Range Of Applications Research Articles

Experimental study of solid-liquid origami composite structures with improved impact resistance

In this paper, a liquid-solid origami composite design is proposed for the improvement of impact resistance. Employing this design strategy, Kresling origami composite structures with different fillings were designed and fabricated, namely air, water, and shear thickening fluid (STF). Quasi-static compression and drop-weight impact experiments were carried out to compare and reveal the static and dynamic mechanical behavior of these structures. The results from drop-weight impact experiments demonstrated that the solid-liquid Kresling origami composite structures exhibited superior yield strength and reduced peak force when compared to their empty counterparts. Notably, the Kresling origami structures filled with STF exhibited significantly heightened yield strength and reduced peak force. For example, at an impact velocity of 3 m/s, the yield strength of single-layer STF-filled Kresling origami structures increased by 772.7% and the peak force decreased by 68.6%. This liquid-solid origami composite design holds the potential to advance the application of origami structures in critical areas such as aerospace, intelligent protection and other important fields. The demonstrated improvements in impact resistance underscore the practical viability of this approach in enhancing structural performance for a range of applications.

Collimated hot electron generation from sub-wavelength grating target irradiated by a femtosecond laser pulse of relativistic intensity

We investigate the production of hot electrons from the interaction of relativistically intense (I>1018 W/cm2) ultrashort (25 fs) laser pulses with sub-wavelength grating target. We measure the hot electron angular distribution and energy spectra for grating target and compare them with those from a planar mirror target. We observe that hot electrons are emitted in a collimated beam along the specular direction of the grating target. From the measurements, we see fast electron temperature and flux for grating are higher than those for mirror due to a stronger coupling with the laser. We performed numerical simulations, which are in good agreement with experimental results, and offer insights into the acceleration mechanism by resulting electric and magnetic fields. Such collimated fast electron beams have a wide range of applications in applied and fundamental science.

Antimicrobial Activity against Antibiotic-resistant Pathogens and Antioxidant Activity and LCMS/MS Phytochemical Content Analysis of Selected Medicinal Plants

Medicinal plants are a major source of numerous therapeutic agents, and the emergence of pathogenic bacteria has rekindled interest in traditional medicine systems as an alternative approach to overcoming resistance. The dried plant material of four medicinal plants, namely Terminalia arjuna (bark), Terminalia bellirica (fruit), Aegle marmelos (leaves), and Bacopa monnieri (leaves), was powdered, and aqueous extracts were prepared. The antimicrobial activity of the extracts was evaluated against three clinically important strains: Pseudomonas aeruginosa, Staphylococcus aureus, and Escherichia coli 385. As E. coli 385 was resistant to the broadest spectrum of antibiotics tested, it was classified as (MDR multi-drug resistant). E. coli, Bacillus subtilis, Mycobacterium smegmatis (MTTC), and Vibrio alginolyticus (ATCC) were also assessed using the agar well diffusion method for zones of inhibition and minimum inhibitory/bactericidal concentration (MIC/MBC). Clinically important strains were found to be sensitive to the aqueous extract of T. bellerica (19.51 ± 0.61 mm) with MICs ranging from 0.31 to 0.62 mg/ml. The MDR strain was also sensitive to Bellirica monnieri (16.10 ± 0.31 mm) aqueous extract. To determine the potential for a wide range of applications, the antioxidant activities of the extracts were evaluated using DPPH, ABTS, and FRAP assays. The T. arjuna plant extract exhibited the highest radical scavenging activity with the lowest EC50 values for DPPH (1.15 ± 0.061 mg/ml) and ABTS (1.02 ± 0.07 mg/ml). The plant extracts were characterized by UV-visible spectroscopy, Fourier-transform infrared spectroscopy, and LC-MS/MS.

