Search Results (1,954)

We incorporated Espaloma forcefield parameterization into MoSDeF tools for performing molecular dynamics simulations of organic molecules with HOOMD-Blue. We compared equilibrium morphologies predicted for perylene and poly-3-hexylthiophene (P3HT) with the ESP-UA forcefield in the present work against prior work using the OPLS-UA forcefield. We found that, after resolving the chemical ambiguities in molecular topologies, ESP-UA is similar to GAFF. We observed the clustering/melting phase behavior to be similar between ESP-UA and OPLS-UA, but the base energy unit of OPLS-UA was found to better connect to experimentally measured transition temperatures. Short-range ordering measured by radial distribution functions was found to be essentially identical between the two forcefields, and the long-range ordering measured by grazing incidence X-ray scattering was qualitatively similar, with ESP-UA matching experiments better than OPLS-UA. We concluded that Espaloma offers promise in the automated screening of molecules that are from more complex chemical spaces. Full article

To solve the energy crisis and environmental issues, it is essential to create effective and sustainable energy conversion and storage technologies. Traditional materials for energy conversion and storage however have several drawbacks, such as poor energy density and inadequate efficiency. The advantages of MOF-based materials, such as pristine MOFs, also known as porous coordination polymers, MOF composites, and their derivatives, over traditional materials, have been thoroughly investigated. These advantages stem from their high specific surface area, highly adjustable structure, and multifunctional nature. MOFs are promising porous materials for energy storage and conversion technologies, according to research on their many applications. Moreover, MOFs have served as sacrificial materials for the synthesis of different nanostructures for energy applications and as support substrates for metals, metal oxides, semiconductors, and complexes. One of the most intriguing characteristics of MOFs is their porosity, which permits space on the micro- and meso-scales, revealing and limiting their functions. The main goals of MOF research are to create high-porosity MOFs and develop more efficient activation techniques to preserve and access their pore space. This paper examines the porosity tunable mixed and hybrid MOF, pore architecture, physical and chemical properties of tunable MOF, pore conditions, market size of MOF, and the latest development of MOFs as precursors for the synthesis of different nanostructures and their potential uses. Full article

This study aimed to determine the chemical composition of five Lavender essential oils (LEOs) using the gas chromatography–mass spectroscopy technique and to assess their antibacterial activity against four marine Vibrio species, including Shewanella algae, Shewanella maridflavi, Vibrio harveyi, and Vibrio alginolyticus. Sensitivity tests were performed using the disk diffusion and serial dilution methods. The results showed that all five LEOs exhibited antibacterial activity against the four tested marine Vibrio species. The antibacterial activities of all five LEOs were above moderate sensitivity. The five LEOs from French blue, space blue, eye-catching, and true Lavender showed high sensitivity, particularly against Shewanella maridflavi. The compounds of LEOs from different varieties of Lavender were similar and mainly comprised linalool, linalyl acetate, eucalyptol, and isoborneol. Different varieties of LEOs possessed unique components besides common components, and the percentage of each one was different, which led to different fragrance loads. The major fragrances were lily of the valley, an aromatic compound fragrance, and an herbal fragrance. The antibacterial activity of LEO from eye-catching Lavender was better than that of others, which could provide a reference for its application in the prevention and control of marine Vibrio spp. and the development of antibacterial products. Full article

Drug discovery is inherently a multi-criteria optimization problem. In the first instance, it involves a tremendously large chemical space, where each compound can be characterized by multiple molecular and biological properties. Modern computational approaches try to efficiently explore the chemical space in search of molecules with the desired combination of properties. For example, Pareto optimizers identify a so-called “Pareto front”, a set of non-dominated solutions. From a qualitative perspective, all solutions on the front are potentially equally desirable, each expressing a trade-off between the goals. However, often there is a need to weight the objectives differently, depending on their perceived importance. To address this, we recently implemented a new Multi-Criteria Decision Analysis (MCDA) method as part of the AI-powered Drug Design (AIDD^TM) technology initiative. This allows the user to weight various objective functions differently, which, in turn, efficiently directs the generative chemistry process toward the desired areas in chemical space. Full article

