Application Profile Research Articles

Hybridization of woven kenaf and unidirectional glass fibre roving for unsaturated polyester composite

This is a study on the mechanical properties of kenaf/glass-reinforced polyester composites intended for use in structural profiles with a wall thickness by max. 6 mm. Mechanical properties such as tensile, compression, bending and interlaminar shear stress were investigated by comparing the hybrid variants with the pure fibreglass variant. According to the study, woven kenaf/unidirectional glass roving (WK/UG) alternate recorded the highest tensile properties among hybrid samples. It demonstrated a decrement of about 8.2% of the tensile strength (404.54 MPa) and 10.7% of tensile modulus (24.54 GPa) compared to conventional fibreglass samples. Alternating WK/UG samples demonstrated higher compressive strength (417.15 MPa) compared to other hybrid specimens, recording a slight decrease at 6.09% compared to pure fibreglass composites. The highest bending properties were also observed in hybrid alternate WK/UG samples among other hybrid laminates with only a decrement of 4.13% in modulus of rupture (456.33 MPa) and 1.9% in modulus of elasticity (14.49 GPa) when compared to the control specimen. The ILSS of hybrid composites 2WK/3UG/2WK (30.97 MPa) and WK/UG alternate (34.90 MPa) showed good agreement with the pure fibreglass (42.33 MPa) composites. Using SEM images, tensile fractured specimens were examined to comprehend composites’ failure mechanism and interfacial adhesion. Overall, woven kenaf/unidirectional glass roving alternate sequence is chosen as a potential alternative in developing structural profiles for moderate load-bearing structural applications. In contrast, 3WK/UG/3WK with a higher kenaf to glass ratio demonstrate potential in low load-bearing structural profile applications.Graphical abstract

Hierarchical spontaneous self-assembly of polyethylenimine-poly(propylene glycol) copolymer into cationic polyelectrolyte pH-responsive thermogel

Temperature-responsive hydrogels, otherwise known as thermogels, are promising biomaterials which undergo phase change from liquid to a solid-like gel state when warmed. Although poly (ethylene glycol) (PEG) is employed extensively as the hydrophilic segment in thermogelling polymer design, recent years has witnessed development of new families of PEG-free thermogelling copolymers which offer new opportunities in expanding material property profiles for new biomedical applications. Herein, we study the supramolecular self-assembly process of PEG-free amphiphilic branched graft copolymer containing polyethylenimine (PEI) and poly (propylene glycol) (PPG), which shows both thermogelling ability and pH-responsive gelation behaviour. Additionally, we demonstrate the ease of post-synthetic modulation of thermogel properties by amine functionalisation on the PEI copolymer segments through convenient nucleophilic substitution and amide formation reactions. The synthetic platform offered by PEI-PPG thermogels allow facile customisation of these versatile materials for targeted biomedical applications.

Sketching of interactive VoIP traffic with multivariate statistical learning-based classification

Classifying VoIP (Voice over Internet Protocol) traffic is vital for optimizing network performance and Quality of Service (QoS). This study introduces the Multivariate Statistical-Based Classification (MVSC) system, designed to classify network traffic with high accuracy and efficiency. As traditional methods struggle in the diverse and complex landscape of today’s network traffic, which includes voice, video, gaming, and data, the MVSC algorithm rises to the challenge. It employs Statistical Dissemination and leverages various statistical features such as Packet Size, Inter-Arrival Statistics, Packet and Data rates, Flow Length, and Five-tuple information to create nuanced profiles of network traffic packets. These packets are then grouped into distinct clusters based on their statistical attributes through Application Flow Cluster Grouping. A unique aspect of the MVSC system is its approach to representing each application flow as points in a two-dimensional space, where distances to predefined application profiles are calculated. The nearest profile then determines the type of VoIP traffic. Experimental results using university traffic data (KU-IDS) underscore the system’s high accuracy, consistently around 98-99%. These findings affirm the system’s suitability for real-time deployment. In summary, the MVSC system offers a robust and efficient solution for VoIP traffic classification, significantly boosting network performance and QoS, and proving to be an invaluable asset in contemporary network management.

