Diverse Devices Research Articles

Upgrading Polyolefin Plastic Waste into Multifunctional Porous Graphene using Silicone-Assisted Direct Laser Writing.

The widespread use of plastics, especially polyolefin including polyethylene and polypropylene, has led to severe environmental crises. Chemical recycling, a promising solution for extracting value from plastic waste, however, is underutilized due to its complexity. Here, a simple approach, silicone-assisted direct laser writing (SA-DLW) is developed, to upgrade polyolefin plastic waste into multifunctional porous graphene, called laser-induced graphene (LIG). This method involves infiltrating polyolefins with silicone, which retards ablation during the DLW process and supplies additional carbon atoms, as confirmed by experimental and molecular dynamic results. A remarkable conversion yield of 38.3% is achieved. The upgraded LIG exhibited a porous structure and high conductivity, which is utilized for the fabrication of diverse energy and electronic devices with commendable performance. Furthermore, the SA-DLW technique is versatile for upgrading plastic waste in various types and forms. Upgrading plastic waste in the form of fabric has significantly simplified pre-treatment. Finally, a wearable flex sensor is fabricated on the non-woven fabric of a discarded medical mask, which is applied for gesture monitoring. This work offers a simple but effective solution to upgrade plastic waste into valuable products, contributing to the mitigation of environmental challenges posed by plastic pollution.

All Solid Photonic Crystal Fiber Enabled by 3D Printing Fiber Technology for Sensing of Multiple Parameters

AbstractUsing the flexibility and diversity of material and structure designs possible with 3D printing fiber technology, an all‐solid photonic crystal fiber (PCF) is fabricated using borate (B2O3) doping. The geometry, material, and optical properties of this 3D printed PCF are characterized and analyzed using optical microscopy, scanning electron microscopy (SEM), fiber index profilometry, and Fourier transform infrared (FTIR) microscopy. Analysis demonstrates that B2O3 doped in fabricated PCF has experienced evaporation leading to mass loss during drawing. In addition, there is no observable difference between the structure of substrate silica (SiO2) and the SiO2 nanoparticles. However, microdomain differences may explain enhanced reflectance. Furthermore, a Mach–Zehnder interferometer (MZI) sensor is constructed with this 3D printed solid PCF and applied to temperature, refractive index, tensile force, and bending sensing. The specially designed 3D printed PCF has maximum temperature sensitivity up to Δλ/ΔT ≈0.075 nm °C−1. When immersed in 76.34 wt.% glycerol‐water solution, the sensitivity can be further improved. These results demonstrate that 3D printing fiber technology enables the custom fabrication of highly sensitive optical fiber sensors, increasing opportunities for the development of diverse and flexible sensors and devices for future internet‐of‐things (IoT) applications.

A blockchain‐based secure framework for data management

AbstractData management is a crucial requirement due to the autonomous and constrained nature of Unmanned Aerial Vehicles (UAVs), Internet of Things (IoTs), and the aviation domain. The autonomous and restricted nature of these sectors increases the need for a shared, distributed database, strong access control management, consensus in autonomous decision‐making, and effective communication across diverse protocols and devices. This research presents a comprehensive approach and offers a new viewpoint to the field of blockchain while establishing a fundamental baseline for future improvements in data management systems and addressing the shortcomings of previously proposed existing frameworks in order to fulfill the complex needs of secure data management. This study contributes to the advancement of secure and efficient data management systems by implementing robust data monitoring for error detection, ensuring data integrity, and enabling encrypted or anonymous data sharing based on sensitivity levels. Additionally, the integration of diverse devices, enforcement of immutable regulations compliance, and development of permissioned blockchain systems for identity management further enhance the system's capabilities, offering comprehensive solutions for modern data management challenges. In the tests, the proposed framework showed increased successful transactions in all rate controllers. Besides, effect of the validator number on throughput and latency is tested and analyzed thoroughly.

