Range Of Applications Research Articles

Exosome Isolation Using Chitosan Oligosaccharide Lactate-1-Pyrenecarboxylic Acid-Based Self-Assembled Magnetic Nanoclusters.

Exosomes are small extracellular vesicles that play a crucial role in intercellular communication and offer significant potential for a wide range of biomedical applications. However, conventional methods for exosome isolation have limitations in terms of purity, scalability, and preservation of exosome structural integrity. To address these challenges, an exosome isolation platform using chitosan oligosaccharide lactate conjugated 1-pyrenecarboxylic acid (COL-Py) based self-assembled magnetic nanoclusters (CMNCs), is presented. CMNCs are characterized to optimize their size, stability, and interaction dynamics with exosomes. The efficiency of CMNCs in isolating exosomes is systematically evaluated using various analytical methods to demonstrate their ability to capture exosomes based on amphiphilic lipid bilayers. CMNC-based exosome isolation consistently yields exosomes with structural integrity and purity similar to those obtained using traditional methods. The reusability of CMNCs over multiple exosome isolation cycles underscores their scalability and offers an efficient solution for biomedical applications. These results are supported by western blot analysis, which demonstrated the superiority of CMNC-based isolation in terms of purity compared to conventional methods. By providing a scalable and efficient exosome isolation process that preserves both structural integrity and purity, CMNCs can constitute a new platform that can contribute to the field of exosome studies.

Performance simulation of the perovskite solar cells with Ti3C2 MXene in the SnO2 electron transport layer

MXenes, a class of two-dimensional (2D) transition metal carbides and nitrides, have a wide range of potential applications due to their unique electronic, optical, plasmonic, and other properties. SnO2–Ti3C2 MXene with different contents of Ti3C2 (0.5, 1.0, 2.0, 2.5 wt‰), experimentally, has been used as electron transport layers (ETLs) in Perovskite Solar Cells (PSCs). The SCAPS-1D simulation software could simulate a perovskite solar cell comprised of CH3NH3PbI3 absorber and SnO2 (or SnO2–Ti3C2) ETL. The simulation results like Power Conversion Efficiency (PCE), Open circuit voltage (VOC), Short circuit current density (JSC), Fill Factor (FF), and External Quantum Efficiency (EQE) have been compared within samples with different weight percentages of Ti3C2 MXene incorporated in ETL. Reportedly, the ETL of SnO2 with Ti3C2 (1.0 wt‰) effectively increases PCE from 17.32 to 18.32%. We simulate the role of MXene in changing the ideality factor (nid), photocurrent (JPh), built-in potential (Vbi), and recombination resistance (Rrec). The study of interface recombination currents and electric field shows that cells with 1.0 wt‰ of MXene in SnO2 ETL have higher values of ideality factor, built-in potential, and recombination resistance. The correlation between these values and cell performance allows one to conclude the best cell performance for the sample with 1.0 wt‰ of MXene in SnO2 ETL. With an optimization procedure for this cell, an efficiency of 27.81% is reachable.

Anisotropic Morphing in Bistable Kirigami through Symmetry Breaking and Geometric Frustration.

Shape morphing in bistable kirigami enables remarkable functionalities appealing to a diverse range of applications across the spectrum of length scale. At the core of their shape shifting lies the architecture of their repeating unit, where highly deformable slits and quasi-rigid rotating units often exhibit multiple symmetries that confer isotropic deployment obeying uniform scaling transformation. In this work, symmetry breaking in bistable kirigami is investigated to access geometric frustration and anisotropic morphing, enabling arbitrarily scaled deployment in planar and spatial bistable domains. With an analysis on their symmetry properties complemented by a systematic investigation integrating semi-analytical derivations, numerical simulations, and experiments on elastic kirigami sheets, this work unveils the fundamental relations between slit symmetry, geometric frustration, and anisotropic bistable deployment. Furthermore, asymmetric kirigami units are leveraged in planar and flat-to-3D demonstrations to showcase the pivotal role of shear deformation in achieving target shapes and functions so far unattainable with uniformly stretchable kirigami. The insights provided in this work unveil the role of slit symmetry breaking in controlling the anisotropic bistable deployment of soft kirigami metamaterials, enriching the range of achievable functionalities for applications spanning deployable space structures, wearable technologies, and soft machines.