A lightweight performance proxy for deep‐learning model training on Amazon SageMaker

SummaryCloud computing has become popular for training deep‐learning (DL) models, avoiding the costs of acquiring and maintaining on‐premise systems. SageMaker is a cloud service that automates the execution of DL workloads. Its features include automatic hyperparameter optimization and use of spot instances. Nonetheless, it does not assist in selecting the right instance type for a workload. In public clouds, rent price depends on the configuration of the chosen instance type. Advanced and faster instances are typically more expensive, but not always the best choice. To select the optimal instance type, users must compare the workload's relative performance (and hence cost) on several candidates. Building on the execution profiles of multiple DL applications, we model the performance and cost of training DL applications on SageMaker and propose a lightweight technique to estimate these at low temporal and monetary cost. This method is a performance proxy that can be used to replace more expensive performance measurement procedures. So, it could speed up any technique that relies on such measurements. We show how it can help cloud customers seeking suitable instance types to train DL models, and that it can accurately predict the performance of different instance types when training these models on SageMaker.

165 Predicting Success: A Mixed Model of KL2 Trainee Profiles and Outcomes

OBJECTIVES/GOALS: Penn State CTSI supports KL2 career development awards for faculty seeking to become leaders in clinical and translational research. CTSAs can benefit from a better understanding of KL2 applicant profiles and trainee outcomes. Predictive modeling of KL2 records provides insights into institutional processes and continuous improvement goals. METHODS/STUDY POPULATION: Collecting KL2 application records at Penn State CTSI from 2017 to 2023, comprising both accepted and not accepted candidate profiles, this study used a generalized logistic mixed model with binomial distribution to understand the factors predictive of KL2 trainee acceptance, (n=47). The following factors were modeled as potentially predictive of scholars’ acceptance: Institution-specific Processes—Campus; Terminal Degree Type; College of Residency, Applicant Demographics and Portfolio—Minoritized or Protected Groups; Mean Application Score; Rurality Focus; Gender, and Outcomes—Post-Program h-index. RESULTS/ANTICIPATED RESULTS: Only Campus and Degree were significant factors predictive of trainee acceptance (r<.0001), with a particular campus and the MD degree-designation both exerting selectional pressures on acceptance rates. Applicant demographics were not significant historical factors in selection despite the most recent trainee cohort comprised of all women. Similarly, while our CTSA focuses on rural inequality and accessibility, a research proposal focused on rurality was not a significant factor for acceptance. Notably, NIH-scaled application scores and post-program h-indices were not significant for accepted and non-accepted applicants. DISCUSSION/SIGNIFICANCE: The absence of applicant-focused selectional pressure is striking—Penn State CTSI does not significantly select for gender, URM, or URP status. Administration is now empowered to intentionally engage, recruit, and retain from our other affiliated campuses and colleges.

Resume Optimisation and Suggestions using Large Language Models: A Review

Abstract: In the rapidly evolving job market, the compatibility of a candidate's resume with the job description is critical to the application process. Conventional resume optimization techniques frequently depend on keyword matching, which is shallow and cannot comprehend linguistic nuances or context, resulting in an inadequate match between applicant's profiles and job criteria. This study presents a novel application that uses Bidirectional Encoder Representations from Transformers (BERT) to generate language embeddings from job descriptions and resumes, therefore bridging this gap. Our program offers a contextaware and nuanced evaluation of how well a candidate's qualifications match the requirements of the position by calculating the similarity scores between these embeddings. Furthermore, the incorporation of Generative Pre-trained Transformer (GPT) models provides customized suggestions for resume optimization, emphasizing areas of improvement indicated by the BERT study. Additionally, the application also has a job search tool that lets the users browse LinkedIn for suitable job posts and apply straight to them.

