Search Results (230)

As a promising structure for fabricating inorganic—organic-based optoelectronic devices, metal—metallophthalocyanine (MPc) hybrid layers are highly important to be considered. The efficient charge injection and transport across the metal/MPc interface are strictly dependent on the precise molecular orientation of the MPcs. Therefore, the efficiency of MPc-based optoelectronic devices strictly depends on the adsorption and orientation of the organic MPc on the inorganic metal substrate. The current review aims to explore the effect of the terminated atoms or surface atoms as an internal stimulus on molecular adsorption and orientation. Here, we investigate the adsorption of five different phthalocyanine molecules—free-based phthalocyanine (H₂Pc), copper phthalocyanine (CuPc), iron phthalocyanine (FePc), cobalt phthalocyanine (CoPc), vanadyl phthalocyanine (VOPc)—on three metallic substrates: gold (Au), silver (Ag), and copper (Cu). This topic can guide new researchers to find out how molecular adsorbance and orientation determine the electronic structure by considering the surface–molecule interactions. Full article

The Material Generation Optimization (MGO) algorithm is an innovative approach inspired by material chemistry which emulates the processes of chemical compound formation and stabilization to thoroughly explore and refine the parameter space. By simulating the bonding processes—such as the formation of ionic and covalent bonds—MGO generates new solution candidates and evaluates their stability, guiding the algorithm toward convergence on optimal parameter values. To improve its search efficiency, this paper introduces an Enhanced Material Generation Optimization (IMGO) algorithm, which integrates a Quadratic Interpolated Learner Process (QILP). Unlike conventional random selection, QILP strategically selects three distinct chemical compounds, resulting in increased diversity, a more thorough exploration of the solution space, and improved resistance to local optima. The adaptable and non-linear adjustments of QILP’s quadratic function allow the algorithm to traverse complex landscapes more effectively. This innovative IMGO, along with the original MGO, is developed to support applications across three phases, showcasing its versatility and enhanced optimization capabilities. Initially, both the original and improved MGO algorithms are evaluated using several mathematical benchmarks from the CEC 2017 test suite and benchmarks to measure their optimization capabilities. Following this, both algorithms are applied to the following three well-known engineering optimization problems: the welded beam design, rolling element bearing design, and pressure vessel design. The simulation results are then compared to various established bio-inspired algorithms, including Artificial Ecosystem Optimization (AEO), Fitness–Distance-Balance AEO (FAEO), Chef-Based Optimization Algorithm (CBOA), Beluga Whale Optimization Algorithm (BWOA), Arithmetic-Trigonometric Optimization Algorithm (ATOA), and Atomic Orbital Searching Algorithm (AOSA). Moreover, MGO and IMGO are tested on a real Egyptian power distribution system to optimize the placement of PV and the capacitor units with the aim of minimizing energy losses. Lastly, the PV parameters estimation problem is successfully solved via IMGO, considering the commercial RTC France cell. Comparative studies demonstrate that the IMGO algorithm not only achieves significant energy loss reduction but also contributes to environmental sustainability by reducing emissions, showcasing its overall effectiveness in practical energy optimization applications. The IMGO algorithm improved the optimization outcomes of 23 benchmark models with an average accuracy enhancement of 65.22% and a consistency of 69.57% compared to the MGO method. Also, the application of IMGO in PV parameter estimation achieved a reduction in computational errors of 27.8% while maintaining superior optimization stability compared to alternative methods. Full article