Detection of Various Botnet Attacks Using Machine Learning Techniques

With the rapid growth in the quantity of Internet of Things (IoT) devices linked with the network, there exists a concurrent rise in network attacks, including overwhelming and service disruption incidents. The increasing prevalence of network attacks, such as overwhelming and service denial, poses a threat to IoT devices, leading to network disruptions and service disruption. Detecting these attacks is challenging due to the diverse array of heterogeneous devices in the IoT environment, making traditional rule-based security solutions less effective. Developing optimal security models for diverse device types is challenging. Machine learning (ML) offers an alternative approach, enabling the creation of effective security models by leveraging empirical data specific to each device. We utilize machine learning techniques for the detection of Internet of Things (IoT) attacks. Our focus is on botnet attacks directed at variety of IoT devices. We undertake the development of machine learning-based models tailored to each specific category of device for enhanced security. We utilize the N-BaIoT dataset, which incorporates injected botnet attacks (specifically Gafgyt and Mirai) across diverse IoT device types, including Doorbell, Baby Monitor, Security Camera, and Webcam. We develop models for detecting botnets for each IoT device by utilizing diverse machine learning algorithms. Following model development, we assess the utility of the models with a strong detection F1-score through classification analysis. The novelty of this work lies in crafting a Machine Learning-based framework designed to identify IoT botnet attacks, followed by successful detection of such attacks across diverse IoT devices utilizing this framework. Among the most widely used machine learning algorithms on the NBaIoT dataset, Decision Trees, Random Forests, and K-Nearest Neighbors (KNN) demonstrate superior performance.

Optical characterization of high-quality spin-Coated PVA nanostructured films for photo-sensing application

In our investigation, we fabricated high-quality Polyvinyl Alcohol (PVA) films via spin coating on a variety of substrates, with a focus on their potential applications in diverse devices. Through comprehensive characterization techniques, including spectrophotometer measurements, we evaluated the nanostructure and quality of these films. Our analysis revealed a distinct energy gap of 1.74 eV and a significant 5.37 eV direct allowed optical transition, shedding light on the correlation between absorption coefficient and photon energy. Additionally, we meticulously calculated dispersion parameters using the Wemple-DiDomenico relation, obtaining values of 19.1 eV for dispersion energy and 9.48 eV for oscillator energy under the single oscillator model. These results provide further validation of the unique structural characteristics of PVA films. Importantly, our findings underscore the potential of these materials for advanced optoelectronic devices, particularly in the realm of photosensor applications.

Thermal case study of magnetic radiative flow impacts on Newtonian nanofluid over a stretchable plate in absorbent: Box approach

Nanofluid technology represents a significant breakthrough in thermal engineering, with widespread applications spanning heat exchangers, cancer treatment, and heat storage systems. Despite its success in improving heat transfer rates in diverse devices, the challenge of enhancing thermal conductivity using nanoparticles looms large. This study aims to elucidate the 2D flow of nanofluid under suction/injection over a stretching wedge. It considers factors such as Brownian and thermophoretic diffusions, nonlinear heat generation, and thermal radiation. Additionally, it examines the effects of viscous dissipation and Joule heating. Through the conversion of governing nonlinear coupled partial differential equations into a system of coupled ordinary differential equations using similarity transformations, numerical solutions are obtained via the Keller-box method. The analysis delves into the significant impacts of relevant parameters on velocity, temperature, concentration, and microorganism fields, presented graphically. Notably, the findings underscore the role of solid particles in enhancing the thermal efficiency of base fluids. These investigation outcomes are vital for controlling heat transferal rates and fluid velocities throughout a variation of manufacturing processes and industrial sectors, guaranteeing the required quality of the ending product. The perceptions garnered from this research hold capacity for a broad range of industrial purposes and carry consequences through countless engineering disciplines, mostly within the range of nanotechnology.