THE POSSIBILITIES OF USING PARALLEL PROGRAMMING USING PYTHON

This article explores the development of parallel software applications using the Python programming language. Parallel programming is becoming increasingly important in the information technology world as multi-core processors and distributed computing become more common. Python provides developers with a variety of tools and libraries for creating parallel applications, including threads, processes, and asynchronous programming. This topic covers the basics of parallel programming in Python, including the principles of thread and process management, error handling, synchronization mechanisms, and resource management. He also considers asynchronous programming using the asyncio library, which allows you to efficiently handle asynchronous tasks. In addition, this topic raises issues of optimization and profiling of parallel applications, as well as explores distributed parallel programming using third-party libraries and frameworks. He also emphasizes the importance of testing and debugging in the context of parallel programming. Research and experiments in parallel programming using Python help developers create high-performance and efficient applications that can effectively use multi-core systems and distributed computing. This article offers an in-depth study that examines how Python is suitable for teaching parallel programming to inexperienced students. The results show that there are obstacles that prevent Python from maintaining its advantages in the transition from sequential programming to parallel.

Optimizing Arterial Vessel Contrast in Portal Venous Phase with Virtual Monoenergetic Images from Photon-Counting Detector CT Scans of the Abdomen-First Clinical Experiences.

Photon-counting detector (PCD) computed tomography (CT) allows for the reconstruction of virtual monoenergetic images (VMI) at different thresholds. The aim of our study was to evaluate the optimal arterial contrast in portal venous (pv) scans regarding objective parameters and subjective image quality for different virtual keV levels. We identified 40 patients that underwent a CT scan with an arterial and pv phase on a PCD-CT (NAEOTOM alpha, Siemens Healthineers, Forchheim, Germany). The attenuation of abdominal arteries on pv phases was measured for different virtual keV levels in a monoenergetic+ application profile and for polychromatic (pc) arterial images. Two independent readers assessed subjective image quality, including vascular contrast in pv scans at different energy levels. Additionally, signal- and contrast-to-noise ratios (SNR and CNR) were measured. Our results showed increasing arterial attenuation levels with decreasing energy levels in virtual monoenergetic imaging on pv scans with the highest attenuation at 40 keV, significantly higher than in the pc arterial phase (439 ± 97 HU vs. 360 ± 97, p < 0.001). Noise, SNR, and CNR were worse at this energy level (p < 0.001). Pv VMI showed less noise at energy levels above 70 keV (all p < 0.001). Subjective image quality was rated best at 70 keV, vascular contrast was best at 40 keV. Our research suggests that virtual monoenergetic images at 40 keV in Mono+ mode derived from a PCD-CT can be a feasible alternative to a true arterial phase for assessment of vessels with worse CNR and SNR.

Chemical composition and antimicrobial activity of the essential oils in different populations of Coriandrum sativum L. (coriander) from Iran and Iraq.

Coriander (Coriandrum sativum L.) is an annual herb belonging to the Apiaceae family that is grown worldwide. This aromatic herb has been used for its nutritional value and biological properties. In this study, we compared the essential oil composition and antibacterial activity of coriander seeds from nine Iranian and Iraqi populations for the first time. The seed oils were extracted using a Clevenger-type apparatus, and their chemical composition was determined using GC and GC/MS Agilent apparatuses. The antimicrobial activity of the oils was tested against three infectious bacteria (Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa) using the agar well diffusion method. The experiments were repeated three times, and the results were analyzed using PAST, SAS, and SPSS software. The results showed that oxygenated monoterpenes, especially linalool, were the major compounds in the oils, followed by α-pinene, γ-terpinene, and geranyl acetate. The proportions of these compounds varied among the populations. Trace amounts of other compounds were also detected, some of which were only found in certain populations. The populations were detected as linalool chemotype, and classified into four groups based on their chemical constituents in the UPGMA tree. The PCA-Biplot showed that these groups were characterized by the presence and percentage of specific compounds. The essential oils showed bacterial growth inhibitory properties only at 100% concentration. S. aureus was the most sensitive bacterium to the coriander essential oil, while the essential oils of all populations inhibited the growth of this bacterium. Additionally, the essential oils were more effective than antibiotics against E. coli. These findings contribute to our understanding of coriander seed essential oil by providing data on antibacterial activity and chemical characteristics. Furthermore, the study highlights the importance of selecting populations based on their specific essential oil profiles for antibacterial applications.

