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Synergistic incorporation of calcium and zinc fertilizers in superabsorbent polymers
No full textMineral fertilizers and water are essential for food production, and their sustained delivery is imperative to maximize crop yields. During heavy rainfalls, fertilizers can leach into the environment. During drought, fertilizers cannot dissolve in the soil. Superabsorbent polymers (SAP) can improve soil water retention capacity, by absorbing water during rainfall and releasing it during dry periods. In this study, we have integrated varying levels of calcium nitrate and zinc sulfate in an SAP to achieve fertilizer-SAP composites. Fertilizer-SAP composites can both help alleviate fertilizer leaching during heavy rainfall and facilitate their dissolution during dry periods, thank to moisture they absorb from the soil. SAP composites can also slow down the release of nutrients and keep them closer to the root area, for optimum plant uptake. Furthermore, we have observed a synergistic effect of nutrient incorporation in SAP, manifested by the enhanced water uptake of composites compared to pure SAP. For instance, SAP composites with 17 wt% zinc sulfate and calcium nitrate achieved superior water absorptions of 1462 and 1626 g/g, respectively. In comparison, pure SAP in this study reached approximately 800 g/g, and most commercial SAPs have water absorption capacities of 500 g/g. Suggested SAP application is often reported as 15–45 kg per hectare, in the literature. In comparison, the recommended zinc and calcium doses are as low as 3–5 kg/ha. Therefore, by a single fertilizer-SAP application, farmers can cover both the SAP and zinc, or calcium demands of their fields
Human-in-the-loop optimization of perceived realism of multi-modal haptic rendering under conflicting sensory cues
No full textDuring haptic rendering, a visual display and a haptic interface are commonly utilized together to elicit multi-sensory perception of a virtual object, through a combination and integration of force-related and movement-related cues. In this study, we explore visual-haptic cue integration during multi-modal haptic rendering under conflicting cues and propose a systematic means to determine the optimal visual scaling for haptic manipulation that maximizes the perceived realism of spring rendering for a given haptic interface. We show that the parameters affecting visual-haptic congruency can be effectively optimized through a qualitative feedback-based human-in-the-loop (HiL) optimization to ensure a consistently high rating of perceived realism. Accordingly, the multi-modal perception of users can be successfully enhanced by solely modulating the visual feedback without altering the haptic feedback, to make virtual environments feel stiffer or more compliant, significantly extending the range of perceived stiffness levels for a haptic interface. We extend our results to a group of individuals to capture the multi-dimensional psychometric field that characterizes the cumulative effect of feedback modalities utilized during sensory cue integration under conflicts. Our results not only provide reliable estimates of just noticeable difference thresholds for stiffness with and without visual scaling but also capture all the prominent features of sensory cue integration, indicating weights that are proportional to the congruency level of manipulated visual signals. Overall, preference-based HiL optimization excels as a systematic and efficient method of studying multi-modal perception under conflicts
Enhancing directional thermal conductivity in hexagonal boron nitride reinforced epoxy composites through robust interfacial bonding
No full textEstablishing a robust interfacial bond between hexagonal boron nitride (h-BN) plates and the epoxy matrix is essential for enhancing heat transfer, which is difficult because of h-BN's low-surface energy, tendency to clump together, and the chemical inertness of the epoxy matrix. This research shows different techniques for treating the surface of h-BN fillers by applying acids and thermal processes to activate the surface. The silanization process was used to increase the silane content on the surface of activated h-BN in order to make it more compatible with the epoxy matrix. X-ray photoelectron spectroscopy analysis revealed silicon peaks (Si2s peak at 150.1 eV and Si2p peak at 100.3 eV) in the spectrum of silane-treated samples. Heat treatment resulted in the production of more oxygen molecules on the shell of h-BN compared to the acid treatment. Here, the primary focus was on examining how surface treatment affects thermal conductivity (TC) performance in both in-plane and through-thickness paths. There was an increase in the epoxy's TC perpendicular to the plane, going from 0.21 to 0.47 (W/mK), showing a remarkable 123.8% enhancement by adding 10 wt% of silane-modified-thermal treated h-BN particles. The improvement resulted from effectively silanizing the exterior boundary of h-BN particles, enhancing connection and distribution in the epoxy matrix. Surface modification of h-BN-epoxy composites improves TC, leading to better heat conduction in thermal management systems, benefiting industries like aerospace, automotive, and energy systems. Highlights: Silanization of h-BN for better filler-matrix bonding leading to improved heat transfer Boosting thermal conductivity in the through-thickness direction with surface-modified h-BN Significant improvement in through-thickness thermal conductivity with treated h-BN. Thermal treatment of h-BN produced better oxygenation than acid treatment. Application in aerospace and automotive through improved heat transfer
