Diverse Devices Research Articles

Cloud-Based Solutions for Video Streaming and Big Data Testing

In the era of digital transformation, cloud-based solutions have revolutionized the video streaming industry and big data testing methodologies. As consumer demand for high-quality, on-demand video content surges, traditional on-premises infrastructure often falls short in meeting scalability, performance, and cost-efficiency requirements. Cloud-based platforms, with their scalable resources and flexibility, have become essential in addressing these challenges. This paper explores the benefits and implementation strategies of cloud-based solutions for video streaming and big data testing, focusing on how these technologies enhance performance, scalability, and cost-effectiveness Video streaming services require a robust infrastructure capable of handling large volumes of data and delivering seamless user experiences across diverse devices and network conditions. Cloud-based video streaming solutions offer several advantages, including the ability to dynamically scale resources based on user demand, optimize content delivery through content delivery networks (CDNs), and provide a global reach without the need for extensive physical infrastructure. By leveraging cloud services, streaming platforms can reduce latency, enhance video quality, and improve overall user satisfaction. Traditional testing methods often struggle to cope with the complexity and volume of big data. Cloud-based testing platforms provide scalable environments where testers can simulate various data scenarios, execute performance tests, and analyze results with greater efficiency. The cloud's elasticity allows for on-demand provisioning of testing resources, making it easier to handle the dynamic nature of big data and conduct comprehensive testing without investing in expensive hardware.

Structural characterization of epitaxial ScAlN films grown on GaN by low-temperature sputtering

ScAlN has garnered substantial attention for its robust piezoelectric and ferroelectric properties, holding promise for diverse electronic device applications. However, the interplay between its structural attributes and physical properties remains poorly understood. This study systematically elucidates the structural characteristics of epitaxial ScAlN films grown on GaN by low-temperature sputtering. Correlations between Sc composition, lattice constants, and film strains were revealed utilizing high-resolution X-ray diffraction, reciprocal space mapping, and machine learning analyses. Our machine-learning model predicted c-axis lattice constants of ScAlN grown on GaN under various conditions and suggested that sputtering permits coherent growth over a wide compositional range. These findings advance the understanding of ScAlN and provide valuable insights for the research and development of novel ScAlN-based devices.

The effect of anthropomorphism of virtual voice assistants on perceived safety as an antecedent to voice shopping

This paper delves into the realm of virtual assistants (VAs) and their pivotal role in shaping user acceptance of voice shopping. VAs, initially tailored for voice interactions on smartphones, have permeated diverse consumer devices, revolutionizing user experience through conversational interfaces. However, the reluctance to embrace voice shopping persists due to safety apprehensions stemming from users' lack of confidence in VAs. The paper centers on anthropomorphism, emphasizing the human-like attributes of VAs, and its direct correlation with users' sense of safety. The study explores user perceptions of VAs in voice shopping, particularly focusing on anthropomorphic aspects like social presence, human-like voice, and friendliness. The investigation highlights their impact on perceived safety, word-of-mouth, and voice shopping acceptance. Analyzing prominent VAs—Alexa, Google Home, and Siri—the study identifies two fundamental factors influencing voice shopping acceptance: the humanity embedded in the VA's voice and the perceived safety it offers. Feeling secure during VA interactions significantly increases safety perception, fostering greater acceptance of VAs for shopping purposes. Additionally, the human-like qualities in the VA's voice positively influence users' perceived friendliness and safety, augmenting trust and overall acceptance of this technology.