Mangrove ecosystems have attracted widespread attention because of their high salinity, muddy or sandy soil, and low pH, as well as being partly anoxic and periodically soaked by tides. Mangrove plants, soil, or sediment-derived fungi, especially the Penicillium species, possess unique metabolic pathways to produce secondary metabolites with novel structures and potent biological activities. This paper reviews the structural diversity and biological activity of secondary metabolites isolated from mangrove ecosystem-derived Penicillium species over the past 5 years (January 2020–October 2024), and 417 natural products (including 170 new compounds, among which 32 new compounds were separated under the guidance of molecular networking and the OSMAC approach) are described. The structures were divided into six major categories, including alkaloids, polyketides, terpenoids, benzene derivatives, steroids, and other classes. Among these natural products, the plausible biosynthetic pathways of 37 compounds were also proposed; 11 compounds have novel skeleton structures, and 26 compounds contain halogen atoms. A total of 126 compounds showed biological activities, such as cytotoxic, antifungal, antibacterial, anti-inflammatory, and α-glucosidase-inhibitory activities, and 11 compounds exhibited diverse biological activities. These new secondary metabolites with novel structures and potent bioactivities will continue to guide the separation or synthesis of structurally novel and biologically active compounds and will offer leading compounds for the development and innovation of pharmaceuticals and pesticides. Full article

Glutathione S-transferases (GSTs) are promising pharmacological targets for developing antiparasitic agents against helminths, as they play a key role in detoxifying cytotoxic xenobiotics and managing oxidative stress. Inhibiting GST activity can compromise parasite viability. This study reports the successful identification of two selective inhibitors for the mu-class glutathione S-transferase of 25 kDa (Ts25GST) from Taenia solium, named i11 and i15, using a computationally guided approach. The workflow involved modeling and refining the 3D structure from the sequence using the AlphaFold algorithm and all-atom molecular dynamics simulations with an explicit solvent. Representative structures from these simulations and a putative binding site with low conservation relative to human GSTs, identified via the SILCS methodology, were employed for virtual screening through ensemble docking against a commercial compound library. The two compounds were found to reduce the enzyme’s activity by 50–70% under assay conditions, while showing a reduction of only 30–35% for human mu-class GSTM1, demonstrating selectivity for Ts25GST. Notable, i11 displayed competitive inhibition with CDNB, while i15 exhibited a non-competitive inhibition type. Full article

The catalyst is the pivotal component in CO₂ electroreduction systems for converting CO₂ into valuable products. Carbon-based single-atom materials (CSAMs) have emerged as promising catalyst candidates due to their low cost and high atomic utilization efficiency. The rational design of the morphology and microstructure of such materials is desirable but poses a challenge. Here, we employed different Mg(OH)₂ templates to guide the fabrication of two kinds of amorphous nitrogen-doped carbon nanosheet-supported Fe single atoms (FeSNC) with rough and flat surface structures. In comparison to flat FeSNC with saturated FeN₄ sites, the rough FeSNC (R-FeSNC) exhibited unsaturated FeN_4−x sites and contracted Fe-N bond length. The featured structure endowed R-FeSNC with superior capacity of catalyzing CO₂ reduction reaction, achieving an exceptional CO selectivity with Faradaic efficiency of 93% at a potential of −0.66 V vs. RHE. This study offers valuable insights into the design of CSAMs and provides a perspective for gaining a deeper understanding of their activity origins. Full article

Exopolysaccharides (EPSs) are large-molecular-weight, complex carbohydrate molecules and extracellularly secreted bio-polymers released by many microorganisms, including lactic acid bacteria (LAB). LAB are well known for their ability to produce a wide range of EPSs, which has received major attention. LAB-EPSs have the potential to improve health, and their applications are in the food and pharmaceutical industries. Several methods have been developed and optimized in recent years for producing, extracting, purifying, and characterizing LAB-produced EPSs. The simplest method of evaluating the production of EPSs is to observe morphological features, such as ropy and mucoid appearances of colonies. Ethanol precipitation is widely used to extract the EPSs from the cell-free supernatant and is generally purified using dialysis. The most commonly used method to quantify the carbohydrate content is phenol–sulfuric acid. The structural characteristics of EPSs are identified via Fourier transform infrared, nuclear magnetic resonance, and X-ray diffraction spectroscopy. The molecular weight and composition of monosaccharides are determined through size-exclusion chromatography, thin-layer chromatography, gas chromatography, and high-performance liquid chromatography. The surface morphology of EPSs is observed via scanning electron microscopy and atomic force microscopy, whereas thermal characteristics are determined through thermogravimetry analysis, derivative thermogravimetry, and differential scanning calorimetry. In the present review, we discuss the different existing methods used for the detailed study of LAB-produced EPSs, which provide a comprehensive guide on LAB-EPS preparation, critically evaluating methods, addressing knowledge gaps and key challenges, and offering solutions to enhance reproducibility, scalability, and support for both research and industrial applications. Full article