Azimuthal rotation-controlled nanoinscribing for continuous patterning of period- and shape-tunable asymmetric nanogratings

We present an azimuthal-rotation-controlled dynamic nanoinscribing (ARC-DNI) process for continuous and scalable fabrication of asymmetric nanograting structures with tunable periods and shape profiles. A sliced edge of a nanograting mold, which typically has a rectangular grating profile, slides over a polymeric substrate to induce its burr-free plastic deformation into a linear nanopattern. During this continuous nanoinscribing process, the “azimuthal angle,” that is, the angle between the moving direction of the polymeric substrate and the mold’s grating line orientation, can be controlled to tailor the period, geometrical shape, and profile of the inscribed nanopatterns. By modulating the azimuthal angle, along with other important ARC-DNI parameters such as temperature, force, and inscribing speed, we demonstrate that the mold-opening profile and temperature- and time-dependent viscoelastic polymer reflow can be controlled to fabricate asymmetric, blazed, and slanted nanogratings that have diverse geometrical profiles such as trapezoidal, triangular, and parallelogrammatic. Finally, period- and profile-tunable ARC-DNI can be utilized for the practical fabrication of diverse optical devices, as is exemplified by asymmetric diffractive optical elements in this study.

Magnetic Assembly of Magnetite/Perovskite Hybrid Nanorods for Circularly Polarized Luminescence

AbstractMagnetic fields are uniquely valuable for creating colloidal nanostructured materials, not only providing a means for controlled synthesis but also guiding their self‐assembly into distinct superstructures. In this study, a magnetothermal process for synthesizing hybrid nanostructures comprising ferrimagnetic magnetite nanorods coated with fluorescent perovskite nanocrystals is reported and their magnetic assembly into superstructures capable of emitting linear and circularly polarized light are demonstrated. Under UV excitation, the superstructures assembled in a liner magnetic field produce linear polarized luminescence, and those assembled in a chiral magnetic field exhibit strong circularly polarized luminescence (CPL) with a glum value up to 0.44 (±0.004). The CPL is believed to originate from the dipolar interaction between neighboring perovskite nanocrystals attached to the chiral assemblies and the chiral‐selective absorption of the perovskite emission by the magnetite phase. The magnetic synthesis and assembly approaches and the resulting distinctive chiral superstructures are anticipated to open up new avenues for designing diverse functional chiroptical devices.

Inerter-controlled topological interface states in locally resonant lattices with beyond-nearest neighbor coupling

This paper explores the emergence of topological interface states in one-dimensional locally resonant lattices incorporating inerters in both nearest neighbor (NN) and beyond-nearest neighbor (BNN) coupling. The investigation focuses on the unique wave propagation characteristics of these lattices, particularly the presence and behavior of interface states. The non-trivial topological behavior due to broken inversion symmetry within the unit cell of the locally resonant lattice is comprehensively investigated in the presence of inerters in NN and BNN coupling. The emerging interface states in the supercell analysis exhibit specific spatial and frequency localization properties due to inerter-based BNN interactions. Additionally, the study demonstrates the ability of inerter elements with weak inertance to control the frequency of interface states while maintaining the fundamental topological properties of the lattice. The identified topological interface states in lattices with BNN coupling present an opportunity for designing diverse devices, such as waveguides, filters, sensors, and energy harvesting systems. Overall, this research enhances our comprehension of topological phenomena in inerter-based locally resonant lattices with BNN interactions and introduces possibilities for creating robust and versatile devices based on topologically protected edge/interface states.

Effect of Surface Functional Groups on the Electronic Behavior and Optical Spectra of Mn2N Based MXenes.