Advanced visual components inspired by animal eyes

Abstract Artificial vision systems pervade our daily lives as a foremost sensing apparatus in various digital technologies, from smartphones to autonomous cars and robotics. The broad range of applications for conventional vision systems requires facile adaptation under extreme and dynamic visual environments. However, these current needs have complicated individual visual components for high-quality image acquisition and processing, which indeed leads to a decline in efficiency in the overall system. Here, we review recent advancements in visual components for high-performance visual processing based on strategies of biological eyes that execute diverse imaging functionalities and sophisticated visual processes with simple and concise ocular structures. This review first covers the structures and functions of biological eyes (i.e., single-lens eyes and compound eyes), which contain micro-optic components and nanophotonic structures. After that, we focus on their inspirations in imaging optics/photonics, light-trapping and filtering components, and retinomorphic devices. We discuss the remaining challenges and notable biological structures waiting to be implemented.

Synthesis of graphene oxide and nitrogen-doped graphene oxide by nanosecond pulsed laser ablation of graphene in liquid for fiber optic gas sensing application

Heteroatom-doped graphene oxide has a wide range of applications in bio-imaging and sensing. In this work, Graphene Oxide (GO) and Nitrogen-doped GO (NG) were synthesized by laser ablation of Graphene in ethanol. The dopant Diethylenetriamine (DETA) is used in different amounts for different nitrogen concentrations. Optical, morphological, structural, and elemental composition studies were done by UV–vis spectroscopy, FT-IR, FE-SEM, XRD, Raman, and EDAX analysis, respectively. The nitrogen doping on the surface of GO was confirmed by FT-IR and EDAX studies. Upon laser ablation with fundamental wavelength, the graphene is converted to spherical GO nanoparticles, and nitrogen doping is done to produce porous nano coral structured NG nanoparticles. The sensitivity and selectivity of GO and NG for ammonia, ethanol, and acetone target gaseous were investigated and compared. NG sample shows excellent sensitivity and selectivity towards acetone gas. And the Nitrogen-doped graphene oxide can be considered an ideal material for gas-sensing applications.

Editorial

Represent the Organizing Committee of The 1st International Conference on Applied Physics and Mathematics (ICAPM2023). The conference was a great success providing appropriate platform to researchers, scholars and industry practitioners to share their work and experience on the Changsha, China in different sectors.In today’s era, the research and development of Applied Physics can be considered very valuable, it also has been a buzzword in recent times majorly in last few years. Almost every research and scholar is getting involved in this context to derive significant and tangible gains to the benefit of the society as a whole.And as we know, Applied Physics is generally considered to be composite because it contains several cross cutting core disciplines, such as Semiconductor Physics, Nanoscale physics, Electronics Design & Manufacturing and GIS/GPS Systems, etc. The basic concepts and technologies of these disciplines have been combined to form the basis of Applied Physics. These technologies can help people generate important insights to support further decisions. However, the application of Applied Physics is very challenging, which leads to the diversity of its development. Since the past few decades, Applied Physics has been one of the most interesting fields. It has recently become an inevitable part of our daily life by adding technology to solve practical problems.The 1st International Conference on Applied Physics and Mathematics (ICAPM2023) focuses on works that have been done for development of new technology or enhancement of the existing ones to make them applicable in diverse range of sectors and corresponding problems. We have received a good number of responses of around 65 papers from researchers, scholars and industry practitioners covering a wide range of application areas.According to their quality, degree of conformity with our topics and other indicators, we finally received 23 articles.We hope this proceeding will provide valuable insights and serve for new scholars interested to explore Applied physics, Application of applied physics and Mathmatics in various sectors.Editor:Xilong QuList of Committees are available in this Pdf.