Coarse-Grained Crystal Graph Neural Networks for Reticular Materials Design.

Reticular materials, including metal-organic frameworks and covalent organic frameworks, combine the relative ease of synthesis and an impressive range of applications in various fields from gas storage to biomedicine. Diverse properties arise from the variation of building units─metal centers and organic linkers─in almost infinite chemical space. Such variation substantially complicates the experimental design and promotes the use of computational methods. In particular, the most successful artificial intelligence algorithms for predicting the properties of reticular materials are atomic-level graph neural networks, which optionally incorporate domain knowledge. Nonetheless, the data-driven inverse design involving these models suffers from the incorporation of irrelevant and redundant features such as a full atomistic graph and network topology. In this study, we propose a new way of representing materials, aiming to overcome the limitations of existing methods; the message passing is performed on a coarse-grained crystal graph that comprises molecular building units. To highlight the merits of our approach, we assessed the predictive performance and energy efficiency of neural networks built on different materials representations, including composition-based and crystal-structure-aware models. Coarse-grained crystal graph neural networks showed decent accuracy at low computational costs, making them a valuable alternative to omnipresent atomic-level algorithms. Moreover, the presented models can be successfully integrated into an inverse materials design pipeline as estimators of the objective function. Overall, the coarse-grained crystal graph framework is aimed at challenging the prevailing atom-centric perspective on reticular materials design.

An improved optical length research on the physical boundary of particle-dispersed fuel

The Volumetric Homogenization Method (VHM) is one of the most commonly used methods for neutronics calculation in reactors or components. However, the use of VHM for the Double-Heterogeneous (DH) System may lead to a large deviation in reactivity calculation. The deviation of DH System using the VHM can be expressed by the theoretically modified optical length. When the theoretically modified optical length is less than 10−4, the deviation caused by the VHM will be less than 100 pcm. This paper points out that the existing theoretically modified optical length for some cases of DH systems is not accurate, and this method is only for pin cell. In this paper, by calculating a series of DH models and their corresponding VHM models, it is found that the water-uranium ratio and the shape of fuel region seriously affects the reactivity calculation deviation of particle-dispersed fuel, the original modified optical length does not take this into account, resulting in unacceptable errors. On this basis, the application of optical length to physical boundaries for DH system is further discussed and the shape correction factor is taken into consideration. Therefore, an improved optical length is proposed, which greatly expands the range of application of the physical boundary judgment methods for the double heterogeneity of dispersed particle fuel. The numerical results show that the accuracy of the improved optical length in defining the physical boundary of double heterogeneous system is higher than the theoretically modified optical length.

Two-Photon Absorbing Dendrimers and Their Properties-An Overview.

This review describes the two-photon absorption properties of dendrimers, which are arborescent three-dimensional macromolecules differing from polymers by their perfectly defined structure. The two-photon absorption process is a third order non-linear optical property that is attractive because it can be used in a wide range of applications. In this review, dendrimers that were studied for their two-photon absorption properties are first described. Then, the use of dendritic TPA chromophores for light harvesting, photopolymerization, optical power limitation, cell imaging, singlet oxygen generation, and photodynamic therapy is described. This review thus proposes an overview of the properties and possible applications of two-photon absorbing dendrimers.

Current progress and future perspectives in total‐body positron emission tomography/computed tomography. Part II: Clinical applications

AbstractTotal‐body positron emission tomography (TB‐PET) has significantly advanced from initial conception to global commercial availability. The high sensitivity of TB‐PET has led to superior lesion detection, thereby expanding the range of clinical applications. TB‐PET technology offers several advantages: (a) It enables the detection of small lesions, facilitating precise cancer staging and targeted cancer formulation. (b) The technology shortens the acquisition time while maintaining the quality of diagnostic images. (c) TB‐PET allows for a reduction in the amount of administered radiotracer, which minimizes image noise, reduces the effective radiation dose to patients, and enhances staff safety. (d) The scanner supports the development of new tracers and the dynamic imaging of these tracers throughout the entire body. (e) TB‐PET accommodates delayed scanning, which has been shown to improve the detection of small and previously undetected malignant lesions by enhancing the clearance in areas of significant background activity. (f) Owing to its high‐quality images, TB‐PET is suitable for parametric imaging, which offers several advantages over conventional standardized uptake value imaging. However, TB‐PET still faces several challenges. There is a lack of consensus on the optimal dose and scan duration for clinical diagnosis using TB‐PET. Additionally, unified standards for parametric imaging via TB‐PET are yet to be established, and the full clinical significance of this technology remains under‐explored. The accompanying review (Part 1) covers TB‐PET data manipulation and analysis.

