Hasil untuk "Chemical industries"

K. Masters

Kimiyasu Sato, Ryutaro Usukawa, Yusuke Imai et al.

ABSTRACT Plastic‐forming methods of ceramic pastes, such as extrusion, have been significant in the ceramics industry for many years. Despite its significance, the plasticity of ceramic pastes is poorly understood. It is essential to understand the fundamentals of ceramic paste plasticity to develop a forming technology with superior performance. The ceramic pastes’ plasticity can be characterized by the co‐existence of apparently opposing properties, that is, “flowability” and “rigidity.” It has been claimed that the apparently opposing properties can be achieved by controlling the interparticle interaction that is attractive at long range and repulsive at short range. The present article aims to verify the model based on experimental results from interaction force measurements. The interaction forces are gathered by the atomic force microscope (AFM) colloid probe technique. On the basis of the discussion, it is concluded that the above microscopic model is available to interpret ceramic pastes’ macroscopic behaviors.

O. B. Wurzburg

Yaling Ke, Jakob Assan

The prospect of controlling chemical reactivity using frequency-tunable optical microcavities has materialized over the past decade, evolving into a fascinating yet challenging new field of polaritonic chemistry, a multidisciplinary domain at the intersection of quantum optics, chemical dynamics, and non-equilibrium many-body physics. While most theoretical efforts to date have focused on single-mode cavities, practical implementations in polaritonic chemistry typically involve planar optical cavities that support a series of equally spaced photon modes, determined by the cavity geometry. In this work, we present a numerically exact, fully quantum-mechanical study of chemical reactions in few-mode cavities, revealing two key scenarios by which multi-mode effects can enhance cavity-modified reactivity. The first scenario emerges when the free spectral range is comparable to the single-mode Rabi splitting. In such cases, hybridization between a rate-decisive molecular vibration and a central resonant cavity mode reshapes the resonance landscape, enabling additional reaction pathways mediated by adjacent cavity modes. The second scenario exploits the intrinsic anharmonicity of molecular vibrations, which gives rise to multiple dipole-allowed transitions with distinct energies. Under multi-mode strong coupling, where different cavity modes individually resonate with these distinct transitions, multi-photon processes involving sequential absorption across multiple modes become accessible. This leads to a nontrivial and non-additive rate enhancement via cascade-like vibrational ladder climbing. Together, these findings offer new strategies for tailoring chemical reactivity by harnessing the structural richness of multi-mode structure, offering valuable insights for optimal experimental designs in polaritonic catalysis.

Hytham Mahmoud Abdel-Latif , Amal Yaseen Zaman , Rawan Bafail et al.

Background: The recurring psychological, behavioural, and physical symptoms of premenstrual tension syndrome (PTS) are related to the luteal phase of the menstrual cycle. Its development may be influenced by hormonal influences. Objectives: To investigate the effects of catecholamines on PTS and possible improvements upon using pharmacological treatments. Patients and methods: After agreement of participants and ethical committee approval, this randomized controlled trial enrolled 60 obese women with PTS who were split into younger (18-39 years) and older (40-48 years) age groups in comparison to an age-matched healthy control group. All of the study participants' serum levels of adrenaline (epinephrine), and norepinephrine were measured. Utilizing pertinent rating scales, a few psychosomatic PTS symptoms (headache, backache, and stomach bloating) were also assessed. Results: Compared to the control group, obese women with PTS had significantly higher serum levels of adrenaline, and noradrenaline (p<0.05). Utilizing a combination of therapies, including Metformin, Amiloride/ Hydrochlorthiazide, a calorie-restricted diet, walking exercises, and either Vitazinc®, or Royal vitamin G, significantly (p< 0.01) reduced the hormonal abnormalities. In women with PTS, symptoms of headache, backache, and stomach bloating significantly increased (p< 0.001). Utilizing the combined treatments dramatically improved all of that. In conjunction with the normalization of serum hormone levels, such combined therapies considerably reduced the levels of the aforementioned hormones in the blood and the intensity of all the analysed psychosomatic symptoms (p<0.001). Conclusion: PTS is related to obesity and is linked to higher serum catecholamines. There were improvements in hormonal abnormalities and psychosomatic symptoms with the given pharmacological treatments.

Muhammad Khalid, Syed Hussain, Muhammad Shafiq et al.

The Cathodic Cage Plasma Nitriding (CCPN) system has been widely used as a better and low-cost surface modification technique for stainless steel and other metals. The efficacy of the CCPN system depends upon several control parameters. The effect of the radius and the shape of the cathodic cage (aluminum) on the electric potential at the sample surface was investigated using COMSOL Multiphysics. The CCPN system was optimized using these results.  The role of temperature on the performance of CCPN on AISI 430 ferritic stainless steel was investigated experimentally. The samples of AISI 430 ferritic stainless steel were treated at varied temperatures (200oC-400oC) under fixed processing conditions of the CCPN system. The nitrided samples were characterized using X-ray diffraction, micro-hardness tester, cross-sectional scanning electron microscopy, Fourier transform infrared spectroscopy and ball-on-disc wear tester.