The integration of a photodetector that converts optical signals into electrical signals is essential for scalable integrated lithium niobate photonics. Two-dimensional materials provide a potential high-efficiency on-chip detection capability. Here, we demonstrate an efficient on-chip photodetector based on a few layers of MoTe₂ on a thin film lithium niobate waveguide and integrate it with a microresonator operating in an optical telecommunication band. The lithium-niobate-on-insulator waveguides and micro-ring resonator are fabricated using the femtosecond laser photolithography-assisted chemical–mechanical etching method. The lithium niobate waveguide-integrated MoTe₂ presents an absorption coefficient of 72% and a transmission loss of 0.27 dB µm⁻¹ at 1550 nm. The on-chip photodetector exhibits a responsivity of 1 mA W⁻¹ at a bias voltage of 20 V, a low dark current of 1.6 nA, and a photo–dark current ratio of 10⁸ W⁻¹. Due to effective waveguide coupling and interaction with MoTe₂, the generated photocurrent is approximately 160 times higher than that of free-space light irradiation. Furthermore, we demonstrate a wavelength-selective photonic device by integrating the photodetector and micro-ring resonator with a quality factor of 10⁴ on the same chip, suggesting potential applications in the field of on-chip spectrometers and biosensors. Full article

Rosa canina is well-known plant, and its fruits have been used for centuries. The seeds have been less utilized and remain untested. The objective of this study was to examine the physico-chemical properties of rosehip seeds and to identify potential applications based on their properties. The physico-chemical properties of the seeds were determined, including swelling index (2.51), color in the CIE L * a * b * space (b * = 26.2), as well as carbohydrate (79.2 g/100 g), soluble dietary fiber (71.6 g/100 g), protein (7.67 g/100 g), fat (6.23 g/100 g), and ash (1.96 g/100 g) content. Among the amino acids, glutamic acid dominated (1.58 mg/100 g), while among the fatty acids, linoleic acid (53.19%), linolenic acid (20.24%), and oleic acid (17.63%) were the most dominant. In addition, rosehip seeds contained large amounts of calcium (3851 mg/kg), potassium (2732 mg/kg), and phosphorus (991 mg/kg), as well as vitamin C (1783 μg/g). It was found that the content of other valuable phytochemicals, such as phenolic compounds (2633 μg/g) and carotenoids (3.13 μg/g) influenced the antioxidant properties of the tested raw material. This activity examined by the ferric-reducing antioxidant power (FRAP) and DPPH radical scavenging activity methods was assessed at a level of 191 and 229 μmol TE/g, respectively. The results indicate the potential use of rosehip seed, a by-product of the food processing industry, as a new high-value ingredient for health products, such as nutraceuticals, pharmaceuticals, and cosmeceuticals. Full article

The synthesis of ethylamine-based perovskites has emerged to attempt to replace the lead in lead-based perovskites for the alkaline earth elements barium and strontium, introducing chloride halide to prepare the perovskites in solar cell technology. X-ray diffraction studies were conducted, and EXPO2014 software was utilized to resolve the structure. Chemical characterization was performed using Fourier transform infrared spectroscopy, photophysical properties were analyzed through ultraviolet–visible spectroscopy, and photoluminescence properties were determined to confirm the perovskite characteristics. The software employed can determine new crystal structures, as follows: orthorhombic for barium perovskite CH₃CH₂NH₃BaCl₃ and tetragonal for strontium perovskite CH₃CH₂NH₃SrCl₃. The ultraviolet–visible spectroscopy data demonstrated that a temperature increase (90–110 °C) contributed to reducing the band gap from 3.93 eV to 3.67 eV for barium perovskite and from 4.05 eV to 3.84 eV for strontium perovskite. The results exhibited that new materials can be obtained through gentle chemistry and specialized software like EXPO2014, both of which are capable of conducting reciprocal and direct space analyses for identifying crystal structures using powder X-ray diffraction. Full article

