacteria as the dominating component of the community in ultra-deep, hot, saline fluids, although their numbers are low. Culture based and metagenomic analyses are ongoing to reveal the first clues to the metabolic capacities of this phylum. <br/
Defining Dimension Metrics for Evaluating Overall Prompting Effectiveness
No full textThe integration of Large Language Models (LLMs) into education, particularly in software engineering and IT, presents opportunities and challenges. While LLMs support problem-solving and code generation following the specifications, their effectiveness often depends on students’ ability to formulate precise and effective prompts. To address this, frameworks known as meta-prompts have been proposed. However, the impact of specific frameworks on students’ prompt-writing skills and learning outcomes remains underexplored. This study examines two didactic frameworks–Iterative Feedback and Reflection (IFR) and Adaptive Learning Progression (ALP)—to assess their effectiveness in enhancing prompt-writing skills and learning engagement. We propose complementary metrics within the Overall Prompting Effectiveness (OPE) framework, defined through three key dimensions: Adaptability, Relevance, and Efficiency. These dimensions encapsulate essential components for effective interaction with LLMs in educational contexts. The design of controlled experiment involves IT-engineering students divided into two groups, each using one of the two different didactical meta-prompt-enhanced frameworks. The IFR group engages in iterative cycles of prompt refinement and self-reflection, while the ALP group utilizes adaptive meta-prompts that dynamically adjust task complexity based on performance. Data collection focuses on OPE-aligned metrics, including the number of prompt iterations, time efficiency, response alignment, and learning progress self-rating, allowing for a comparative analysis of the frameworks’ impacts on learning outcomes. Our work establishes and evaluates these metrics, contributing to research in LLM-assisted learning. It addresses gaps in prompt engineering by showing how IFR and ALP frameworks can be utilized to enhance skill development and offers guidance on integrating LLMs into educational contexts for better interactive learning