Thermally Activated Delayed Fluorescence Macrocycles for Organic Light‐Emitting Diodes

AbstractMacrocyclic compounds have garnered considerable attention in the realms of supramolecular chemistry and optoelectronic materials owing to their distinctive geometries, exceptional stability, and potential chiral properties. As third‐generation organic light‐emitting diodes (OLEDs) emitters, thermally activated delayed fluorescence (TADF) materials present themselves as promising contenders due to their ability to achieve complete exciton utilization through the harnessing of triplet excitons. The incorporation of macrocyclic structures with TADF characteristics holds the potential to yield TADF emitters endowed with unique attributes, thereby synergistically enhancing the performance and longevity of TADF materials for diverse device applications, ultimately propelling their commercialization. Within this comprehensive review, a thorough exploration of the molecular structures, synthesis methodologies, structure‐property relationships, and applications of the reported macrocyclic TADF systems in OLEDs are delved. Additionally, the prevailing challenges and prospective research directions are highlighted that demand attention in the realm of macrocyclic TADF materials. It is hoped that this review will provide researchers engaged in optoelectronic materials research with a lucid comprehension and inspiration concerning the intricacies of these macrocyclic TADF systems.

Composite Switched Lyapunov Function-Based Control of DC–DC Converters for Renewable Energy Applications

Renewable energy sources play a pivotal role in the pursuit of sustainable and eco-friendly power solutions. While offering environmental benefits, they present inherent challenges. Photovoltaic systems rely on surrounding conditions, wind systems contend with variable wind speeds, and fuel cells are both costly and inefficient. Furthermore, the energy injected by renewable energy sources (RES) exhibits unpredictable behavior. To tackle these problems, researchers employ diverse power electronic devices and converters like inverters, power quality filters, and DC–DC choppers. Among these, DC–DC converters stand out for effectively regulating DC voltage and enhancing the efficiency of RESs. The meticulous selection of a suitable DC–DC converter, coupled with the integration of an efficient control technique, significantly influences overall power system performance. This paper introduces a novel approach to the design of switching controllers for DC–DC converters, specifically tailored for application in renewable energy systems. The proposed controller leverages the power of composite switched Lyapunov functions (CSLF) to enhance the efficiency and performance of DC–DC converters, addressing the unique challenges posed by renewable energy sources. Through comprehensive analysis and simulation, this study demonstrates the efficacy of the controller in optimizing power transfer, improving stability, and ensuring reliable operation in diverse renewable energy environments. Moreover, the small-scale DC–DC converter experiment’s findings are presented to confirm and validate the proposed scheme’s practical applicability.

Securing Iot Data Transmission: A Comprehensive Approach Integrating Two-Fish Encryption With Wireless Smart Energy Systems And Iot Cloud Services

This work addresses the challenges posed by the interconnected nature of the Internet of Things (IoT). This paradigm, facilitating the seamless connectivity of diverse devices, gives rise to issues such as addressability, confidentiality, and data management. The proposed solution advocates for the integration of the robust Twofish encryption algorithm into wireless smart energy systems, creating a holistic approach that capitalizes on the capabilities of IoT cloud services. Twofish, recognized for its strong encryption capabilities, becomes an integral part of the IoT framework to ensure the security of communication and data confidentiality. Through the amalgamation of Twofish encryption with wireless smart energy systems and IoT cloud services, the approach provides a comprehensive solution for the secure transmission and management of data. This integration enhances system scalability, flexibility, and accessibility, thereby safeguarding the confidentiality of information exchanged over the internet. The paper establishes the superiority of Twofish over lightweight cryptography for low-power devices within IoT systems through a thorough analysis of critical factors. These factors encompass the robust security strength of Twofish, its effective encryption capabilities guaranteeing data confidentiality, the algorithm's maturity validated by extensive scrutiny from the cryptographic community, compatibility and interoperability with diverse platforms, resource efficiency in maintaining a balance between security and device capabilities, and the crucial long-term security assurance required for the extended operational lifetimes of IoT devices. The research findings affirm that the incorporation of Twofish into wireless smart energy systems and IoT cloud services offers a secure and efficient solution for safeguarding sensitive information within IoT environments. This consolidated approach contributes significantly to advancing secure communication and data management in the continually evolving landscape of the Internet of Things.

Evaluation of 6LoWPAN Generic Header Compression in the Context of a RPL Network.