Compatibilizing effect of grafted recycled graphene on the viscoelasticity and mechanical performance of polypropylene composites
No full textThe interface problem encountered with the graphene addition in polyolefin composites is an important issue and it is necessary to use various compatibilizers to overcome this problem. Maleic anhydride grafted Polypropylene (MAPP), which is an extensively used compatibilizer in PP composites tends to eliminate these interfacial problems by interacting physically through PP chains and allows easier stress transmission from matrix to reinforcer. However, there is a lack of understanding about how this compatibilizer works with graphene-based PP composite systems since MAPP is mostly used to harmonize hydrophilic surfaces of fillers with non-polar PP. With this study, instead of direct usage of MAPP and GNP separately, waste tyre driven graphene nanoplatelets (GNP) was grafted by MAPP and the resulted material (MAPP-g-GNP) was compounded with PP by applying high shear rates at a melt phase. Effect of MAPP-g-GNP on the processability, viscoelastic response, and mechanical performance of PP composites were investigated, and high degree of interfacial enhancement was achieved by chemically combined MAPP with amphiphilic type of GNP in PP matrix. The use of MAPP-g-GNP at a loading ratio of 0.1 wt% resulted in 38% increase in flexural modulus and 26% in flexural strength and tensile modulus compared to neat PP. The two additives used in the PP composite (2 wt% MAPP and 0.1 wt % GNP) were reduced to a single additive (PP/MAPP-g-GNP) with a 95% weight reduction in the additive content of PP. Rheological studies support that MAPP is not as successful as MAPP-g-GNP in strengthening the interface when used alone with GNP due to the lower complex viscosity and the higher crossover frequency. This study contributes to the production of high-performance PP materials with a very low amount of combined GNP and compatibilizer by integrating the circularity approach in compound development by using GNP obtained from recycled and upcycled waste tyres
Enhancing cultural heritage archive analysis via automated entity extraction and graph-based representation learning
No full textRecent efforts to digitize textual, visual, and physical forms of cultural heritage require advanced tools for preservation and analysis. The availability of extensive online data creates a need for intelligent systems to help users and archivists understand latent relationships in these collections. A major challenge in cultural heritage studies is the labor-intensive process of analyzing these materials. Inconsistent linguistic terms and ambiguous concepts in digital documents make it difficult to uncover relationships without expert supervision. Moreover, while advanced models based on large-scale pretraining demonstrate strong performance in extracting semantic relationships, they depend on extensive pretraining on large external datasets, limiting their applicability for smaller or specialized collections. We propose a system that combines natural language processing for entity extraction with graph representation learning to model relationships among documents, categories, and n-grams, resulting in a fully-connected network representation. Unlike methods requiring large-scale pretraining, our approach operates effectively using only the information available in the dataset itself, making it particularly suited for smaller cultural heritage document collections. The system extracts significant terms from document metadata, produces embeddings for each document, and uses these embeddings to build a recommendation system for entity discovery. We tested the system on a collection of early 20th-century documents from Crete, evaluating its performance against alternative methods in collaboration with experts from the archival research organization SALT. This approach not only facilitates deeper insights into smaller, specialized collections but also reduces dependency on vast external training resources, enhancing its practical utility in cultural heritage studies
Exploring the impact of UV-C radiation and UV-protective additives on SEBS thermoplastic materials
Get PDFStyrene-ethylene-butylene-styrene (SEBS) thermoplastic elastomers are highly sought after for various industrial and consumer applications due to their exceptional flexibility, impact resistance, and thermal stability. However, their susceptibility to ultraviolet (UV) degradation, particularly from UV-C radiation, significantly challenges their long-term performance. This study investigates the impact of UV-C radiation on SEBS and evaluates the effectiveness of a UV protective additive in mitigating these effects. Neat SEBS and UV-protected SEBS samples were subjected to accelerated UV-C weathering for 2 weeks, and their mechanical, thermal, morphological, and chemical properties were thoroughly characterised before and after exposure. The results demonstrate that UV-C radiation significantly reduces the tensile strength of neat SEBS and induces nano-crack formation on its surface, as revealed by mechanical testing and scanning electron microscopy analysis, respectively. Furthermore, UV-C exposure negatively affects the thermal stability of SEBS, as evidenced by a decrease in the T50 temperature determined from thermogravimetric analysis. However, incorporating the UV-protective additive significantly mitigates these detrimental effects. The UV-protected SEBS retains a much higher percentage of its original tensile strength, exhibits minimal changes in surface morphology, and maintains comparable thermal stability to the unexposed samples. These findings highlight the crucial role of UV protective additives in enhancing the resistance of SEBS to UV-C radiation, paving the way for developing more durable and weather-resistant SEBS materials for demanding applications
