Potential In Various Fields Research Articles

Durable Superhydrophobic Coatings with Attapulgite for Inhibiting 5G Radome Rain Attenuation.

5G radomes are easily wetted and stained by rainfall, which greatly reduces the quality of signal transmission. Superhydrophobic coatings are expected to solve this problem because of their unique wettability, but it is still challenging to develop robust superhydrophobic coatings via simple methods. Here, we report the design of robust superhydrophobic coatings containing oxalic acid-modified attapulgite (MDP) for inhibiting rain attenuation of 5G radomes. First, a homogeneous suspension was prepared by nonsolvent-induced phase separation of a silicone-modified polyester adhesive (SMPA) solution containing fluorinated MDP (F-MDP) nanorods. Superhydrophobic coatings can be easily prepared by spraying the suspension. The effects of phase separation and the SMPA/F-MDP ratio on the surface morphology, superhydrophobicity, and stability of the coatings were systematically investigated. The micro-/nanostructure and low surface energy endow the coatings with excellent static and dynamic superhydrophobicity. Compared with previous studies, the coatings exhibit excellent mechanical stability, flexibility, chemical stability, and pressure resistance due to the combined effects of adhesion by SMPA, self-similar micro-/nanostructures, reinforcement by the MDP nanorods, etc. Consequently, the coatings show good performance in preventing rain attenuation of 5G radomes, an emerging application of Superhydrophobic coatings. We believe that the coatings have great application potential in various fields, including 5G communication.

A review of deep learning techniques for enhancing spectrum sensing and prediction in cognitive radio systems: approaches, datasets, and challenges

Cognitive radio (CR) is an emerging wireless technology designed to optimize frequency band usage and address spectrum shortages. Spectrum sensing and prediction are crucial for cognitive radios to make intelligent spectrum access decisions and dynamically alter transmission parameters based on real-time spectrum conditions. These techniques contribute to the efficient use of the spectrum. However, they encounter significant obstacles such as noise uncertainty, low signal-to-noise ratios, channel fading, and more, necessitating robust and intelligent solutions. Deep learning has attracted much attention and displayed great potential in various fields in recent years. This review paper provides a thorough examination of the use of deep learning techniques in spectrum sensing and prediction in the context of cognitive radio. It examines the available literature in depth, highlighting the techniques, the assessment keys, datasets, and limitations of the investigations. The article seeks to provide significant insights and guidance for future developments in harnessing deep learning for better cognitive radio spectrum management by critically assessing the present state of research.

Unveiling Eco-Friendly Reverse Micelle Systems: Dimethyl Carbonate as a Novel Biocompatible Solvent.

In this study, we systematically explored the characteristics of dimethyl carbonate (DMC)/sodium 1,4-bis-2-ethylhexylsulfosuccinate (AOT) reverse micelles (RMs) in the presence of water using dynamic light scattering (DLS), proton nuclear magnetic resonance (1H NMR), and molecular probes. DMC, a biocompatible solvent, enables the formulation of AOT RMs without the need for a co-surfactant. DLS revealed that as the water content increased, the droplet sizes grew larger. 1H NMR studies indicated that at low water content, water molecules interacted with DMC via hydrogen bonding. This interaction promoted the penetration of DMC toward the interface, affecting the solvation of AOT's sulfonate group. At higher water content, a competition for hydrogen bonding emerged between water-water and water-surfactant molecules, leading to distinct interfacial properties, as evidenced by molecular probes. The critical micellar concentration for DMC/AOT/water RMs was 7x10-3 M, similar to RMs formed with other biocompatible solvents. The presence of water facilitated the solvation of the surfactant's polar regions, promoting the RMs formation. The polarity of this system was measured using the ET(30) value. This novel micellar system holds significant potential in various fields, including catalysis, nanomaterials, and green chemistry.