Microstructure of dendrimers functionalized with folic acid as studied by molecular dynamics simulations

Poly(amidoamine) dendrimers (PAMAM), due to their unique properties such as nanometric size and shape, monodispersity, and numerous modifiable surface groups, are promising nanocarriers in a wide range of biomedical applications. However, amine-terminated PAMAM dendrimers have been proven to be toxic at physiological pH, which limits their biomedical application. It has been approved that the binding of folic acid (FA) to dendrimer reduces nanocarrier toxicity. Also, it can selectively target cancer cells with folate receptors (FR-α) overexpression. In this study, we have explored the structural features of FA-functionalized PAMAM dendrimers using all-atom molecular dynamics (MD) simulations. To this end, third and fourth generations of PAMAM dendrimers were functionalized with FA, and MD simulations were performed for 100 ns at 310 K and 1 bar at neutral pH. We have examined the effect of FA functionalization on the molecular size, solvent accessible surface area, structural changes of amidoamine monomers, and the solvation of investigated dendrimers. Our results reveal that G4FA is the most suitable drug delivery system for cancer therapy among others.

Amino-Acid-Encoded Bioinspired Supramolecular Self-Assembly of Multimorphological Nanocarriers.

Supramolecular self-assembly has emerged as an efficient tool to construct well-organized nanostructures for biomedical applications by small organic molecules. However, the physicochemical properties of self-assembled nanoarchitectures are greatly influenced by their morphologies, mechanical properties, and working mechanisms, making it challenging to design and screen ideal building blocks. Herein, using a biocompatible firefly-sourced click reaction between the cyano group of 2-cyano-benzothiazole (CBT) and the 1,2-aminothiol group of cysteine (Cys), an amino-acid-encoded supramolecular self-assembly platform Cys(SEt)-X-CBT (X represents any amino acid) is developed to incorporate both covalent and noncovalent interactions for building diverse morphologies of nanostructures with bioinspired response mechanism, providing a convenient and rapid strategy to construct site-specific nanocarriers for drug delivery, cell imaging, and enzyme encapsulation. Additionally, it is worth noting that the biodegradation of Cys(SEt)-X-CBT generated nanocarriers can be easily tracked via bioluminescence imaging. By caging either the thiol or amino groups in Cys with other stimulus-responsive sites and modifying X with probes or drugs, a variety of multi-morphological and multifunctional nanomedicines can be readily prepared for a wide range of biomedical applications.

Investigation of morphology and volume phase transition of supramacromolecular microgels by NMR spectroscopy and quartz crystal microbalance

AbstractMicrogels have a wide range of applications, from catalysis to biomedical applications such as drug delivery in dispersion, or as surface‐attached materials to improve the biocompatibility of implants or as anti‐fouling coatings. The first aim of this work is to understand the morphology of the supramacromolecular, tannic acid (TA) crosslinked microgels using two‐dimensional 1H NMR spectroscopy, and relaxometry. The second aim is to study the volume phase transition (VPT) of TA‐crosslinked microgels with different crosslinker densities in dispersion using NMR. In this context, 1H high‐resolution NMR spectroscopy is used in combination with the two‐state model and the Boltzmann sigmoidal function to determine thermodynamic quantities of the VPT, such as temperature (Tt), width of transition (ΔTt), and change in entropy (ΔS). Third, surface‐deposited microgels are characterized in terms of their VPT using a quartz crystal microbalance (QCM‐D) to study the properties of these microgels for applications where they are used as coatings. Finally, the Tt of microgels supramolecular crosslinked with TA are compared with microgels covalently crosslinked with N,N′‐methylenebisacrylamide (BIS) in dispersion, using NMR, and deposited on a surface, using QCM‐D. For this purpose temperature‐responsive poly(N‐vinylcaprolactam) and poly(N‐isopropylacrylamide) microgels crosslinked with the plant‐derived polyphenol TA or BIS are used.