As an appealing approach for discovering novel leads, the key advantage of de novo drug design lies in its ability to explore a much broader dimension of chemical space, without being confined to the knowledge of existing compounds. So far, many generative models have been described in the literature, which have completely redefined the concept of de novo drug design. However, many of them lack practical value for real-world drug discovery. In this work, we have developed a graph-based generative model within a reinforcement learning framework, namely, METEOR (Molecular Exploration Through multiplE-Objective Reinforcement). The backend agent of METEOR is based on the well-established GCPN model. To ensure the overall quality of the generated molecular graphs, we implemented a set of rules to identify and exclude undesired substructures. Importantly, METEOR is designed to conduct multi-objective optimization, i.e., simultaneously optimizing binding affinity, drug-likeness, and synthetic accessibility of the generated molecules under the guidance of a special reward function. We demonstrate in a specific test case that without prior knowledge of true binders to the chosen target protein, METEOR generated molecules with superior properties compared to those in the ZINC 250k data set. In conclusion, we have demonstrated the potential of METEOR as a practical tool for generating rational drug-like molecules in the early phase of drug discovery. Full article

The rapid advancement of artificial intelligence is transforming the computer-aided design of microfluidic chips. As a key component, microfluidic mixers are widely used in bioengineering, chemical experiments, and medical diagnostics due to their efficient mixing capabilities. Traditionally, the simulation of these mixers relies on the finite element method (FEM), which, although effective, presents challenges due to its computational complexity and time-consuming nature. To address this, we propose a nine-grid network (NGN) model theory with a centrally symmetric structure.The NGN uses a symmetric structure similar to a 3 × 3 grid to partition the fluid space to be predicted. Using this theory, we developed and trained an artificial neural network (ANN) to predict the fluid dynamics within microfluidic mixers. This approach significantly reduces the time required for fluid evaluation. In this study, we designed a prototype microfluidic mixer and validated the reliability of our method by comparing it with predictions from traditional FEM software. The results show that our NGN model completes fluid predictions in just 40 s compared to approximately 10 min with FEM, with acceptable error margins. This technology achieves a 15-fold acceleration, greatly reducing the time and cost of microfluidic chip design. Full article

The high sensitivity of living organic forms to space radiation remains the critical issue during spaceflight, to which they will be chronically exposed during months of interplanetary or even decades of interstellar spaceflight. In the human body, all actively dividing and poorly differentiated cells are always close to being damaged by radiological or chemical agents. The chronic exposure to ionizing radiation primarily causes changes in blood counts and intestinal damage such as fibrosis, obliterative vasculitis, changes in the gut microbiota, and atrophy or degeneration of muscle fibers. The project “MISS: Microbiome Induced Space Suit” was presented at the Giant Jamboree of the International Genetically Engineered Machine Competition 2021, with the aim to investigate the ability of the novel microbiota-mediated approach to enhance human resistance to ionizing radiation. The key innovative part of the project was the idea to create a novel radioprotector delivery mechanism based on human gut microbiota with the function of outer membrane vesicles (OMVs) secretion. The project concept proposed the feasibility of genetically modifying the human microbiota in situ through the delivery of genetic constructs to the host’s crypts using silicon nanoparticles with chemically modified surfaces. In this perspective, we discuss the advances in modifying microbiota-mediated secretory activity as a promising approach for radioprotection and as an alternative to hormone therapy and other health conditions that currently require continuous drug administration. Future clinical trials of in situ methods to genetic engineering the crypt microbiota may pave the way for indirect regulation of human cells. Full article

In recent years, the search for more sustainable fillers for elastomeric composites than silica and carbon black has been underway. In this work, silanized starch was used as an innovative filler for elastomeric composites. Corn starch was chemically modified by silanization (with n-octadecyltrimethoxysilane) via a condensation reaction to produce a hydrophobic starch. Starch/natural rubber composites were prepared by mixing the modified starch with elastomer. The morphology, hydrophobicity, and chemical structure of starch after and before modification were studied. The results showed that starch after silanization becomes hydrophobic (θ_w = 117.3°) with a smaller particle size. In addition, FT-IR spectrum analysis confirmed the attachment of silane groups to the starch. The modified starch dispersed better in the natural rubber matrix and obtained a more homogeneous morphology. The composite achieved the best dynamic (ΔG′ = 203.8 kPa) and mechanical properties (TS_b = 11.4 MPa) for compositions with 15 phr of modified starch. In addition, the incorporation of silanized starch improved the hydrophobicity of the composite (θ_w = 117.8°). The higher starch content allowed the composites to achieve a higher degree of cross-linking, resulting in better resistance to swelling in organic solvents. This improvement is due to enhanced elastomer–filler interactions and reduced spaces that prevent solvent penetration into the material’s depths. The improved mechanical properties and good dynamic properties, as well as improved hydrophobicity, were mainly due to improved interfacial interactions between rubber and starch. This study highlights the potential and new approach of silane-modified starch as a sustainable filler, demonstrating its ability to enhance the mechanical, dynamic, and hydrophobic properties of elastomeric composites while supporting greener material solutions for the rubber industry. Full article