Metadata application profile as a mechanism for semantic interoperability in FAIR and open data publishing

Application profiles, also known as metadata application profiles, are customised collections of vocabularies adapted from various namespaces and tailored for specific local applications. These profiles act as constrainers and explainers for the (meta)data. Semantic interoperability is the ability of computer systems to exchange data in a mutually understandable manner, facilitating data sharing across diverse platforms and applications without compromising its meaning. As a critical component of semantic interoperability, application profiles enforce semantics to (meta)data, enhancing its openness, interoperability, and reusability. This study assesses the feasibility of representing a comprehensive application profile in a format aligned with the semantic web, ensuring interoperability between profiles and datasets. Dublin Core Description Set Profiles (DSP) is adapted as the modeling framework for metadata application profiles, steering the associated datasets toward RDF compliance. The research outcomes include “Yet Another Metadata Application Profiles” (YAMA) as a preprocessor grounded in the DSP framework for developing and managing metadata application profiles. YAMA facilitates the generation of various standard formats of application profiles, ensuring they are represented in human-readable documentation, machine-actionable forms, and even data validation languages. A data mapping extension to YAMA is proposed to ensure the semantic interoperability of open data, bridging non-RDF data structures to RDF, thus enabling the publication of 5-star open data. This ensures smooth dataset integration and the creation of linkable, semantically rich open datasets. The work emphasizes the pivotal role of application profiles in fortifying the semantic interoperability of (meta)data, thereby elevating dataset openness.

Dual-Polarized Phased Array With Ultralow Profile for Wide-Angle Scanning Application

Dual-Polarized Phased Array With Ultralow Profile for Wide-Angle Scanning Application

Two-Dimensional CuMn-Layered Double Hydroxides: A Study of Interlayer Anion Variants on the Electrochemical Sensing of Trichlorophenol.

Despite their diverse application profile, aromatic organochlorides such as 2,4,6-trichlorophenol (TP) are widely renowned for creating a negative toll on the balance of the ecosystem. Strict regulatory regimes are required to limit exposure to such organic pollutants. By deployment of a straightforward detection scheme, electrochemical sensing technology offers a competitive edge over the other techniques and practices available for pollutant monitoring. Here, we present a streamlined hydrothermal approach for synthesizing copper-manganese layered double hydroxide (CuMn-LDH) rods to be employed as electrocatalysts for detecting TP in various media. With a focused intention to leverage the full potential of the prepared CuMn-LDHs, the interlamellar region is configured using a series of intercalants. Further, a thorough comparative analysis of their structures, morphologies, and electrochemical performance is accomplished using various analytical techniques. The electrocatalytic oxidation ability of the CuMn-LDH toward TP molecules is markedly altered by incorporating various anions into the gallery region. The dynamic attributes of the developed sensor, such as a wide linear response (0.02-289.2 μM), a low detection limit (0.0026 μM), and good anti-interfering ability, acclaim its superior viability for real-time detection of TP with exceptional tolerance to the presence of foreign moieties. Hence, this work manifests that the nature of intercalants is a vital aspect to consider while designing LDH-based electrochemical probes to detect priority pollutants.