BIOSURFACTANTS: SUSTAINABLE ALTERNATIVE TO SYNTHETIC SURFACTANTS AND THEIR APPLICATIONS

Biosurfactants are surface active agents produced by microorganisms, which help reduce surface or interfacial tension between two immiscible liquids like oil and water. In recent years, Due to their environmentally friendly nature and wide range of applications in various industries, they can act as a sustainable alternative to synthetic surfactants. This review article provides an overview of biosurfactants, emphasizing their need for biosurfactants, the production process, and their classification based on molecular weight, charge, and the microorganism they derived. The advantages include biodegradability, biocompatibility, low toxicity, surface activity, and specificity, and various areas where the biosurfactant used are emulsification, thermal stability, pH stability, wetting ability, foaming ability, and spreadability. Research on using biosurfactants in various formulations like nanoparticles, liposomes, transdermal application, nanoemulsion, and nanocapsules is also highlighted in this review to support its application in the medical field. Biosurfactants are also utilized in various fields like the pharmaceuticals, cosmetics, food, and oil industries. However, they have their drawbacks, which include high production costs, variability in production yield, sensitivity to the environment, lack of standardization, hurdles in regulatory approval, and research and development limitations. Despite certain drawbacks, biosurfactant offers a sustainable alternative to synthetic surfactants.

Anomalous heat transport and universality in macroscopic diffusion models

AbstractAnomalous diffusion is ubiquitous in nature and relevant for a wide range of applications, including energy transport, especially in bio- and nano-technologies. Numerous approaches have been developed to describe it from a microscopic point of view, and recently, it has been framed within universality classes, characterized by the behaviour of the moments and auto-correlation functions of the transported quantities. It is important to investigate whether such universality applies to macroscopic models. Here, the spectrum of the moments of the solutions of the transport equations is investigated for three continuous PDE models featuring anomalous diffusion. In particular, we consider the transport described by: (i) a generalized diffusion equation with time-dependent diffusion coefficient; (ii) the Porous Medium Equation and (iii) the Telegrapher Equation. For each model, the key features of the source-type solution as well as the analytical results for the moment analysis are revisited and extended via both analytical and numerical approaches. Equivalence of the asymptotic behaviour of the corresponding heat transport is confirmed within the realm of weak anomalous diffusion.

Reliability and validity of an app-assisted tissue compliance meter in measuring tissue stiffness on a phantom model.

Most methods for soft tissue stiffness assessment require high financial resources, significant technical effort, or extensive therapist training. The PACT Sense device was developed to be used in a wide range of applications and user groups. However, to date, there are no data on its validity and reliability. The aim of this study was to investigate the validity and reliability of the PACT device. A polyurethane phantom tissue model (PTM) mimicking the mechanical properties of the fascia profunda and the erector spinae muscle was used. Stiffness measurements with PACT were conducted by two independent investigators. For construct validity, correlations were calculated between the known stiffness of the PTM and values obtained with PACT. For concurrent validity, we determined the association between the PACT values and additional measurements with the established MyotonPRO device. To estimate interrater and intrarater (two measurements with an interval of 7 days) reliability, we used the intraclass correlation coefficient (ICC). Correlation analysis (PTM/PACT) revealed very high concurrent validity (r = 0.99; p < 0.001), construct validity (PACT/MyotonPRO) was 0.87, p < 0.001. Both, interrater reliability (ICC = 0.85; p = 0.036) and intrarater reliability were good (ICC = 0.89; p < 0.001). The PACT provides valid and reliable stiffness measurements in tissue phantoms. Further studies in humans are needed to confirm its physiometric properties under in vivo conditions.

Near-Unity Light-Matter Interaction in Mid-Infrared van der Waals Metasurfaces.