Comparison of different supervised learning algorithms for position analysis of the slider-crank mechanism

Slider-crank mechanisms have a wide range of applications in machine design, primarily for converting rotary motion into reciprocating motion, as seen in internal combustion engines, pumps, compressors, and human-powered vehicles. Due to their widespread usage, analytical and graphical solutions for the position analysis of slider-crank mechanisms are introduced in various textbooks and lecture notes. Recently, artificial neural network algorithms have been applied to various design applications across different research fields, and mechanism design is no exception. However, investigations into the application of neural network algorithms specifically to slider-crank mechanisms are quite limited. In this study, the analytical position analysis of the slider-crank mechanism is presented, and datasets are obtained first. Subsequently, the application of following three different supervised learning algorithms to the position analysis of the slider-crank (R-RRT) mechanism is investigated using analytical solution datasets: the Levenberg-Marquardt Backpropagation (LM) algorithm, Bayesian Regularization (BR) algorithm, and Scaled Conjugate Gradient Descent (SCG) algorithm. These three algorithms were chosen due to their distinct characteristics. The main objectives of this study are to highlight the differences among various neural network algorithms' applications and to understand the applicability and suitability of different neural network algorithms for kinematic position analysis in mechanisms. The results indicate that the Bayesian regularization algorithm yields the best results, while the Levenberg-Marquardt Backpropagation algorithm exhibits the best performance.

Ion evaporation-induced tip streaming from liquid drops of ionic liquids

Ion evaporation from charged surfaces of ionic liquids has aroused significant interest due to its wide range of applications in various fields, such as ionic liquid ion sources (ILIS), electrospray thrusters, and high-precision etching, among others. This study delves into the transient electrohydrodynamics of ionic liquid drops undergoing ion evaporation with the assistance of an externally applied electric field. A transient electrohydrodynamic (EHD) model is developed to make up for the dearth of direct observation (visualization). A structure morphologically resembling the traditional cone-jet is depicted, featuring charged jets emitted from the drop's ends that subsequently break into tiny droplets. In contrast to the traditional cone-jet, this structure forms through a distinct mechanism, with charge depletion induced by ion evaporation playing a crucial role. A comprehensive exploration of parameter influences reveals the following key findings: (a) the normalized radius of the jets increases with the augmentation of the dimensionless number Ξ, which is defined as the ratio of the electric potential energy of ions to their activation energy for evaporation; (b) enhancing the electric Bond number Bo, which quantifies the ratio of Maxwell stress to surface tension, expedites the temporal evolution of tip streaming.

Investigation on gas–liquid–solid three-phase flow characteristics in aircraft wastewater pipes

The aircraft wastewater system is an important system for civil aircraft. The wastewater is transported through the pipeline under the pressure difference between the cabin and the vacuum wastewater tank, and its flow time and characteristics are crucial for the system design. In this paper, the gas–liquid–solid flow of aircraft wastewater is investigated using the Euler–Euler–Euler multiphase flow numerical simulation method. The influencing factors of wastewater flow are analyzed. Afterward, the impact of different factors on the flow time and characteristics is analyzed, such as pressure differences between the cabin and the tank, pipeline length, pipeline inner diameter, and the disturbance for a straight long pipeline. The results show that the wastewater flow time is reduced from 6.4 to 2.6 s with the increase in the pressure difference from 20 to 60 kPa and increased from 3.4 to 10.2 s with the increase in the pipeline length from 30 to 70 m. The results obtained provide a modern theoretical basis for the design of such systems of vacuum wastewater and have a wide range of applications not only in aviation but also in other modern kinds of transport (rail, shipping), contributing to reducing their negative impact on the environment.

Feasible Synthesis of N,O‐Bidentate Difluoroboron Chromophores through Direct Functionalization of Quinoxalin‐2(1H)‐ones with Ketones in One Shot

Comprehensive SummaryOrganic difluoroboron complexes are a kind of potential platforms for a wide range of applications owing to their excellent photophysical properties. Herein, we have explored a simple and direct synthesis methodology for a library of N,O‐bidentate difluoroboron complexes from quinoxalin‐2(1H)‐ones and ketones in one shot. The photophysical properties of the generated complexes were evaluated and the application potential of these compounds on subcellular imaging was also explored.

Development of a machine learning detector for North Atlantic humpback whale song.