This study employs Density Functional Theory (DFT) calculations and traditional all-atom Molecular Dynamics (MD) simulations to reveal atomistic insights into a task-specific Deep Eutectic Solvent (DES) supported by graphene oxide with the aim of mimicking its application in the natural gas desulfurization process. The DES, composed of N,N,N′,N′-tetramthyl-1,6-hexane diamine acetate (TMHDAAc) and methyldiethanolamine (MDEA) supported by graphene oxide, demonstrates improved efficiency in removing hydrogen sulfide from methane. Optimized structure and HOMO-LUMO orbital analyses reveal the distinct spatial arrangements and interactions between hydrogen sulfide, methane, and DES components, highlighting the efficacy of the DES in facilitating the separation of hydrogen sulfide from methane through DFT calculations. The radial distribution function (RDF) and interaction energies, as determined by traditional all-atom MD simulations, provide insights into the specificity and strength of the interactions between the DES components supported by graphene oxide and hydrogen sulfide. Importantly, the stability of the DES structure supported by graphene oxide is maintained after mixing with the fuel, ensuring its robustness and suitability for prolonged desulfurization processes, as evidenced by traditional all-atom MD simulation results. These findings offer crucial insights into the molecular-level mechanisms underlying the desulfurization of natural gas, guiding the design and optimization of task-specific DESs supported by graphene oxide for sustainable and efficient natural gas purification. Full article

Background: Selenium (Se) is an essential trace element for maintaining human health, with significant antioxidant and immunoregulatory functions. Inadequate Se intake may be associated with Keshan disease, Kashin–Beck disease, and hypothyroidism. However, effective indicators for scientifically guiding Se supplementation in Se-deficient populations are still lacking. Objectives: This study aims to explore the dynamic distribution of Se across various nutritional biomarkers and major organs in rats through a Se supplementation experiment, as well as the pairwise correlations between them, in order to identify reliable nutritional indicators for evaluating Se levels in the body. Methods: Se levels in hair, blood, and major tissues and organs were determined by atomic fluorescence spectrometry, and glutathione peroxidase (GSH-Px) levels were measured using an ELISA. Results: Se supplementation significantly increased Se levels in rat blood, hair, and major organs, as well as GSH-Px levels in blood. Se primarily accumulated in the liver and kidneys, followed by myocardium, spleen, and muscles. Serum and plasma Se were found to be the best indicators of short-term Se intake, while erythrocyte Se levels showed a stronger correlation with Se levels in tissues and organs, making it a better marker for assessing long-term Se nutritional status compared to hair Se. Conclusions: This study demonstrates the potential of erythrocyte Se levels as an indicator for evaluating long-term Se nutritional status, providing scientific evidence for Se nutritional assessments. Full article

Two-dimensional transition metal borides (MBenes) have emerged as promising electrocatalysts for hydrogen evolution reactions (HERs), attracting significant research interest due to theoretical computations that enhance the understanding and optimization of their performance. This review begins with a comprehensive summary of HER mechanisms, followed by an in-depth examination of the geometric and electronic properties of MBenes. Subsequently, this review explores MBene-based electrocatalysts for HERs, employing free-energy diagrams and an electronic structure analysis to assess both the intrinsic catalytic activity of MBenes and the theoretical performance of single-atom modified MBenes. Finally, the prospects and challenges associated with MBenes are discussed, providing valuable insights to guide future research in this area. Overall, this topic holds significant relevance for researchers in the HER field, and this review aims to deliver theoretical insights for the optimal design of advanced MBene electrocatalysts. Full article