Accessing mid-infrared radiation is of great importance for a range of applications, including thermal imaging, sensing, and radiative cooling. Here, we study light interaction with hexagonal boron nitride (hBN) nanocavities and reveal strong and tunable resonances across its hyperbolic transition. In addition to conventional phonon-polariton excitations, we demonstrate that the high refractive index of hexagonal boron nitride outside the Reststrahlen band allows enhanced light-matter interactions in deep subwavelength (<λ/15) nanostructures across a broad 7-8 μm range. Emergence and interplay of Fabry-Perot and Mie-like resonances are examined experimentally and theoretically. Near-unity absorption and high quality (Q ≥ 80) resonance interaction in the vicinity of the hBN transverse optical phonon is further observed. Our study provides avenues to design highly efficient and ultracompact structures for controlling mid-infrared radiation and accessing strong light-matter interactions with hBN.

An Investigation on Students Learnings &amp; Mental Health Influenced by Artificial Intelligence

Artificial intelligence has impacted student’s social, emotional, and physical well-being. It has resulted in how a person thinks and reacts to a particular situation. The Purpose of this investigation is to test the effectiveness of learning patterns on our mental health. It can affect a student’s energy level, concentration, dependability, and mental ability. On the other hand, AI can automate the process of grading assignments and providing feedback to students, which can save teachers a lot of time and can focus more on student interaction. It is essential to ensure that AI systems used in education are unbiased and fair. We read articles about the connection between artificial intelligence and mental health that use mood rating scales and classified them into the pros and cons of artificial intelligence. In our college, we will also carry out a poll that shows how Artificial intelligence has harmed and benefited the students. Therefore, it is crucial to carefully examine the benefits and risks of AI in education and implement appropriate safeguards to ensure that it is used ethically and responsibly. Public mental health based on artificial intelligence engineering has a wide range of application and development prospects in enhancing the effectiveness of instruction, achieving high efficiency and intelligence, and enhancing specialized education. The use of artificial intelligence-based public mental health in business administration teaching effects has highly favorable importance for precisely determining the mental health and teaching effects of business administration students. We develop a method for the influence of public mental health on the teaching effect of the business administration profession based on artificial intelligence engineering to improve the teaching- learning effect of the business administration major. ( Keywords:- Learnings, Mental Health, and Artificial Intelligence )

Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential.

Atomistic simulation has a broad range of applications from drug design to materials discovery. Machine learning interatomic potentials (MLIPs) have become an efficient alternative to computationally expensive ab initio simulations. For this reason, chemistry and materials science would greatly benefit from a general reactive MLIP, that is, an MLIP that is applicable to a broad range of reactive chemistry without the need for refitting. Here we develop a general reactive MLIP (ANI-1xnr) through automated sampling of condensed-phase reactions. ANI-1xnr is then applied to study five distinct systems: carbon solid-phase nucleation, graphene ring formation from acetylene, biofuel additives, combustion of methane and the spontaneous formation of glycine from early earth small molecules. In all studies, ANI-1xnr closely matches experiment (when available) and/or previous studies using traditional model chemistry methods. As such, ANI-1xnr proves to be a highly general reactive MLIP for C, H, N and O elements in the condensed phase, enabling high-throughput in silico reactive chemistry experimentation.

Gesture Recognition Based on Deep Learning: A Review

Gesture recognition is an important and inevitable technology in modern times, its appearance and improvement greatly improve the convenience of people's lives, but also enrich people's lives. It has a wide range of applications in various fields. In daily life, it can carry out human-computer interaction and the use of smart home. In terms of medical treatment, it can help patients to recover and assist doctors to carry out experiments. In terms of entertainment, it allows users to interact with the game in an immersive manner. This paper chooses three technologies that deep learning plays a more prominent role in gesture recognition, namely CNNs, LSTM and transfer learning based on deep learning. They each have their own advantages and disadvantages. Because of the different principles of use, different techniques have different roles, such as CNNs can carry out feature extraction, LSTM can deal with long time series, transfer learning can transfer what is learned from another task to this task. Select different practical technologies according to different application scenarios, and make improvements in real time in practical applications. Gesture recognition based on deep learning has the advantages of good accuracy, robustness and real-time implementation, but it also bears the disadvantages of huge economic and time costs and high hardware requirements. Despite some challenges, researchers continue to optimize and improve the technology, and believe that in the future, gesture recognition technology will be more mature and valuable.