The even more restrictive regulations imposed on chemical and acoustic emissions of ships necessitate the installation of after-treatment systems onboard. The spaces onboard are limited, and the Exhaust Gas Cleaning Systems (EGCSs) have big dimensions, so an appropriate integration and optimization of EGCSs allows to save space and comply with international regulations. Moreover, in the available literature, there is a lack of guidelines about the design of integrated EGCSs. This study aims to develop an ad hoc optimization methodology that uses combined Computational Fluid Dynamics (CFD)–Finite Element Method (FEM) simulations, surrogate models, and Genetic Algorithms to optimize the acoustic properties of EGCSs while considering the limits imposed by the efficiency of chemical reactions for the abatement of NO_x and SO_x. The developed methodology is applied to a Diesel Oxidation Catalyst (DOC), and the obtained results lead to a system that integrates the silencing effect into the DOC. Full article

The efficacy of machine learning has increased exponentially over the past decade. The utilization of machine learning to predict and design materials has become a pivotal tool for accelerating materials development. High-entropy alloys are particularly intriguing candidates for exemplifying the potency of machine learning due to their superior mechanical properties, vast compositional space, and intricate chemical interactions. This review examines the general process of developing machine learning models. The advances and new algorithms of machine learning in the field of high-entropy alloys are presented in each part of the process. These advances are based on both improvements in computer algorithms and physical representations that focus on the unique ordering properties of high-entropy alloys. We also show the results of generative models, data augmentation, and transfer learning in high-entropy alloys and conclude with a summary of the challenges still faced in machine learning high-entropy alloys today. Full article

The textural properties of synthetic and natural clays in the sodium form and exchanged with tetramethylammonium cations (TMA⁺) were characterized using N₂ and Ar physisorption isotherms at cryogenic temperatures. Specific surface areas and micro/mesoporous volumes were determined using the BET and the t-plot models. The t-plot analysis requires the use of reference isotherms measured at the same temperature on the surface of non-porous materials with an identical chemical composition. In order to better assess the effects of chemical heterogeneities in the clay particles, reference isotherms representative of silica surfaces were taken into account in the analysis of the t-curve and corrected to account for variations in curvature at the interface of the film adsorbed in the micropores. In addition, high-resolution Ar adsorption isotherms at 87 K were analyzed using the Derivative Isotherm Summation (DIS) method to quantify the energy contributions of adsorption sites and determine the fractions of basal and lateral surfaces of clay particles. The high-energy adsorption sites, identified in the low-pressure range, were attributed to intra-particle microporosity due to stacking defects and/or open inter-layer spaces. These sites were differentiated from those on the lateral and basal surfaces of the particles. A modification of the DIS method was proposed to measure these contributions and improve the fit with the experimental data. The results show that TMA⁺ cation exchange significantly increases the microporosity of clays compared to their sodic form, which can be attributed to the increased contribution of intra-particle adsorption sites due to interlayer expansion. Full article

The sun is a fundamental element of the natural environment, and kinetic equations are crucial mathematical models for determining how quickly the chemical composition of a star like the sun is changing. Taking motivation from these facts, we develop and solve a novel fractional kinetic equation containing Fermi–Dirac (FD) and Bose–Einstein (BE) functions. Several distributional properties of these functions and their proposed new generalizations are investigated in this article. In fact, it is proved that these functions belong to distribution space ${\mathcal{D}}^{\prime }$ while their Fourier transforms belong to ${\mathcal{Z}}^{\prime }.$ Fourier and Laplace transforms of these functions are computed by using their distributional representation. Thanks to them, we can compute various new fractional calculus formulae and a new relation involving the Fox–Wright function. Some fractional kinetic equations containing the FD and BE functions are also formulated and solved. Full article