The unique properties of graphene have attracted the interest of researchers from various fields, and the discovery of graphene has sparked a revolution in materials science, specifically in the field of two-dimensional materials. However, graphene synthesis’s costly and complex process significantly impairs researchers’ endeavors to explore its properties and structure experimentally. Molecular dynamics simulation is a well-established and useful tool for investigating graphene’s atomic structure and dynamic behavior at the nanoscale without requiring expensive and complex experiments. The accuracy of the molecular dynamics simulation depends on the potential functions. This work assesses the performance of various potential functions available for graphene in mechanical properties prediction. The following two cases are considered: pristine graphene and pre-cracked graphene. The most popular fifteen potentials have been assessed. Our results suggest that diverse potentials are suitable for various applications. REBO and Tersoff potentials are the best for simulating monolayer pristine graphene, and the MEAM and the AIREBO-m potentials are recommended for those with crack defects because of their respective utilization of the electron density and inclusion of the long-range interaction. We recommend the AIREBO-m potential for a general case of classical molecular dynamics study. This work might help to guide the selection of potentials for graphene simulations and the development of further advanced interatomic potentials. Full article

The advancement of solid-state hydrogen storage materials is critical for the realization of a sustainable hydrogen economy. This comprehensive review elucidates the state-of-the-art characterization techniques employed in solid-state hydrogen storage research, emphasizing their principles, advantages, limitations, and synergistic applications. We critically analyze conventional methods such as the Sieverts technique, gravimetric analysis, and secondary ion mass spectrometry (SIMS), alongside composite and structure approaches including Raman spectroscopy, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and atomic force microscopy (AFM). This review highlights the crucial role of in situ and operando characterization in unraveling the complex mechanisms of hydrogen sorption and desorption. We address the challenges associated with characterizing metal-based solid-state hydrogen storage materials discussing innovative strategies to overcome these obstacles. Furthermore, we explore the integration of advanced computational modeling and data-driven approaches with experimental techniques to enhance our understanding of hydrogen–material interactions at the atomic and molecular levels. This paper also provides a critical assessment of the practical considerations in characterization, including equipment accessibility, sample preparation protocols, and cost-effectiveness. By synthesizing recent advancements and identifying key research directions, this review aims to guide future efforts in the development and optimization of high-performance solid-state hydrogen storage materials, ultimately contributing to the broader goal of sustainable energy systems. Full article

Mg metal vascular stents not only have good mechanical support properties but also can be entirely absorbed by the human body as a trace element beneficial to the human body, but because Mg metal is quickly dissolved and absorbed in the human body, magnesium metal alone cannot be ideally used as a vascular stent. Since the dense oxide Zn film formed by Zn contact with oxygen in the air has good anti-corrosion performance, the Zn nanolayer film deposited on the Mg matrix vascular scaffold by magnetron sputtering can effectively inhibit the rapid dissolution of Mg metal. However, there are few studies on the molecular dynamic structural defects of Mg-matrix Zn atomic magnetron sputtering, and the atomic level simulation of Mg-matrix Zn thin-film depositions can comprehensively understand the atomic level structural defects in the deposition process of Zn thin films from a theoretical perspective to further guide experimental research. Based on this, this research first studied and analyzed the atomic layer structure defects, surface morphology, surface roughness, atomic density of different deposited layers, radial distribution function, and residual stress of the thin-film deposition layer of 1500 deposited Zn atoms at the initial deposition stage, during and after deposition under Mg-matrix thermal layer 500K and a deposited velocity 5 Å/ps by molecular dynamics. At the same time, the effects of temperature and deposited velocity of the Mg-matrix thermal layer on the surface morphology, roughness, and biaxial stress of the deposited films were studied. Full article