The extensive applications of MXenes, a novel type of layered materials known for their favorable characteristics, have sparked significant interest. This research focuses on investigating the influence of surface functionalization on the behavior of Mn2NTx (Tx=O2, F2) MXenes monolayers using the "Density functional theory (DFT) based full-potential linearized augmented-plane-wave (FP-LAPW)" method. We elucidate the differences in the physical properties of Mn2NTx through the influence of F and O surface functional groups. We found that O-termination results in half-metallic behavior, whereas the F-termination evolves metallic characteristics within these MXene systems. Similarly, surface termination has effectively influenced their optical absorption efficiency. For instance, Mn2NO2 and Mn2NF2 effectively absorb UV light ~50.15×104 cm-1 and 37.71×104 cm-1, respectively. Additionally, they demonstrated prominent refraction and reflection characteristics, which are comprehensively discussed in the present work. Our predictions offer valuable perspectives into the optical and electronic characteristics of Mn2NTx-based MXenes, presenting the promising potential for implementing them in diverse optoelectronic devices.

Adversarial attacks and adversarial training for burn image segmentation based on deep learning.

Deep learning has been widely applied in the fields of image classification and segmentation, while adversarial attacks can impact the model's results in image segmentation and classification. Especially in medical images, due to constraints from factors like shooting angles, environmental lighting, and diverse photography devices, medical images typically contain various forms of noise. In order to address the impact of these physically meaningful disturbances on existing deep learning models in the application of burn image segmentation, we simulate attack methods inspired by natural phenomena and propose an adversarial training approach specifically designed for burn image segmentation. The method is tested on our burn dataset. Through the defensive training using our approach, the segmentation accuracy of adversarial samples, initially at 54%, is elevated to 82.19%, exhibiting a 1.97% improvement compared to conventional adversarial training methods, while substantially reducing the training time. Ablation experiments validate the effectiveness of individual losses, and we assess and compare training results with different adversarial samples using various metrics.

A General Synthesis Method for Covalent Organic Framework and Inorganic 2D Materials Hybrids.

Two-dimensional (2D) inorganic/organic hybrids provide a versatile platform for diverse applications, including electronic, catalysis, and energy storage devices. The recent surge in 2D covalent organic frameworks (COFs) has introduced an organic counterpart for the development of advanced 2D organic/inorganic hybrids with improved electronic coupling, charge separation, and carrier mobility. However, existing synthesis methods have primarily focused on few-layered film structures, which limits scalability for practical applications. Herein, we present a general synthesis approach for a range of COF/inorganic 2D material hybrids, utilizing 2D inorganic materials as both catalysts and inorganic building blocks. By leveraging the intrinsic Lewis acid sites on the inorganic 2D materials such as hexagonal boron nitride (hBN) and transition metal dichalcogenides, COFs with diverse functional groups and topologies can grow on the surface of inorganic 2D materials. The controlled 2D morphology and excellent solution dispersibility of the resulting hybrids allow for easy processing into films through vacuum filtration. As proof of concept, hBN/COF films were employed as filters for Rhodamine 6G removal under flow-through conditions, achieving a removal rate exceeding 93%. The present work provides a simple and versatile synthesis method for the scalable fabrication of COF/inorganic 2D hybrids, offering exciting opportunities for practical applications such as water treatment and energy storage.

Polarization-insensitive and high-efficiency edge coupler for thin-film lithium niobate.

In this Letter, we propose and demonstrate a fiber-to-chip edge coupler (EC) on an x-cut thin film lithium niobate (TFLN) for polarization-insensitive (PI) coupling. The EC consists of three width-tapered full-etched waveguides with silica cladding and matches well with a single-mode fiber (SMF). The measured results show that the minimum coupling losses for TE0/TM0 modes remain to be 0.9 dB/1.1 dB per facet, and the polarization dependent loss (PDL) is <0.5 dB over the wavelength range from 1260 to 1340 nm. Moreover, the EC features large misalignment tolerance of ±2 µm in the Z direction and ±1.5 µm in the X direction for both polarizations for a 1 dB penalty. To the best of our knowledge, this is the first realized O-band edge coupler on TFLN with SMF. The proposed device shows promising potential for integration into TFLN polarization diversity devices.