Absorption enhancement in LWIR detector via waveguide and plasmonic modes engineering
No full textA high-temperature long-wavelength infrared (LWIR) photodetector based on a thin InAs/InAsSb type-II superlattice (T2SL) absorber is presented, utilizing enhanced optical absorption. This enhancement arises from the excitation of planar waveguide and surface plasmon polariton (SPP) modes, both strongly confined within the absorber and enabled by an adjacent highly doped semiconductor contact (HDSC) layer. Although SPP modes contribute to absorption near the SPP resonance (10 μm), the dominant mechanism, which exhibits stronger absorption, is due to cavity-like guided modes excited between 7 μm and 9 μm. The absorber-HDSC interface exhibits unique optical properties due to a large and tailorable Fresnel reflection phase, governed by a lossless Drude-like index contrast, even though both materials are dissipative. This configuration offers advantages over conventional dielectric or metallic claddings in terms of mode confinement, phase control, and fabrication feasibility. The results obtained in this study confirm the novel approach to achieve strong absorption in thin photodetector structures and open new avenues for subwavelength photonic device design
Prediction of soft manipulator's dynamic position using long-short-term memory neural networks
No full textSoft robotics has recently emerged as a rapidly growing and promising paradigm within the broader robotics domain. A few features that distinguish soft robotics from classical rigid robotics are the former's utilization of low-stiffness materials, bio-mimetic designs, compliant nature, suitability for unstructured environments, and higher degrees of freedom. Despite these unique characteristics, modeling soft robotics using physics-informed methods can pose several challenges due to the innate attributes of the robots' deformable materials. This can result in complex behavior due to nonlinearity in the robots' dynamics. One particularly appealing approach to overcome such obstacles is employing data-driven models based on machine learning techniques. In this paper, we use time-series models such as LSTM to predict the 2D position of the tip of a soft manipulator. We compare the results against a baseline polynomial regression and ARIMA models
Active learning for predicting drug permeability across the blood-brain barrier
No full textPredicting blood-brain barrier (BBB) permeability of drugs is a critical task in drug discovery for central nervous system (CNS) disorders. Active learning (AL) offers a promising approach to reducing labeling costs by selecting the most informative samples for training. This study investigates the performance of several AL sampling strategies - random, uncertainty, and dissimilarity - alongside two novel methods: explore-intensify and round-robin cycle switching. We evaluate these strategies using XGBoost models trained on ECFP fingerprints. Experiments are conducted under two data-splitting strategies: label-stratified and scaffold-based splits. Our results show that AL methods match the performance of passive learning while using only 10-65% of the labeled data. In particular, our second novel strategy achieves superior performance. The results demonstrate the effectiveness of dynamic AL approaches in accelerating molecular property prediction with fewer labeled samples
Responsible AI in marketing: AI booing and AI washing cycle of AI mistrust
Get PDFThe growing integration of Artificial Intelligence (AI) in marketing has introduced both opportunities and challenges, particularly concerning consumer trust. This paper critically examines two emerging phenomena: AI Washing, where companies exaggerate AI capabilities for marketing advantage, and AI Booing, a public backlash fueled by unmet expectations, ethical concerns, and transparency issues. By analyzing the interplay between these opposing forces, we explore the cyclical nature of AI mistrust and its implications for responsible AI adoption in marketing. Through a review of existing literature and industry examples, this study identifies key ethical, operational, and regulatory challenges in AI-driven marketing strategies. Our findings call attention to the need for transparency, human agency, stakeholder collaboration, and ethical data management to foster responsible AI practices that align with consumer trust and regulatory expectations. We conclude with recommendations for marketing professionals and policymakers to mitigate the cycle of AI mistrust and establish more credible AI integrations in marketing