The study of humpback whale song using passive acoustic monitoring devices requires bioacousticians to manually review hours of audio recordings to annotate the signals. To vastly reduce the time of manual annotation through automation, a machine learning model was developed. Convolutional neural networks have made major advances in the previous decade, leading to a wide range of applications, including the detection of frequency modulated vocalizations by cetaceans. A large dataset of over 60 000 audio segments of 4 s length is collected from the North Atlantic and used to fine-tune an existing model for humpback whale song detection in the North Pacific (see Allen, Harvey, Harrell, Jansen, Merkens, Wall, Cattiau, and Oleson (2021). Front. Mar. Sci. 8, 607321). Furthermore, different data augmentation techniques (time-shift, noise augmentation, and masking) are used to artificially increase the variability within the training set. Retraining and augmentation yield F-score values of 0.88 on context window basis and 0.89 on hourly basis with false positive rates of 0.05 on context window basis and 0.01 on hourly basis. If necessary, usage and retraining of the existing model is made convenient by a framework (AcoDet, acoustic detector) built during this project. Combining the tools provided by this framework could save researchers hours of manual annotation time and, thus, accelerate their research.

A Noniterative Supervised On-Chip Training Circuitry for Reservoir Computing Systems.

Artificial neural networks (ANNs) is an exponentially growing field, mainly because of its wide range of applications to everyday life such as pattern recognition or time series forecasting. In particular, reservoir computing (RC) arises as an optimal computational framework suited for temporal/sequential data analysis. The direct on-silicon implementation of RCs may help to minimize power and maximize processing speed, that is especially relevant in edge intelligence applications where energy storage is considerably restricted. Nevertheless, most of the RC hardware solutions present in the literature perform the training process off-chip at the server level, thus increasing processing time and overall power dissipation. Some studies integrate both learning and inference on the same chip, although these works are normally oriented to implement unsupervised learning (UL) [with a lower expected accuracy than supervised learning (SL)], or propose iterative solutions (with a subsequent higher power consumption). Therefore, the integration of RC systems including both inference and a fast noniterative SL method is still an incipient field. In this article, we propose a noniterative SL methodology for RC systems that can be implemented on hardware either sequentially or fully parallel. The proposal presents a considerable advantage in terms of energy efficiency (EE) and processing speed if compared to traditional off-chip methods. In order to prove the validity of the model, a cyclic echo state NN with on-chip learning capabilities for time series prediction has been implemented and tested in a field-programmable gate array (FPGA). Also, a low-cost audio processing method is proposed that may be used to optimize the sound preprocessing steps.

Proposal of ensemble qubits with two-atom decay

We propose and analyze a novel approach to implement ensemble qubits. The required anharmonicity is provided by a simultaneous decay of two atoms (i.e. two-atom decay), which is achieved by fully quantum degenerate parametric amplification. For an atomic ensemble, the two-atom decay generates and stabilizes a 2D quantum manifold, which is spanned by the ground and single-excited superradiant states. Moreover, this nonlinear decay process can strongly suppress transitions to higher-excited superradiant states, and convert residual transitions into an effective decay from the single-excitation superradiant state to the ground state. Our method does not require Rydberg dipole blockade and, thus, strong atom-atom interactions, compared to previous work. This indicates that it can be applied to typical atomic or spin ensembles in simple experimental setups. Remarkably, our idea is compatible with the cavity protection mechanism, and therefore spin dephasing due to inhomogeneous broadening can be strongly suppressed. The presented ensemble qubit provides a new platform for quantum information processing, and also extends the range of applications of atomic or spin ensembles.