The Internet of Things (IoT) facilitates the integration of diverse devices, leading to the formation of networks such as Low-power Wireless Personal Area Networks (LoWPANs). These networks have inherent constraints that make header and payload compression an attractive solution to optimise communication. In this work, we evaluate the performance of Generic Header Compression (6LoWPAN-GHC), defined in RFC 7400, for IEEE 802.15.4-based networks running the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL). Through simulation and real-device experiments, we study the impact of 6LoWPAN-GHC on energy consumption and delays and investigate for which scenarios 6LoWPAN-GHC is beneficial. We show that all RPL control packets are compressible by 6LoWPAN-GHC, which reduces their transmission delay and as such their vulnerability to interference. However, for the devices under study transmitting at 250 kbit/s, the energy gain obtained from sending a compressed packet is outweighed by the energy needed to compress it. The use of 6LoWPAN-GHC causes an energy increase of between 2% and 26%, depending on the RPL packet type. When the range is more important than the bandwidth and a sub-GHz band is used at 10 kbit/s, an energy gain of 11% to 29% can be obtained, depending on the type of RPL control packet.

Temperature-dependent short channel effects in nanoscale double gate FinFETs: A comparative study

This work investigates the impact of temperature variation on Short Channel Effects (SCEs). Gallium Arsenide (GaAs), Gallium Antimonide (GaSb), Gallium Nitride (GaN) and Silicon (Si) are the channel materials that are investigated. The study examines phenomenal metrics such as Drain Induced Barrier Lowering (DIBL), Subthreshold Swing (SS), Threshold Voltage Roll-off, On-current and Transconductance using PADRE Simulator. The results revealed that GaAs-FinFET excels in terms of DIBL, threshold voltage, transconductance and on-current at higher temperatures. On the other hand, GaN-FinFET excels in terms of SS at lower temperatures. These findings contribute to the understanding of temperature effects on nanoscale double gate FinFETs, aiding their optimization for diverse electronic devices.

Laser-Induced Electron Synchronization Excitation for Photochemical Synthesis and Patterning Graphene-Based Electrode.

Micro-supercapacitors (MSCs) represent a pressing requirement for powering the forthcoming generation of micro-electronic devices. The simultaneous realization of high-efficiency synthesis of electrode materials and precision patterning for MSCs in a single step presents an ardent need, yet it poses a formidable challenge. Herein, a unique shaped laser-induced patterned electron synchronization excitation strategy has been put forward to photochemical synthesis RuO2 /reduced graphene oxide (rGO) electrode and simultaneously manufacture the micron-scale high-performance MSCs with ultra-high resolution. Significantly, the technique represents a noteworthy advancement over traditional laser direct writing (LDW) patterning and photoinduced synthetic electrode methods. It not only improves the processing efficiency for MSCs and the controllability of laser-induced electrode material but also enhances electric fields and potentials at the interface for better electrochemical performance. The resultant MSCs exhibit excellent area and volumetric capacitance (516mFcm-2 and 1720Fcm-3 ), and ultrahigh energy density (0.41Whcm-3 ) and well-cycle stability (retaining 95% capacitance after 12000 cycles). This investigation establishes a novel avenue for electrode design and underscores substantial potential in the fabrication of diverse microelectronic devices.

Recent Trends of Integration of Blockchain Technology With the IoT by Analysing the Networking Systems: Future Research Prospects

In recent times, attention has surged towards entities with the potential to revolutionize various sectors. The integration of Internet of Things (IoT) and blockchain technologies, known as IoT-blockchain, offers numerous advantages, including heightened security, privacy, traceability, transparency, and reduced costs. This abstract delves into the taxonomy and prominent platforms of blockchain applications for IoT in networking systems, exploring recent advancements, obstacles, and future research avenues. IoT blockchain's crucial aspect lies in establishing decentralized networks, enabling secure collaboration and data interchange among diverse devices without a central governing entity. Platforms like Ethereum, Hyperledger, and IOTA facilitate the creation and management of these networks. Recent developments focus on enhancing security, scalability, and efficiency through novel consensus mechanisms and cryptographic techniques. Challenges persist, including the need for improved interoperability, integration with existing systems, efficient governance, regulatory structures, and the identification of use cases and business models for widespread adoption. The examination of successful governance, regulatory frameworks, and potential adoption catalysts completes the discourse on IoT blockchain technology.