Design and Analysis of UAV Profile for Agriculture and Surveying Application

This study represents the aerodynamic design of an Unmanned aerial vehicle intended for surveillance or agriculture with a maximum take weight of 125 kg. Weight estimation and constraint analysis were done based on the Mission profile. Design of Computer-Aided Design (CAD) models were generated for three different configurations using CATIA V5R20 as a high wing, mid-wing, and low wing. Flow analysis was done for the above configurations at various angles of attack. ANSYS 15 was used for the flow Analysis. A Tetrahedron element meshed the model with the minimum required orthogonal quality. Five microns were given to the initial layer height of the prism mesh. Spalart Allmaras model is used as the Turbulence model in the solver. The aerodynamic characteristics of the above configuration obtained from Computational Fluid Dynamics (CFD) results were compared with the DATCOM program and validated with the wind tunnel experimental test data. The open-circuited suction-type Subsonic wind tunnel was employed for the test. The aerodynamic properties for the angle of attack in the range of -2° to 14° angle of attack are calculated using a six-component balance. The study aims to find the Unmanned Aerial Vehicle (UAV) configuration based on the aerodynamic characteristics obtained from the CFD and DATCOM results. High-wing UAVs have better aerodynamic efficiency than the other two configurations.

Repeated Aqueous Film-Forming Foams Applications: Impacts on Polyfluoroalkyl Substances Retention in Saturated Soil.

Historical practices at firefighter-training areas involved repeated aqueous film-forming foams (AFFFs) applications, resulting in source zones characterized by high concentrations of perfluoroalkyl and polyfluoroalkyl substances (PFAS). Repeated applications of AFFF composed of 14 anionic and 23 zwitterionic perfluoroalkyl substances (PFAS) were conducted on a single one-dimensional saturated soil column to quantify PFAS retention. An electrofluorination-based (3M) Milspec AFFF, which was above the mixture's critical micelle concentration (CMC), was at application strength (3%, v/v). Retention and retardation of PFAS mass increased with each successive AFFF addition, although the PFAS concentration profiles for subsequent applications differed from the initial. Greater degree of mass retention and retardation correlated with longer PFAS carbon-fluorine chain length and charged-headgroup type and as a function of AFFF application number. Anionic PFAS were increasingly retained with each subsequent AFFF application, while zwitterionic PFAS exhibited an alternating pattern of sorption and desorption. Surfactant-surfactant adsorption and competition during repeat AFFF applications that are at concentrations above the CMC resulted in adsorbed PFAS from the first application, changing the nature of the soil surface with preferential sorption of anionic PFAS and release of zwitterionic PFAS due to competitive elution. Applying a polyparameter quantitative structure-property relationship developed to describe sorption of AFFF-derived PFAS to uncontaminated, saturated soil was attempted for our experimental conditions. The model had been derived for data where AFFF is below the apparent CMC and our experimental conditions that included the presence of mixed micelles (aggregates consisting of different kinds of surfactants that exhibit characteristics properties different from micelles composed of a single surfactant) resulted in overall PFAS mass retained by an average of 27.3% ± 2.7% (standard error) above the predicted values. The correlation was significantly improved by adding a "micelle parameter" to account for cases where the applied AFFF was above the apparent CMC. Our results highlight the importance of interactions between the AFFF components that can only be investigated by employing complex PFAS mixtures at concentrations present in actual AFFF at application strength, which are above their apparent CMC. In firefighter-training areas (AFFF source zones), competitive desorption of PFAS may result in downgradient PFAS retention when desorbed PFAS become resorbed to uncontaminated soil.