The Application of Ultrasound Pre-Treatment in Low-Temperature Synthesis of Zinc Oxide Nanorods

Zinc oxide, due to its unique physicochemical properties, including dual piezoelectric and semiconductive ones, demonstrates a high application potential in various fields, with a particular focus on nanotechnology. Among ZnO nanoforms, nanorods are gaining particular interest. Due to their ability to efficiently transport charge carriers and photoelectric properties, they demonstrate significant potential in energy storage and conversion, as well as photovoltaics. They can be prepared via various methods; however, most of them require large energy inputs, long reaction times, or high-cost equipment. Hence, new methods of ZnO nanorod fabrication are currently being sought out. In this paper, an ultrasound-supported synthesis of ZnO nanorods with zinc acetate as a zinc precursor has been described. The fabrication of nanorods included the treatment of the precursor solution with ultrasounds, wherein various sonication times were employed to verify the impact of the sonication process on the effectiveness of ZnO nanorod synthesis and the sizes of the obtained nanostructures. The morphology of the obtained ZnO nanorods was imaged via a scanning electron microscope (SEM) analysis, while the particle size distribution within the precursor suspensions was determined by means of dynamic light scattering (DLS). Additionally, the dynamic viscosity of precursor suspensions was also verified. It was demonstrated that ultrasounds positively affect ZnO nanorod synthesis, yielding longer nanostructures through even reactant distribution. Longer nanorods were obtained as a result of short sonication (1–3 min), wherein prolonged treatment with ultrasounds (4–5 min) resulted in obtaining shorter nanorods. Importantly, the application of ultrasounds increased particle homogeneity within the precursor suspension by disintegrating particle agglomerates. Moreover, it was demonstrated that ultrasonic treatment reduces the dynamic viscosity of precursor suspension, facilitating faster particle diffusion and promoting a more uniform growth of longer ZnO nanorods. Hence, it can be concluded that ultrasounds constitute a promising solution in obtaining homogeneous ZnO nanorods, which is in line with the principles of green chemistry.

Preparation of Pressure-Resistant and Mechanically Durable Superhydrophobic Coatings via Non-Solvent Induced Phase Separation for Anti-Icing.

Inspired by the lotus leaf effect, superhydrophobic coatings have significant potential in various fields, However, their poor pressure resistance, weak mechanical durability, and complex preparation processes severely limit practical applications. Here, a method for preparing pressure-resistant and durable superhydrophobic coatings by simply spray-coating a phase separation suspension containing fluorinated silica nanoparticles and polyolefin adhesive onto substrates is introduced, which forms superhydrophobic coatings with a porous and hierarchical micro-/nanostructure. The resulting superhydrophobic coatings exhibit outstanding pressure resistance, maintaining a Cassie-Baxte state after 18 days of submersion in 1 m of water. Furthermore, the coatings demonstrate remarkable mechanical durability, withstanding 200 cycles of Taber abrasion, 100 cycles of tape-peeling, and 750g of sand abrasion. The coatings also show excellent chemical stability, enduring long-term immersion in corrosive liquids and 120 d of outdoor exposure. Additionally, the coatings display excellent anti-icing properties and can be applied to various substrate surfaces. This approach improves on the limitations of conventional superhydrophobic coatings and accelerates the application of superhydrophobic coatings in real-world environments.

A Survey of the Routing Problem for Cooperated Trucks and Drones

The emerging working mode of coordinated trucks and drones has demonstrated significant practical potential in various fields, including logistics and delivery, intelligence surveillance reconnaissance, area monitoring, and patrol. The seamless collaboration between trucks and drones is garnering widespread attention in academia and has emerged as a key technology for achieving efficient and secure transportation. This paper provides a comprehensive and in-depth review of the research status on the routing problem for coordinated trucks and drones, covering aspects such as application background, cooperative modes, configurations, issues that have been taken into consideration, and solution methodologies.

Nanozymes in Biomedical Applications: Innovations Originated From Metal-Organic Frameworks.

Nanozymes exhibit significant potential in medical theranostics, environmental protection, energy development, and biopharmaceuticals due to their exceptional catalytic performance. Compared with natural enzymes, nanozymes have the advantages of simple preparation and purification, convenient production and low cost. Therefore, it is very important to prepare nanozymes quickly and efficiently, which not only helps to expand their application scope, but also can further exert their great potential in various fields. Metal-organic frameworks (MOF) materials serve as versatile substrates for constructing nanozymes, offering unique advantages like adjustable structure, high specific surface area, and porous channels. MOF coordination nodes constructed from metal ions or metal clusters have unique properties that can be leveraged to tailor nanozyme characteristics for different applications. This review describes and analyzes recent methods for constructing nanozymes using MOF materials, and explores their application prospects in biomedicine. By expounding the preparation techniques and biomedical applications of nanozymes, this review aims to inspire researchers to develop innovative nanozyme materials and explore new application directions.