Solvent-induced microphase separation membranes of block poly (aryl ether sulfone) copolymer with high selectivity of O2/N2 separation

Gas separation membranes have a wide range of applications in industry, and there are two important factors in judging membrane standards permeability and selectivity. How to improve the selectivity of gas separation membranes while improving permeability is a challenge. In this study, poly (aryl ether sulfone) (PES) block copolymers were synthesized from fluorine-terminated polysulfone (PSF–F) and hydroxyl-terminated cardo PES containing phenyl substituted isoindolinone side group (PES-PPI-OH). Large side groups can enlarge the free volume and hinder chain movement to improve gas permeation behavior. Solvent-induced microphase separation of block copolymers by casting membranes with solvent benzyl alcohol (BA) can improve selectivity. The PSF-b-PES-PPI (10:10) BA membrane maintain good mechanical properties and tensile strength up to 93 MPa which compared to random polymer membrane rises to 14 MPa. Gas permeation at 23 °C showed that the O2/N2 selectivity of PSF-b-PES-PPI (5:15)BA membrane was 10.6, which was the 2008 Robeson upper bound. Thus, poly (aryl ether sulfone) materials showed great potential application for O2/N2 separation.

A Fundamental Study on the Preparation of Sodium Tungstate from Wolframite via the Smelting Process

Tungsten is a high-value resource with a wide range of applications. The tungsten metal is produced via ammonium paratungstate, which is a multi-stage process including leaching, conversion, precipitation, calcination, and reduction. A short process to produce tungsten metal from the electrolysis of molten sodium tungstate has been demonstrated. However, sodium tungstate cannot be directly produced from wolframite in the conventional hydrometallurgical process. There was no information reported in the literature on producing sodium tungstate directly from tungsten concentrates. The present study proposed a simple and low-cost process to produce sodium tungstate by high-temperature processing of wolframite. The mixtures of wolframite, sodium carbonate, and silica were melted in air between 1100 and 1300 °C. High-density sodium tungstate was easily separated from the immiscible slag, which contained all impurities from wolframite, flux, excess sodium oxide, and dissolved tungsten oxide. The slag was further water leached to recover sodium tungstate in the solution. Effects of Na2CO3/Ore and SiO2/Ore ratios, temperature, and reaction time on the recovery of tungstate and the purity of sodium tungstate were systematically studied. Sodium tungstate containing over 78% WO3 was produced in the smelting process, which is suitable for the electrolysis process. The experimental results will provide a theoretical basis for the direct production of sodium tungstate from wolframite. The compositions of the WO3-containing slags and sodium tungstate reported in the present study fill the knowledge gap of the tungsten-containing thermodynamic database. Further studies to use complex and low-grade tungsten concentrates to produce sodium tungstate are underway.

Designer spectrographs for applications in the advanced undergraduate instructional lab

In an advanced undergraduate instructional laboratory, it is often necessary to analyze the spectrum of light emitted from an experimental setup. There are numerous instruments that are used to accomplish this analysis, including spectrometers and spectrographs. In this report, we present 3D-printed, low-budget spectrographs (∼ US $200), which are specifically designed for different applications. For example, one can either observe a visible spectrum over a large range of wavelengths about a desired center wavelength or achieve more precise measurements by choosing a smaller part of the visible spectrum. Our generalized design approach is well within the knowledge base of advanced undergraduate physics majors and can be applied to a wide range of applications within the visible spectrum. To demonstrate the utility of these designer spectrographs, we provide examples of recording multiple doublets in the sodium spectrum (a determination of the fine structure spin-orbit splitting of the 3p energy level) as well as measuring the wavelength differences between the hydrogen and deuterium Balmer alpha lines (a measurement of an isotope shift).

The influence of the geographic positioning system error on the quality of ship magnetic signature reproduction based on measurements in sea conditions