Design of new small molecules derived from indolin-2-one as potent TRKs inhibitors using a computer-aided drug design approach

Tropomyosin receptor kinase (TRKs) enzymes are responsible for cancers associated with the neurotrophic tyrosine kinase receptor gene fusion and are identified as effective targets for anticancer drug discovery. A series of small-molecule indolin-2-one derivatives showed remarkable biological activity against TRKs enzymatic activity. These small molecules could have an excellent profile for pharmaceutical application in the treatment of cancers caused by TRKs activity. The aim of this study is to modify the structure of these molecules to obtain new molecules with improved TRK inhibitory activity and pharmacokinetic properties favorable to the design of new drugs. Based on these series, we carried out a 3D-QSAR study. As a result, robust and reliable CoMFA and CoMSIA models are developed and applied to the design of 11 new molecules. These new molecules have a biological activity superior to the most active molecule in the starting series. The eleven designed molecules are screened using drug-likeness, ADMET proprieties, molecular docking, and MM-GBSA filters. The results of this screening identified the T1, T3, and T4 molecules as the best candidates for strong inhibition of TRKs enzymatic activity. In addition, molecular dynamics simulations are performed for TRK free and complexed with ligands T1, T3, and T4 to evaluate the stability of ligand-protein complexes over the simulation time. On the other hand, we proposed experimental synthesis routes for these newly designed molecules. Finally, the designed molecules T1, T2, and T3 have great potential to become reliable candidates for the conception of new drug inhibitors of TRKs. Communicated by Ramaswamy H. Sarma

Evaluation of Intestinal Permeability of the Antifungal Compound PD76: Comparison of in silico Platforms and in vitro Assay in Caco-2 Cell Model

The compound 2-hydrazinyl-4-(4-methoxyphenyl)thiazole (PD76) is a novel thiazolyl hydrazine derivative with proven antifungal activity against different fungal species, mainly Candida and Cryptococcus. Considering the advantages of oral route for clinical therapy, the aim of this work was to evaluate the potential intestinal permeability of this new antifungal drug. For the quantitation of PD76, a high-performance liquid chromatography method was developed and fully validated. The cytotoxicity of the compound in Caco-2 cells was analyzed and intestinal permeability of PD76 was assessed by means of the comparison of in vitro assay in Caco-2 cells and in silico platforms ADMETlab and admetSAR. Cell viability above 70% was obtained at all PD76 studied concentrations. Using Caco-2 cell model, the compound showed apparent permeability coefficients (Papp) of 5.25 × 10-6 and 23.28 × 10-6 cm s-1 in apical-basolateral and basolateral-apical directions, respectively. Experiments performed using verapamil as P-gp inhibitor demonstrated that PD76 is slightly susceptible to active efflux. Both in silico platforms inferred that PD76 presents permeability in Caco-2 cells, with Log P values of 2.82 (ADMETlab) and 2.10 (admetSAR). The results obtained in permeability studies showed that PD76 presents moderate intestinal permeability and a promising profile for clinical application.

Near-IR nanolignin sensitizers based on pyrene-conjugated chlorin and bacteriochlorin for ROS generation, DNA intercalation and bioimaging.

Near-infrared (NIR) fluorescent agents are extensively used for biomedical imaging due to their ability for deep tissue penetration. Tetrapyrrole-based photosensitizers are promising candidates in this regard. Further, the extended conjugation of such macromolecules with chromophores can enhance their fluorescence efficiency and DNA intercalation ability. Herein, pyrene-conjugated NIR photosensitizers, such as chlorin (PyChl) and bacteriochlorin (PyBac), were synthesized from the corresponding pyrene-porphyrin (PyP). The correlation between the theoretical and experimental optical properties (absorption and fluorescence spectroscopy results) was determined using the DFT/TD-DFT computational approach. Next, studies on the photophysical properties, reactive oxygen species (ROS) production, and DNA binding were conducted on these macrocycles to study the effect of pyrene conjugation on the pyrrolic ring. Furthermore, each photosensitizer was loaded into lignin nanoparticles (LNPs) using the solvent-antisolvent method to accomplish fluorescence-guided imaging. The developed near-IR chlorin- and bacteriochlorin-doped lignin nanocarriers (PyChl-LNCs and PyBac-LNCs) exhibited significant in vitro singlet oxygen generation upon red LED light exposure. Moreover, these macrocycle-loaded nanolignin sensitizers showed good fluorescence-guided bioimaging with fungal cells (Candida albicans). Further, the nanoprobes exhibited pH-dependent release profiles for biological applications. These nanolignin sensitizers demonstrated promising potential to be utilized in near-IR image-guided photodynamic therapy.