Imidazole-stabilized gold nanoclusters as photocatalytic initiator and crosslinker to fabricate functional hydrogel

Photopolymerization is the most widely used method to fabricate hydrogels, which hold great potentials in various fields including biomedical applications, electronic skin and flexible wearable devices. Herein, gold nanoclusters (AuNCs) were employed as both efficient photo-initiators and crosslinkers to prepare polyacrylamide (PAM) hydrogels with good mechanical properties. Firstly, an imidazole-based monomer, ImPAA, was designed and synthesized, serving as an reducing agent and ligand to prepare imidazole-stabilized AuNCs (AuNC@ImPAA) in water. Under white light irradiation, AuNC@ImPAA efficiently initiated the radical polymerization of acrylamide monomers in water via a photocatalytic mechanism. The abundant vinyl groups on the surface of AuNC@ImPAA enabled its role as a multiple covalent crosslinker, endowing the resulting hydrogel with impressive mechanical properties. The maximum tensile strength and fracture strain was 287 kPa and 591 %, respectively. Moreover, triggered by UV light, the embedded AuNC@ImPAA within the hydrogel aggregated and converted into gold nanoparticles (AuNPs), yielding an AuNP/PAM hydrogel. This hybrid AuNP/PAM hydrogel exhibited a good photothermal conversion performance, with an efficiency of 14.14 % under 660 nm laser irradiation, and its potential as a photothermal actuator was demonstrated. Thus, we provide a facile light-mediated strategy to fabricate functional hydrogel.

Entropy optimization in Casson tetra-hybrid nanofluid flow over a rotating disk with nonlinear thermal radiation: a Levenberg–Marquardt neural network approach

Abstract This research employs a neural network, specifically the Levenberg–Marquardt algorithm, to characterize the entropy optimization performance in the electro-magneto-hydrodynamic flow of a Casson tetra-hybrid nanofluid over a rotating disk. The problem was formulated mathematically using equations for momentum, continuity, and temperature. This study converts ordinary differential equations (ODEs) into partial differential equations (PDEs) by a self-similarity transformation. The equations are resolved via the fourth-order Runge-Kutta method in combination with a shooting technique for obtaining the required datasets. Using the Levenberg-Marquardt algorithm (LMA), these datasets are characterised as training, testing, and validation. The proposed outcomes are presented in multiple tables and graphs. This trained neural network is then utilized to predict the heat flow velocity and Nusselt number of the rotating disk. The developed model was evaluated using mean square error, error analysis, and regression analysis, thereby confirming the consistency, accuracy, and reliability of the designed technique. The best validation performance for skin friction and the Nusselt number for the Casson tetra-hybrid nanofluid flow across a rotating disk is 8752e-05 at epoch 95 and 0.00033239 at epoch 37. Training, validation, testing, and all performance metrics of the artificial neural network model are close to unity. As magnetic field strength increases, temperature profiles rise in di-hybrid, ternary-hybrid, and tetra-hybrid nanoparticle scenarios. Tetra-hybrid nanofluids are considered superior fluids when compared to di-hybrid, ternary-hybrid, and tetra-hybrid nanofluids. This optimization method holds promise for diverse applications in biotechnology, microbiology, and medicine, offering significant potential for various fields.

Flexible iontronics with super stretchability, toughness and enhanced conductivity based on collaborative design of high-entropy topology and multivalent ion-dipole interactions.