In previous studies, the authors performed the magnetic signature reconstruction of the marine ship Zodiak as part of the measurement campaign focused on recording magnetic data and the relative position of a ship during its passage over a magnetometer immersed on the testing ground. A high degree of representation of the magnetic signature was obtained. However, the recorded measurement data revealed new patterns of the multi-dipole model behavior that were not observed in the synthetic data based analyzes. It was assumed that the main factor influencing the abovementioned behavior of the model is the error in determining the geographical position of the ship in relation to the magnetometer. Therefore, another research was carried out to determine the relative position of the ship and the measurement device in sea conditions, in the area of the test site used in the previous study. For this purpose, two different classes of GPS receivers were used. The first receiver was the same as that used to determine the position of Zodiak in the previous measurement campaign, while the second receiver, treated as a reference, was a top-class geodetic receiver. The difference in indications between these two receivers gave a picture of the scale of errors in the data recorded during the previous measurement campaign. These errors are used in the article to analyze the effect of inaccuracies in determining the ship position on the quality of magnetic signature reproduction. Two types of signature reproduction error were introduced – the error based only on the data collected from the ship’s paths, and the error in the entire area of magnetic anomaly. The model of Zodiak was used to determine the value of the magnetic flux outside the paths. Profiles of differences in indications of GPS receivers at sea which were obtained from actual measurements were used to analyze the errors in determining the ship position. A measurable result of the work reported in the article is the map of the loss of quality of magnetic signature reproduction as a function of the ship position determination error, which can indicate the range of applicability of the model and the described method.

Comprehensive Review on Drug-target Interaction Prediction - Latest Developments and Overview.

Drug-target interactions (DTIs) are an important part of the drug development process. When the drug (a chemical molecule) binds to a target (proteins or nucleic acids), it modulates the biological behavior/function of the target, returning it to its normal state. Predicting DTIs plays a vital role in the drug discovery (DD) process as it has the potential to enhance efficiency and reduce costs. However, DTI prediction poses significant challenges and expenses due to the time-consuming and costly nature of experimental assays. As a result, researchers have increased their efforts to identify the association between medications and targets in the hopes of speeding up drug development and shortening the time to market. This paper provides a detailed discussion of the initial stage in drug discovery, namely drug-target interactions. It focuses on exploring the application of machine learning methods within this step. Additionally, we aim to conduct a comprehensive review of relevant papers and databases utilized in this field. Drug target interaction prediction covers a wide range of applications: drug discovery, prediction of adverse effects and drug repositioning. The prediction of drugtarget interactions can be categorized into three main computational methods: docking simulation approaches, ligand-based methods, and machine-learning techniques.

Understanding the behaviors of ZIF-67 reinforced electrospun carbon nanofibers in the preparation and stabilization

MOFs-reinforced electrospun carbon nanofibers offer a wide range of applications in the field of energy storage. In this study, ZIF-67 nanoparticles were produced and combined with polyacrylonitrile for electrospinning before being stabilized and carbonized to form a carbon nanofiber. BET, FT-IR, EA, DSC, and other techniques were used to investigate the stabilizing behaviors of PAN electrospun fiber with varying ZIF-67 concentrations at different temperatures. Under the same stabilizing settings, it was discovered that PAN electrospun fiber containing ZIF-67 nanoparticles could absorb more oxygen, promoting a higher degree of cyclization and crosslinking. Therefore, it was preferred to prepare carbon nanofibers at a lower stabilization temperature. Importantly, the degree of stability and disorder of the PAN/ZIF-67 stabilized fiber has been described by the FT-IR and the Raman spectra. This research deepens understanding about the formation of MOF-reinforced carbon nanofibers and expands our knowledge of the stability of PAN/MOF hybrid fibers.

Investigation of Planar Antenna Performances on Commercially Available Dielectric Materials

Planar RF antennas with their conformal geometry, good RF performances and ease of construction find a wide range of applications ranging from mobile phones to RADAR and wearable electronic gadgets to medical implant devices. The selection of a suitable dielectric material is by far the most crucial step in the antenna design process to achieve the desired RF characteristics. The directivity, efficiency and bandwidth of an antenna depend on the substrate dielectric constant, loss tangent and material thickness. This paper investigates the RF performances of two planar antennas designed using two commercially available dielectric materials namely RT Duroid and FR4 with a full wave 3D EM simulation tool. The results show the superior RF characteristics of the antenna designed using RT Duroid over FR4. The antenna designed with RT Duroid substrate is fabricated and the measurement results obtained are found to be in good agreement with the simulation results.