Impedance characterization of dinaphtho[2,3-b:2’,3’-f]thieno[3,2-b]thiophene diodes: Addressing dielectric properties and trap effects

Dinaphtho[2,3-b:2’,3’-f]thieno[3,2-b]thiophene (DNTT) is a widely used small-molecular p-type organic semiconductor. Despite the broad availability of high-performance DNTT transistors, there is a lack of investigation into other devices based on this semiconductor. In this study, rectifying diodes with DNTT as a single transport medium are fabricated and characterized. Realizing unipolar current rectification from asymmetric metal contacts, a number of physical and electrical properties of DNTT are made accessible. Current–voltage measurement, broad-band impedance spectroscopy, drift-diffusion simulation, and equivalent-circuit modeling are combined to quantify important parameters such as dielectric constant, trap energy, and lifetime. These results provide a practical reference for the design and optimization of diverse electronic devices incorporating DNTT.

IoT-based Design and Implementation of an Intelligent Water Quality Monitoring System

Abstract: Water is an essential requirement for human survival, and a scarcity of water renders us unable to sustain our lives. The issue of water pollution and shortage is a pressing worldwide concern that necessitates the adaptation of water resources and the establishment of guiding principles at both the international and individual levels. Water contamination is the primary contributor to global disease outbreaks. In the past, the process of monitoring water quality relied on the physical collection of data, lacking the implementation of a smart sensor system. This resulted in inefficiencies, particularly in the areas of real-time data retrieval, device surveillance, and less precise data gathering. The integration of a real-time water quality monitoring method is important in order to guarantee the provision of a secure drinking water supply. Therefore, the concept of the Internet of Things (IoT), which facilitates the interconnection of diverse devices for the purpose of data interchange and collection, has emerged

Zenith Armor : Advancing Security with Zero Trust Measures

In an era of unprecedented digital connectivity and evolving cyber threats, traditional security paradigms have proven insufficient to safeguard sensitive information. This research paper introduces "Zenith Armor," a comprehensive exploration and implementation of Zero Trust Architecture (ZTA) in the realm of cybersecurity. The paper delves into the core principles of ZTA, emphasizing the imperative shift from implicit trust to continuous verification in securing organizational assets. "Zenith Armor" stands as a testament to the evolving landscape of security frameworks, challenging conventional models by prioritizing identity verification, least privilege access, micro-segmentation, and continuous monitoring. The framework not only mitigates the risks associated with unauthorized access but also addresses the lateral movement of threats within a network, enhancing overall resilience. This paper outlines the essential components of Zenith Armor, emphasizing its dynamic risk assessment capabilities, stringent access control policies, and encryption measures for safeguarding data. Additionally, the framework's applicability to diverse devices, coupled with user education initiatives, creates a robust security ecosystem. The implementation extends beyond traditional network perimeters, incorporating adaptive security measures, and continuous updates. Furthermore, "Zenith Armor" recognizes the significance of a proactive approach to security incidents. A well-defined incident response plan, coupled with vendor and third-party access controls, ensures a swift and effective response to potential breaches. The research paper concludes with a reflection on the transformative potential of "Zenith Armor" in redefining organizational security postures. By embracing the Zero Trust paradigm, organizations can fortify their defenses against an ever-evolving threat landscape, thereby establishing a new zenith in cybersecurity resilience. Keywords—delves ,imperative, micro-segmentation,proactive, zenith.v

Design and Development of an Intuitive Android Application for Smart Farming

Farmers face challenges in adapting to environmental changes, yet with decreasing implementation costs and a growing accessibility to technical expertise, progress is evident. This research introduces KisanVishwa, an Android app designed for smart farming. KisanVishwa optimizes precision agriculture systems, enabling farmers to monitor their fields in real time and address agricultural constraints. Tailored to meet the specific needs of today's farmers, the app provides user-friendly tools and the latest information on farm equipment, fertilizers, chemicals, technological advancements, and local distributors and retailers. By integrating KisanVishwa into their operations, farmers can overcome challenges associated with the adoption and maintenance of precision agriculture. KisanVishwa adopts a user-centric design for compatibility across diverse Android devices, integrating monitoring tools and collaborating with agricultural research institutions. Empowering farmers, the app addresses contemporary challenges, fostering informed decisions and efficiency. With its user-friendly interface, KisanVishwa promotes the adoption of precision agriculture, essential resources, and sustainable practices in the agricultural sector. Convincing traditional farmers to adopt advanced technologies may pose challenges. Functionality may depend on reliable internet, posing challenges in areas with limited connectivity.