All-solid-state ionic conductive elastomers (ASSICEs) are emerging as a promising alternative to hydrogels and ionogels in flexible electronics. Nevertheless, the synthesis of ASSICEs with concomitant mechanical robustness, superior ionic conductivity, and cost-effective recyclability poses a formidable challenge, primarily attributed to the inherent contradiction between mechanical strength and ionic conductivity. Herein, we present a collaborative design of high-entropy topological network and multivalent ion-dipole interaction for ASSICEs, and successfully mitigate the contradiction between mechanical robustness and ionic conductivity. Benefiting from the synergistic effect of this design, the coordination, de-coordination, and intrachain transfer of Li+ are effectively boomed. The resultant ASSICEs display exceptional mechanical robustness (breaking strength: 7.45 MPa, fracture elongation: 2621%, toughness: 107.19 MJ m-3) and impressive ionic conductivity (1.15 × 10-2 S m-1 at 25 °C). Furthermore, these ASSICEs exhibit excellent environmental stability (fracture elongation exceeding 1400% at 50 °C or -60 °C) and recyclability. Significantly, the application of these ASSICEs in a strain sensor highlights their potential in various fields, including human-interface communication, aerospace vacuum measurement, and medical balloon monitoring.

Fabrication Methods, Structural, Surface Morphology and Biomedical Applications of MXene: A Review.

Recently, two-dimensional (2-D) layered materials have revealed outstanding properties and play a crucial role for numerous advanced applications. The emerging transition metal carbides and nitrides, known as MXene with empirical formula Mn+1XnTx, have generated widespread attention and demonstrated impressive potential in various fields. The fabrication of 2-D novel MXene and its composites and their characterizations are applicable to vast applications in different areas such as energy storage, gas sensors, catalysis, and biomedical applications. In this review, the main focus is on the various synthesis methods, their properties, and biomedical applications. This review provides detailed illustrations of MXenes for many biomedical applications, including bioimaging, drug delivery, therapies, biosensors, tissue engineering, and antibacterial reagents. The challenges and future prospects were highlighted in a comprehensive manner, and the existing problems and potential for MXene-based biomaterials were analyzed with the goal of accelerating their use in the biomedical field.

Potential of Non-Saccharomyces Yeasts as Probiotics and Alternatives to Antibiotics in Animal Production.

Probiotics are live yeast or bacterial organisms that have beneficial effects on the host. Several microorganisms exhibit probiotic properties, the most common types being lactic acid bacteria, Bifidobacteria, spore-forming bacteria, and some yeast strains. Saccharomyces cerevisiae var. boulardii is the most important probiotic yeast species. However, another group of foodborne microorganisms, the so-called non-Saccharomyces yeasts (NSYs), has recently been re-evaluated and shown to have enormous potential in various fields of application, ranging from food fermentation to human and animal applications. NSYs are able to produce a range of bioactive compounds such as antimicrobials, mannoproteins, enzymes, polyunsaturated fatty acids, essential amino acids, vitamins, and β-glucans, which increases their potential applications as a new class of probiotics and/or alternatives to antibiotics in animal husbandry. In this review, we aim to highlight the potential and benefits of NSYs as probiotics and natural antimicrobials to improve animal health. Furthermore, the use of NSYs as biological alternatives to antibiotics to control foodborne pathogens in animal production is discussed.

Nonconjugated amylopectin-grafted copolymers with dual fluorescence/low-temperature phosphorescence emission and superior processability

Nonconjugated fluorescent polymers devoid of large π–π conjugated structures have received considerable attention due to their significant academic importance and broad application potentials in various fields. Herein, we report an effective strategy to fabricate multifunctional fluorescent amylopectin derivatives and reveal their unique aspects of aggregation-induced emission (AIE), cryogenic long-persistent phosphorescence (~6 s) and excellent processabilities characteristics, which are extremely different from traditional luminogens. These amylopectin-graft-poly(n-butyl acrylate-co-1-vinylimidazole) copolymers (Amylopectin-BVs) prepared by the grafting-from method employing RAFT and experienced subsequently with metal-ligand cross-linking. Specifically, clustering-triggered fluorescent emission or cryogenic long-persistent phosphorescence of amylopectin could be achieved by the aggregation of oxygen and nitrogen atoms along with conformation rigidification, which shows great promise in optoelectronic and biological applications.