A Simple and Efficient Method for the Nickel‐Catalyzed Synthesis of Azines from Aldehydes and Hydrazines

AbstractAzines are organic molecules with a C=N−N=C functional range of applications. But the conventional azines synthesis process requires a high reaction temperature and long reaction time combined with complex reaction facilities, so the synthesis process needs to be further improved in terms of economic efficiency and process simplification. Here, we demonstrate a structurally well‐defined inorganic ligand Ni compound, (NH4)4[NiMo6O18(OH)6](Ni‐POMs). The catalysts show good activity towards the reaction of carbonyl compounds, and the corresponding products can be obtained in yields up to about 90 %. In this condition, the majority of the aldehydes (aromatic and aliphatic) can be reacted, and the products are obtained in high yields. Density functional theory (DFT) looked into the process by which benzaldehyde and hydrazine hydrate reacted on the surface of Ni‐POMs. It was calculated that the whole reaction goes through two stages and the activation energy of the whole reaction is −6.72 kcal/mol. Thus, from experiment to calculation, a complete structure of the catalytic reaction cycle has been constructed.

Hybrid Machine Learning and Experimental Studies of Antiviral Potential of Ionic Liquids against P100, MS2, and Phi6.

Viruses are a group of widespread organisms that are often responsible for very dangerous diseases, as most of them follow a mechanism to multiply and infect their hosts as quickly as possible. Pathogen viruses also mutate regularly, with the result that measures to prevent virus transmission and recover from the disease caused are often limited. The development of new substances is very time-consuming and highly budgeted and requires the sacrifice of many living organisms. Computational chemistry methods allow faster analysis at a much lower cost and, most importantly, reduce the number of living organisms sacrificed experimentally to a minimum. Ionic liquids (ILs) are a group of chemical compounds that could potentially find a wide range of applications due to their potential virucidal activity. In our study, we conducted a complex computational analysis to predict the antiviral activity of ionic liquids against three surrogate viruses: two nonenveloped viruses, Listeria monocytogenes phage P100 and Escherichia coli phage MS2, and one enveloped virus, Pseudomonas syringae phage Phi6. Based on experimental data of toxic activity (logEC90), we assigned activity classes to 154 ILs. Prediction models were created and validated according to the Organization for Economic Co-operation and Development (OECD) recommendations using the Classification Tree method. Further, we performed an external validation of our models through virtual screening on a set of 1277 theoretically generated ionic liquids and then selected 10 active ionic liquids, which were synthesized to verify their activity against the analyzed viruses. Our study proved the effectiveness and efficiency of computational methods to predict the antiviral activity of ionic liquids. Thus, computational models are a cost-effective alternative approach compared with time-consuming experimental studies where live animals are involved.

Optimization of Installation Position for Complex Space Curve Weldments in Robotic Friction Stir Welding Based on Dynamic Dual Particle Swarm Optimization

Robotic friction stir welding (RFSW), with its wide application range, ample working space, and task flexibility, has emerged as a vital development in friction stir welding (FSW) technology. However, the low stiffness of serial industrial robots can lead to end-effector deviations and vibrations during FSW tasks, adversely affecting the weld quality. This paper proposes a dynamic dual particle swarm optimization (DDPSO) algorithm through a new comprehensive stability index that considers both the stiffness and vibration stability of the robot to optimize the installation position of complex space curve weldments, thereby enhancing the robot’s stability during the FSW process. The algorithm employs two independent particle swarms for exploration and exploitation tasks and dynamically adjusts task allocation and particle numbers based on current results to fully utilize computational resources and enhance search efficiency. Compared to the standard particle swarm optimization (PSO) algorithm, the DDPSO approach demonstrated superior search capabilities and stability of optimization results. The maximum fitness value improved by 4.2%, the average value increased by 12.74%, and the concentration level of optimization results rose by 72.91% on average. The new optimization method pioneers fresh perspectives for optimizing the stability of RFSW, providing significant grounds for the process optimization and offline programming of complex spatial curve weldments.