Bioinspired Photoreceptors with Neural Network for Recognition and Classification of Sign Language Gesture.

This work addresses the design and implementation of a novel PhotoBiological Filter Classifier (PhBFC) to improve the accuracy of a static sign language translation system. The captured images are preprocessed by a contrast enhancement algorithm inspired by the capacity of retinal photoreceptor cells from mammals, which are responsible for capturing light and transforming it into electric signals that the brain can interpret as images. This sign translation system not only supports the effective communication between an agent and an operator but also between a community with hearing disabilities and other people. Additionally, this technology could be integrated into diverse devices and applications, further broadening its scope, and extending its benefits for the community in general. The bioinspired photoreceptor model is evaluated under different conditions. To validate the advantages of applying photoreceptors cells, 100 tests were conducted per letter to be recognized, on three different models (V1, V2, and V3), obtaining an average of 91.1% of accuracy on V3, compared to 63.4% obtained on V1, and an average of 55.5 Frames Per Second (FPS) in each letter classification iteration for V1, V2, and V3, demonstrating that the use of photoreceptor cells does not affect the processing time while also improving the accuracy. The great application potential of this system is underscored, as it can be employed, for example, in Deep Learning (DL) for pattern recognition or agent decision-making trained by reinforcement learning, etc.

QUANTITATIVE APPROACHES IN ARCHITECTURAL DESIGN FOR USER-ORIENTED INDOOR ENVIRONMENTAL QUALITY

ABSTRACT Understanding indoor environmental quality (IEQ) and integrating indoor data into architectural design is a critical component of green building design for building occupants’ comfort and health. With a growing emphasis on creating healthy and comfortable indoor environments in green buildings, indoor data in architectural design has increasingly incorporated quantitative methods to analyze and optimize indoor environmental quality parameters. Data-driven design in architecture has been used as a decision-making approach to the green building design process; it has implications for the building occupants to voluntarily enhance their indoor environments and comfort. A higher degree of personal control for indoor environmental quality (IEQ) leads to higher satisfaction, energy savings, and productivity in work environments. IEQ data access related to thermal, visual, and acoustic comforts and indoor air enables occupants to acquire ownership of indoor controls, contributing to their comfort and task productivity. Undergraduate architecture students carried out a series of data-driven designs for IEQ monitoring and design integrations by conducting a field study, data collection, data analysis, and visualization, and linking findings to architectural proposals. Participating groups were requested to i) theoretically understand IEQ data parameters for four primary indoor environments, ii) experimentally conduct a field survey of target spaces, iii) numerically predict IEQ conditions by using diverse simulation tools and measurement devices, iv) graphically visualize IEQ findings, and v) finally integrate findings into the architectural design as a solutional proposal. The findings highlight that the potential of quantitative approaches in architectural design enhances students’ understanding of environmental contributors and design solutions for occupants’ comfort and health.

Efficacy and predicting factors of multimodal treatment for ruptured intracranial vertebral artery dissecting aneurysms.