Purification and tailored functionalities in detonation nanodiamond

Abstract Nanodiamonds (NDs) offer immense potential in various fields, but graphitic or metal-based impurities hinder their widespread adoption. Conventional purification methods often employ harsh chemicals or high temperatures, raising concerns about ND integrity and surface properties. Herein, we compared various strategies to purify and tailor the surface functional groups in the detonation-derived NDs. A facile 2-step purification strategy combining salt-assisted air oxidation (SAAO) and Fenton chemistry is particularly interesting for efficient and selective removal of graphitic impurities while preserving the diamond lattice structure. SAAO selectively burns off graphitic impurities at 450 °C under controlled oxygen flow, minimizing damage to the diamond core. Subsequently, Fenton's reagent (H2O2/Fe2+) introduces hydrophilic functional groups onto the ND surface, further enhancing diamond purity and promoting subsequent functionalization. This synergistic approach enables (i) highly efficient removal of graphitic impurities while preserving ND morphology and crystal structure, (ii) controlled introduction of surface functionalities, and (iii) improved colloidal stability of purified NDs. This green and efficient purification protocol is beneficial for tailoring ND properties and unlocking their full potential in diverse applications ranging from biomedicine and electronics to catalysis and quantum technologies.

Improving thermodynamic stability of double perovskites with machine learning: The role of cation composition

Perovskite materials have exhibited promising potential in various fields, including solar cells, photodetectors, and light-emitting devices, owing to their exceptional optoelectronic properties and low-cost synthesis methods. In this work, the quest for thermodynamic stability in perovskite materials for optoelectronic applications is addressed through a machine learning (ML) approach. We also analyzed the importance of cations in the stability of double perovskites. It was found that the AUC (area under the curve) and accuracy of the LightGBM model in classification are 0.918 and 0.92, respectively, and the RMSE (root mean square error) and R2-score in regression are 0.119 and 0.872, respectively. Subsequently, the model was used to predict the properties of 10 new perovskites with high accuracy. Finally, the analysis of cationic sites revealed that the stability of the double perovskite is significantly affected by the elements occupying the A, B, and B’ sites. Our results provide an optimal machine learning approach for discerning the thermodynamic stability of double perovskites, which can be a useful guide for tuning the properties of perovskite materials.

PISC-Net: A Comprehensive Neural Network Framework for Predicting Metasurface Infrared Emission Spectra.

Multifunctional metasurfaces have exhibited extensive potential in various fields, owing to their unparalleled capacity for controlling electromagnetic wave characteristics. The precise resolution is achieved through numerical simulation in conventional metasurface design methodologies. Nevertheless, the simulations using these approaches are inherently computationally costly. This paper proposes the Physical Insight Self-Correcting Convolutional Network (PISC-Net), which enables rapid prediction of infrared radiation spectra of metasurfaces with remarkable generalization capacity. In contrast to preceding prediction networks, we have enhanced the cognitive ability of the network to recognize physical mechanisms by designing parameter-communication modules and integrating a priori knowledge grounded in the parameter association mechanism. Additionally, we proposed an effective strategy for constructing data sets that facilitate precise tuning of absorption bands in the entire spectral range (3-14 μm) and serves to reduce the costs associated with data set development. Transfer learning is employed to obtain precise predictions for large-period metasurfaces from limited data sets. This approach demonstrates that a network trained exclusively on simulation data could predict experimental outcomes accurately, as proved by the comparative analysis between simulation, experimental testing, and prediction results. The average mean square error is less than 4%.

The role of machine learning for insight into the material behavior of lattices: A surrogate model based on data from finite element simulation

In recent years, lattice structures have gained extensive attention due to their unique properties and great potential in various fields. Their effective material properties can be obtained by several means, e.g., numerical homogenization methods, finite element (FE) simulations, and experiments. Numerical homogenization methods involve certain assumptions, and thus, their applications are limited to those lattice structures satisfying all assumptions. On the contrary, although the FE simulations and experiments can be performed for all lattice structures, they are more time-consuming and tedious than the numerical homogenization methods, especially for lattice structures with complex geometry. In this study, a surrogate model is presented to address all the drawbacks mentioned above. More precisely, the effective elastic modulus of two-dimensional lattice structures with triangular unit cells is predicted by a surrogate model trained with the random forest algorithm. The dataset is generated from FE simulations. Several geometric details along with the elastic modulus of the parent material are selected as input features. These input features are varied to cover a wide range of lattice structures encountered in engineering applications. The correlation between input features is investigated to test their independence. Furthermore, the contribution of each input feature to the prediction of the surrogate models is also thoroughly examined. The accuracy of the proposed surrogate model is evaluated through comparisons with FE simulations, numerical homogenization methods, and experiments for untrained datasets.