Metal−support interaction in single-atom electrocatalysts: A perspective of metal oxide supports

The discovery of single-atom catalysts (SACs) represents a groundbreaking advancement in the field of catalysis over the past decades. With the in-depth exploration of relevant structure-activity relationships, the metal−support interaction (MSI) is widely adopted to elucidate variations in electronic structure and coordination configuration of atomic active sites on various kinds of supports. Herein, we briefly summarize the metal oxide supports for SACs fabrication, including the distinctive characteristics of metal oxide supports, enlightening advancements in metal oxide support-based SACs (MO-SACs), feasible preparation methods for MO-SACs and effective regulation strategies of MSI effect in MO-SACs. In addition, we present our viewpoints and outlook in this field to stimulate rational design and construction of novel MO-SACs applied in diverse renewable energy devices, while some universal suggestions are sincerely given to provoke thoughtful considerations during the research process.

Solution-processed nanoporous and faceted CuO electrocatalyst for enhanced solar-to-hydrogen and nitrate-to-ammonia production

Porosity control and facet engineering of electrocatalysts are critical for sustainable hydrogen production and wastewater upcycling into value-added chemicals. Herein, we report the sol-gel synthesis of a nanoporous faceted cupric oxide (nf-CuO) electrocatalyst film via a controlled polyesterification reaction. The formation mechanism of the unique nf-CuO morphology was analyzed and proposed. Notably, ligand additives such as ethylene glycol, citric acid, and polyethylene glycol function as morphology-controlling agents, and the polyesterification reaction between ligands can form covalent gel networks with trapped Cu ions. During thermal annealing, nucleation and nanoparticle growth along the covalent gel network enabled the formation of nanoporous and multifaceted CuO electrocatalysts on fluorine-doped SnO2 substrates, which was verified using ex-situ thermogravimetric/differential thermal analysis, Fourier transform infrared spectroscopy and transmission electron microscopy. The optimally synthesized nf-CuO exhibited high electrocatalytic activity in both photoelectrochemical hydrogen production and electrochemical nitrate reduction reactions. This enhanced performance was attributed to the nanoporosity-induced light-harvesting enhancement, enlarged surface area, and increased number of active sites. Our work emphasizes the importance of additive engineering to simultaneously control the porosity and facets of electrocatalysts and demonstrates its effectiveness in enhancing the electrocatalytic activity of diverse energy conversion devices.

A Novel Approach to Mitigating Packet Loss in Wireless Communication Networks through Machine Learning-Based Adaptive Error Correction

Wireless communication networks are integral to modern computing, facilitating seamless data transmission across diverse devices. Despite their importance, packet loss remains a persistent challenge, adversely affecting network reliability and performance. This research paper introduces a pioneering approach to tackle this longstanding issue, employing machine learning for adaptive error correction. The proposed method aims to significantly enhance the robustness and efficiency of wireless communication systems, presenting an innovative solution that has not been extensively explored in existing literature. Keywords—Wireless communication networks,Packet loss mitigation,Machine learning,Adaptive error correction ,Reinforcement learning,Dynamic decision-making,Real-time network conditions,Signal strength,Interference levels,Robustness,Throughput improvement,Latency reduction,Experimental setup,Performance metrics,Comparative analysis,Statistical significance,Sensitivity analysis,Ethical considerations,Practical implications,Future directions