We aimed to investigate the efficacy of our multimodal strategies and propose a treatment algorithm for ruptured vertebral artery dissecting aneurysms (VADAs). This study included 41 patients treated at a single institution between 2015 and 2022. The treatment modalities were justified based on the collateral circulation and aneurysm location related to the posterior inferior cerebellar artery (PICA). Treatment outcomes and complications of each treatment group were analyzed. The association between the collateral blood flow and the postoperative vertebrobasilar ischemia (VBI) was also investigated. There were 17 post-PICA, 10 PICA-involved, 7 pre-PICA, and 7 non-PICA types. Reconstruction techniques included flow diversion devices (n = 11) and stent-assisted coiling (n = 3). Deconstruction techniques included coil trapping (n=17) and microsurgical parent artery occlusion with (n = 8) or without PICA revascularization (n = 2). Five (18.5%) of the deconstruction group had postoperative VBI. Overall favorable outcomes in both groups were observed in 70.7% of patients with a mean follow-up time of 21.5 months. Poor World Federation of Neurosurgical Societies grade (IV-V) was identified as a predictor of unfavorable outcomes (p = 0.003). In addition, the VA4/BA4 ratio > 0.22, the presence of collateral blood flow from the posterior communicating artery (PcomA), and a contralateral VA diameter > 2.5 mm were associated with a lower risk of postoperative VBI. In summary, the proposed strategic treatment in this study is pragmatic, yielding satisfactory results where a deconstructive technique should be used with caution, particularly when there is a flow mismatch or the absence of collateral PcomA in the vertebrobasilar circulation.

A flexible supercapacitor with high energy density and wide range of temperature tolerance using a high-concentration aqueous gel electrolyte

As the demand for a wide range of wearable devices increases, extensive effort is devoted to developing high-performance flexible energy storage devices such as batteries and supercapacitors. Of particular interest, flexible supercapacitors using aqueous electrolytes have high application potential owing to their many advantages including low cost, harmlessness to the human, and environmental safety compared to others using electrolytes such as ionic liquids and organic electrolytes. However, poor temperature tolerance and narrow operation voltage window have hindered the practical application of aqueous supercapacitors. In this study, we report on a wide temperature-tolerant flexible aqueous supercapacitor featuring high electrochemical performance designed by a deliberate selection of electrode materials and electrolytes. As a flexible electrode, three-dimensional microporous graphene foam coated with conducting polymer Polypyrrole (PPy) is used to enhance capacitance up -to 90.0 F/g by increasing the surface area and inducing the pseudo-capacitance of PPy. As a gel-type electrolyte, a high-concentration NaClO4 is mixed with Polyvinyl alcohol to enlarge the voltage window to 1.8 V and a stable temperature range from -25 to 80°C, even under repetitive bending deformation. The fabricated supercapacitor exhibits a superior energy density of 37.4 Wh kg−1 and maintains 92.0% of initial capacitance at room temperature after three repeated cycles of cooling/heating across a wide temperature range over 100°C. This work suggests a high potential application of the fabricated supercapacitor as an eco-friendly and stable energy storage system for powering diverse wearable devices under various conditions.

2440. Central Line-Associated Bloodstream Infection Incidence Reduction Accompanies Initial-Specimen Diversion Device Adoption

Abstract Background Established lines are poor sites for sampling blood (frequently host to enterococci and Candida species) but peripheral venipuncture is an imperfect alternative as antiseptics struggle to eliminate deeply embedded microorganisms. State-of-the-art initial-specimen diversion device (ISDD) use can facilitate near-zero blood culture contamination rates, but impact on central line-associated bloodstream infection (CLABSI) incidence is unclear. Methods Intervention took place within a Joint Commission-accredited, 200-bed, acute-care community hospital serving nine Southeastern United States counties toward the objective of reduced CLABSI reporting. From September 2021 through March 2023, ISDD use augmented all peripheral venipuncture sample collection for adult patient blood culture. CLABSI reporting and the associated National Healthcare Safety Network (NHSN) standardized infection ratio (SIR) data for the 18-month intervention period was compared to the preceding 18-month period. Results For the 18 months prior to ISDD use, 6.5 CLABSIs were predicted and 17 CLABSIs were reported. For the 18 months during which ISDD use augmented standard procedure, 5.5 CLABSIs were predicted and 3 CLABSIs were reported. ISDD use accompanied an 80% reduction in SIR, from 2.6 to 0.5. Conclusion The study period coincided with the COVID-19 pandemic, a nationwide struggle with considerable quality control shortcomings observed in this space that, by juxtaposition, reinforce the strengths of the intervention. SIRs are the primary means of tracking healthcare-associated infection trends, and financial penalties follow performances failing to meet predictions. As CLABSI reporting throughout the intervention period was dramatically below NHSN prediction, the author concludes that ISDDs have standard-of-care potential and encourages further study in the interest of antimicrobial stewardship and quality control. Disclosures All Authors: No reported disclosures