Enantioselective Construction of Anthracenylidene-Based Axial Chirality by Asymmetric Heck Reaction.

Anthracenylidene is an intriguing structural unit with potential in various fields. The study presents a novel approach to introducing axial chirality into this all-carbon core skeleton through a remotely controlled desymmetrization strategy. A palladium-catalyzed enantioselective Heck arylation of exocyclic double bond of anthracene with two distinct substituents at the C10 position is harnessed to realize such a transformation. The judicious identification of the P-centrally chiral ligand is pivotal to ensure the competitive competence in reactivity and stereocontrol when the heteroatom handle is absent from the anthracenylidene skeleton. Both C10 mono- and disubstituted substrates were compatible for the established catalytic system, and structurally diverse anthracenylidene-based frameworks were forged with good-to-high enantiocontrol. The subsequent derivatization of the obtained products yielded a valuable array of centrally and axially chiral molecules, thus emphasizing the practicality of this chemistry. DFT calculations shed light on the catalytic mechanism and provided insights into the origin of the experimentally observed enantioselectivity for this reaction.

Development of organic aggregation-induced emission fluorescent materials based on machine learning models and experimental validation

Organic fluorescent materials have attracted significant attention due to their unique chemical and optoelectronic properties. Unfortunately, traditional organic fluorescent materials are limited by aggregation-caused quenching (ACQ), which restricts their application potential in various fields. The discovery of aggregation-induced emission (AIE) materials has revolutionized the field by providing a solution to this issue. Despite considerable efforts to design and synthesize novel AIE materials, the development process still relies on tedious trial-and-error methods. Therefore, advanced cross-disciplinary techniques must be introduced to establish AIE property prediction models and enhance the development efficiency of organic AIE fluorescent materials. Machine learning (ML) is a powerful tool that has been widely applied across diverse fields to accelerate material development through complex relationship mapping of large data sets. This study presents an ML-based approach aimed at accelerating the development of organic AIE fluorescent materials. We assembled a dataset of 3,074 molecules with AIE/ACQ properties, developed a prediction model using the LightGBM ensemble learning algorithm, and used combined molecular fingerprints as input. The model achieved a remarkable independent test-set accuracy of up to 0.974 and its extrapolation ability was validated by out-of-sample validation (accuracy = 0.963). In addition, experimental validation was performed to assess the reliability of the ML prediction model in an unknown molecular space, showing a satisfactory agreement between the model predictions and the experimental results. Our work highlights the potential of ML for rapidly and accurately predicting the AIE property of unknown molecules and screening AIE materials, thereby accelerating the development of organic AIE fluorescent materials.

Exploring the Social Role of Contemporary Women from the Perspective of the Lack of Historical Female Characters

Historically, women have been underrepresented in literature, the arts, and historical records, and their achievements and roles have often been marginalized or ignored. This phenomenon has led to an intriguing paradox: despite the fact that contemporary women have demonstrated strong abilities and potential in various fields, they are still usually considered to be in a disadvantaged position compared to men. This paper aims to explore the complex reasons behind the paradox of womens underrepresentation in history. To this end, it will review the lack of womens image in history, analyze the impact of social, cultural, and institutional factors on this lack of representation, and explore the gender issues that exist in contemporary society. The paper discusses in detail women's achievements in contemporary society, the embodiment of womens power, and the struggle for womens rights, demonstrating the strong capabilities and potential of contemporary women in many fields. The study concludes that the marginalization of women in historical narratives is a result of social structures, cultural norms, and institutional biases. Despite these challenges, contemporary women have made significant strides in various fields, suggesting that gender should not be a determining factor in assessing competence or potential. This paper argues for a greater awareness of gender equality and calls for a fairer and more inclusive reconstruction of historical narratives to ensure that the contributions of all genders are fairly recognized.