Colloidal quantum dots (CQDs) are valuable for their potential applications in optoelectronic devices. However, they are susceptible to thermal degradation during processing and while in use. Mitigating thermally induced sintering, which leads to absorption spectrum broadening and undesirable changes to thin film electrical properties, is necessary for the reliable design and manufacture of CQD-based optoelectronics. Here, low-temperature metal–oxide atomic layer deposition (ALD) was investigated as a method for mitigating sintering while preserving the optoelectronic properties of mercury telluride (HgTe) CQD films. ALD-coated films are subjected to temperatures up to 160 °C for up to 5 h and alumina (Al₂O₃) is found to be most effective at preserving the optical properties, demonstrating the feasibility of metal–oxide in-filling to protect against sintering. HgTe CQD film electrical properties were investigated before and after alumina ALD in-filling, which was found to increase the p-type doping and hole mobility of the films. The magnitude of these effects depended on the conditions used to prepare the HgTe CQDs. With further investigation into the interaction effects of CQD and ALD process factors, these results may be used to guide the design of CQD–ALD materials for their practical integration into useful optoelectronic devices. Full article

Hydrogen storage materials play a pivotal role in the development of a sustainable hydrogen economy. However, the discovery and optimization of high-performance storage materials remain a significant challenge due to the complex interplay of structural, thermodynamic and kinetic factors. Computational materials science has emerged as a powerful tool to accelerate the design and development of novel hydrogen storage materials by providing atomic-level insights into the storage mechanisms and guiding experimental efforts. In this comprehensive review, we discuss the recent advances in crystal structure prediction and performance assessment of hydrogen storage materials from a computational perspective. We highlight the applications of state-of-the-art computational methods, including density functional theory (DFT), molecular dynamics (MD) simulations, and machine learning (ML) techniques, in screening, evaluating, and optimizing storage materials. Special emphasis is placed on the prediction of stable crystal structures, assessment of thermodynamic and kinetic properties, and high-throughput screening of material space. Furthermore, we discuss the importance of multiscale modeling approaches that bridge different length and time scales, providing a holistic understanding of the storage processes. The synergistic integration of computational and experimental studies is also highlighted, with a focus on experimental validation and collaborative material discovery. Finally, we present an outlook on the future directions of computationally driven materials design for hydrogen storage applications, discussing the challenges, opportunities, and strategies for accelerating the development of high-performance storage materials. This review aims to provide a comprehensive and up-to-date account of the field, stimulating further research efforts to leverage computational methods to unlock the full potential of hydrogen storage materials. Full article

Activity recognition combined with artificial intelligence is a vital area of research, ranging across diverse domains, from sports and healthcare to smart homes. In the industrial domain, and the manual assembly lines, the emphasis shifts to human–machine interaction and thus to human activity recognition (HAR) within complex operational environments. Developing models and methods that can reliably and efficiently identify human activities, traditionally just categorized as either simple or complex activities, remains a key challenge in the field. Limitations of the existing methods and approaches include their inability to consider the contextual complexities associated with the performed activities. Our approach to address this challenge is to create different levels of activity abstractions, which allow for a more nuanced comprehension of activities and define their underlying patterns. Specifically, we propose a new hierarchical taxonomy for human activity abstraction levels based on the context of the performed activities that can be used in HAR. The proposed hierarchy consists of five levels, namely atomic, micro, meso, macro, and mega. We compare this taxonomy with other approaches that divide activities into simple and complex categories as well as other similar classification schemes and provide real-world examples in different applications to demonstrate its efficacy. Regarding advanced technologies like artificial intelligence, our study aims to guide and optimize industrial assembly procedures, particularly in uncontrolled non-laboratory environments, by shaping workflows to enable structured data analysis and highlighting correlations across various levels throughout the assembly progression. In addition, it establishes effective communication and shared understanding between researchers and industry professionals while also providing them with the essential resources to facilitate the development of systems, sensors, and algorithms for custom industrial use cases that adapt to the level of abstraction. Full article