Analysis of thermsolutal performance and entropy generation for ternary hybrid nanofluid in a partially heated wavy porous cabinet

PurposeThis study comprehensively examines entropy generation and thermosolutal performance of a ternary hybrid nanofluid in a partially active porous cabinet. The purpose of this study is to comprehend the intricate phenomena of double diffusion by investigating the dispersion behavior of Al2O3, CuO, and Ag nanoparticles in water.Design/methodology/approachThe cabinet design consists of two horizontal walls and two curved walls with the lower border divided into a heated and concentrated region of length b and the remaining sections are adiabatic. The vertical borders are cold and low concentration, while the upper border is adiabatic. Two cavity configurations such as convex and concave are considered. A uniform porous medium is taken within the ternary hybrid nanofluid. This has been characterized by the Brinkman-extended Darcy model. Thermosolutal phenomena are governed by the Navier-Stokes equations and are solved by adopting a higher-order compact scheme.FindingsThe present study focuses on exploring the influence of several well-defined parameters, including Rayleigh number, Darcy number, Lewis number, Buoyancy ratio number, nanoparticle volume concentration and heater size. The results indicate that the ternary hybrid nanofluid outperforms both the mono and hybrid nanofluids in all considered aspects.Originality/valueThis study brings forth a significant contribution by uncovering novel flow features that have previously remained unexplored. By addressing a well-defined problem, the work provides valuable insights into the enhancement of thermal transport, with direct implications for diverse engineering devices such as solar collectors, heat exchangers and microelectronics.

Interplay of AI and IOT: Application and Challenges

This papеr undеrscorеs thе nеcеssity for collaborativе еfforts from acadеmia, industry, and policymakеrs to addrеss thеsе challеngеs and harnеss thе full potеntial of thе AI and IoT intеrplay. As thе tеchnology landscapе еvolvеs, it is impеrativе to strikе a balancе bеtwееn innovation and rеsponsibility, еnsuring that AI and IoT dеvеlopmеnts bring tangiblе bеnеfits whilе upholding privacy and sеcurity standards. In summary, thе intеrplay of AI and IoT prеsеnts a promising futurе of intеlligеnt, data-drivеn solutions, but it also dеmands carеful considеration of thе accompanying challеngеs to unlock its full potеntial.
 AI, given its capacity to process and comprehend extensive datasets, assumes a central role in amplifying the capabilities of IoT. This collaboration empowers IoT devices to gather, analyze, and act upon real-time data intelligently, fostering the development of more sophisticated and self-reliant systems. A wide array of applications arise from this synergy, spanning smart cities, healthcare, industrial automation, agriculture, and transportation.
 Nonetheless, the fusion of AI and IoT introduces a distinctive array of obstacles. These encompass apprehensions about safeguarding data privacy and security, the necessity for compatibility among diverse IoT devices and AI systems, ethical quandaries regarding AI-driven decision processes, and the imperative for robust infrastructure capable of accommodating the surging data volumes. Moreover, challenges pertaining to standardization and regulatory frameworks add further impediments to the seamless amalgamation of AI and IoT technologies.