Exploring the Off-label Use of Botulinum Toxin in Hidradenitis Suppurativa

Hidradenitis suppurativa (HS) is a therapeutic challenge despite various treatment modalities. This review explores the potential of botulinum toxin, both type A (BTX-A) and type B (BTX-B), as an off-label intervention for hidradenitis suppurativa management. Hidradenitis suppurativa, characterized by inflammatory lesions and abscesses, often leads to chronic pain and poor quality of life. Conventional therapies offer limited sustained relief, prompting exploration into alternative treatments. Existing research, although primarily consisting of case reports and a limited randomized controlled trial, suggests potential benefits of botulinum toxin in alleviating HS symptoms. Observations include improvements in dermatology life quality index (DLQI), pain reduction, and lesion regression following botulinum toxin administration. However, these findings lack uniformity in dosing and demographic representation, necessitating further robust investigations encompassing diverse populations. While initial findings hint at the promise of botulinum toxin as a potential therapeutic avenue for HS, the current evidence base is mainly anecdotal. Large-scale controlled trials across diverse demographics are crucial to substantiate efficacy, establish standardized treatment protocols, and evaluate the safety profile for broader application in managing this challenging condition.

Theoretical study of excitation profiles of coherent anti-stokes Raman Scattering (CARS) and applications to carotenoids

Coherent anti-Stokes Raman Scattering (CARS) spectroscopy is a nonlinear optical technique that is used to investigate many different chemical and biological systems. This tool is very efficient and essentially completely rejects fluorescence. As is well known, the excitation profiles of resonance CARS are mostly used to estimate the origin shift parameters with electronic excitation for vibrations with resonantly enhanced Raman components that give information about the molecular structure and its changes in an excited electronic state. Certainly, it is necessary to have an appropriate theoretical model for pragmatically representing these processes and considering the joint (simultaneous) impact of different broadening mechanisms. The theoretical model presented in the paper is the most general and is free from a priori assumptions. Also, the mathematical algorithm created on its basis is flexible and fast for quantitative calculations.

Investigation of radiation shielding and optical properties of neodymium doped Bismuth Boro tellurite glasses for solid state device applications

In this work, a variety of glass samples with different chemical profiles for optical applications were synthesized, and characterized. The thermal properties of the prepared glass are investigated through Differential Thermal Analysis (DTA) and we have observed the endothermic peak at 894.9 °C and exothermic peak at 1066 °C. The optical properties were investigated using a UV–Visible–NIR Spectrophotometer, and the radiation shielding properties were reported using the Phy-X/PDS software. The optical property results show that when the amount of Nd3+ ions in a glass structure grows, the oscillator strengths also increase. The high emission cross-section and excellent quantum efficiency indicate that glass has the potential for compact 1.06 μm laser applications. The Photoluminescence (PL) analysis of Nd3+ ions doped prepared glass samples are recorded at 808 nm laser diode. The emission spectra for every Nd3+ ion concentration exhibit three distinct emission peaks at wavelengths of 902 nm, 1064 nm, and 1339 nm. The investigation of luminescence kinetics revealed the development of neodymium ion clusters commencing at a neodymium concentration of 1.006 × 1020cm−3. Furthermore, we noticed that the addition of Nd2O3 increased the mass attenuation coefficient and effective atomic number of the glass samples. In terms of the glasses' ability to absorb charged particles, the Nd2O3 addition also made a difference in Half Value Layer, Mean Free Path, and Effective atomic number. The research shows that the glass samples are non-toxic, visible light transparent, and radiation-efficient.