THE ESSENCE AND ROLE OF DISTANCE LEARNING IN THE FORMATION OF PROFESSIONAL COMPETENCES OF THE FUTURE FOREIGN LANGUAGETEACHER

This article delves into the pivotal theme of formation of professional competences among future foreign language teachers within the framework of distance learning. A comprehensive research was conducted to explore the multifaceted concept of “distance learning,” which entails indirect interaction between educators and learners the adept utilization of information and communication technologies, digital resources, diverse electronic devices, electronic communication tools, and telecommunication services. The study meticulously traverses the evolutionary trajectory of distance learning, including correspondence education, case study, broadcasting learning, and networking learning. Additionally, it casts a discerning spotlight upon distinct models of distance learning, such as distance learning based on a single university with the traditions of classical education, distance learning based on the cooperation of several educational institutions, and distance learning at universities specially created for this purpose. The article further navigates through the intricate organizational and methodological constituents intrinsic to distance learning. This intricate tapestry comprises the domains of marketing, educational and material, financial and economic, and identification and control subsystems. Notably, the article undertakes an exhaustive exploration of the strengths and weaknesses inherently associated with distance learning. Keywords: formation of professional competences, future foreign language teachers, distance learning, distance education, higher education institution, information and communication technologies (ICT)

AI-powered medical devices for practical clinicians including the diagnosis of colorectal polyps

Background: The integration of medical devices with artificial intelligence (AI) software is rapidly advancing as technology progresses. AI machine learning can be used in commercial medical services to generate practical data; there is evidence that it can be integrated into newly developed devices. However, such devices must undergo approval, regulation, and supervision. The Food and Drug Administration approves regulations for numerous machine-learning medical devices and shares open lists with the public. In this article, we examine recent medical AI devices in different fields, including the diagnosis of colorectal polyps.Current Concepts: Currently, in the field of gastroenterology, there has been a significant amount of research aimed at enhancing adenoma detection rates using tools powered by AI, such as the EndoScreener and GI Genius. Various such devices have also been developed for other fields; examples include the 23andMe Personal Genome Service for DNA detection, Spectral MD’s DeepView platform for wound imaging in surgery, Gili Pro BioSensor for monitoring vital signs, DreaMed Advisor Pro for diabetes, Minuteful for urinary analysis, BrainScope TBI for cerebral diagnosis, Compumedics Sleep Monitoring System for sleep disorders, Idx-DR v2.3 for ophthalmology, and EarliPoint system for pediatrics.Discussion and Conclusion: By the time this article is published, it is likely that even more AI medical devices will have been approved and commercialized. The development of such devices should be strongly encouraged. Additionally, we anticipate greater involvement from practitioners in the development and validation of diverse medical AI devices in Korea.

High toughness plant oil-based epoxy resins simultaneous with low dielectric properties

Developing novel multifunctional polymeric materials from biomass to replace traditional electric encapsulation materials such as epoxy resins and polydimethylsiloxane is of great significance for energy consumption and sustainable development. Here, a series of bio-based epoxy thermosets were prepared using a solvent-free ring-opening polymerization-initiated process from different epoxidized plant oils. Key factors such as reactive sites and optimized curing conditions for this system were studied systematically. Moreover, the relative properties of the resulting products, including thermal, mechanical, and dielectric performance were investigated and discussed deeply in correlation with their structure and reactivity. The findings in this work demonstrated that the adjustable chain stiffness and cross-link density of cured networks, achieved through the use of different types of vegetable oils, allows for tailoring the comprehensive properties of these networks. Among the prepared samples, soybean oil-based thermoset exhibited superior tensile strength (7.5 ± 0.28 MPa), elongation at break (87.56 ± 4.80%), glass transition temperature (Tg) of approximately 23 °C, storage modulus of 5.84 MPa, and a dielectric constant of approximately 2.95 at 10 MHz. These properties endow soybean oil-based thermoset as a suitable candidate substrate for encapsulation materials, in comparison with commercial polydimethylsiloxane. The findings suggest that the functional materials designed in this study hold promise as alternatives to petroleum-based polymers and offer paramount potential for applications in diverse electronic device applications.