Md Elias, Ehsanur Rahman, Sonia Akter et al.

The growing interest in combining the photocatalytic properties of semiconductors like ZnO and TiO2 with the superior electron conduction capabilities of graphene has resulted in the successful synthesis of in-situ reduced graphene oxide (rGO) supported ZnO-TiO2 nanostructures through a simple microwave-assisted synthesis method. X-ray Diffraction Spectroscopy (XRD), Field Emission Scanning Electron Microscope (FESEM), UV–visible spectroscopy (UV–vis), and Fourier Transform Infrared Spectroscopy (FTIR) were employed to characterize structural, morphological and optical properties as well as surface functional groups of the synthesized products. The XRD measurements of our synthesized samples confirm both structural crystallinity and phase purity, while the FTIR analysis verifies the complete reduction of graphene oxide (GO) to reduced graphene oxide (rGO). The synthesized ternary nanocomposite ZnO-TiO2-rGO exhibited a remarkable 100 % adsorption-assisted removal efficiency for 20 mg/L methylene blue (MB) dye under ultraviolet light illumination within 120 min, along with a 56 % dye adsorption removal efficiency in the same time interval. In comparison, pure ZnO showed 0 % adsorption and only 31 % photocatalytic efficiency at the similar condition. Remarkably, the ZnO-TiO2-rGO nanocomposite exhibited exceptional photocatalytic activity mediated by adsorption, achieving complete degradation of MB dye within 5 min under sunlight irradiation. The photocatalytic efficiency and dye adsorption capacity were found to be significantly lower for the anionic dye methyl orange (MO) compared to the cationic MB dye. The study thoroughly investigated the influence of catalyst dose and initial dye concentration on photodegradation. The proposed mechanism indicates that the extensive surface area and numerous active sites on the rGO promote adsorption, which is then followed by degradation through the metal oxides. Overall, the results unveil that the microwave-assisted synthesis of ZnO-TiO2-rGO nanocomposite is a promising and environmentally friendly approach for efficiently degrading dyes from contaminated wastewater using both UV light and natural sunlight irradiation.

María Carla Groff, Sandra Edith Noriega, María Eugenia Díaz Meglioli et al.

Solid-state fermentation (SSF) is the bioprocess where microorganisms are cultivated in the absence of free water under controlled conditions. Lactic acid can be produced by <i>Rhizopus oryzae</i> SSF of grape stalks. During the microorganism’s growth, the temperature and water content of the solid bed fluctuate, leading to areas of either dry or excessive moisture in the solid substrate. Therefore, it is crucial to control the water supply to the matrix. In this work, we obtain lactic acid through SSF of grape stalks using <i>Rhizopus oryzae</i> NCIM 1299. The SSF was conducted at a fixed temperature of 35 °C, with five constant relative humidity (<i>RH</i>) levels: 50, 57, 65, 72, and 80%<i>RH</i>. Mathematical models, including the Logistic and First-Order Plus Dead-Time models for fungal biomass growth and the Luedeking and Piret with Delay Time model for lactic acid production, were adjusted to kinetic curves. Growth kinetic parameters (<i>X_max</i>, <i>μ_max</i>, <i>T_p</i>, <i>T</i>₀, <i>Y_p/x</i>, and <i>t_d</i>) were determined for all conditions. These kinetic parameters were then correlated with relative humidity using a second-degree polynomial relationship. We observed a decrease in <i>X_max</i> with an increasing %<i>RH</i>, while the value of <i>Y_p/x</i> increased at a higher %<i>RH</i>. Finally, the optimal variable relative humidity profile was obtained by applying the dynamic optimization technique, resulting in a 16.63% increase in lactic acid production.

Nina Glaser, Markus Reiher

Many complex chemical problems encoded in terms of physics-based models become computationally intractable for traditional numerical approaches due to their unfavourable scaling with increasing molecular size. Tensor decomposition techniques can overcome such challenges by decomposing unattainably large numerical representations of chemical problems into smaller, tractable ones. In the first two decades of this century, algorithms based on such tensor factorizations have become state-of-the-art methods in various branches of computational chemistry, ranging from molecular quantum dynamics to electronic structure theory and machine learning. Here, we consider the role that tensor decomposition schemes have played in expanding the scope of computational chemistry. We relate some of the most prominent methods to their common underlying tensor network formalism, providing a unified perspective on leading tensor-based approaches in chemistry and materials science.

Zeying Zhang, Xueqin Zhang, Y. X. Zhao et al.

The balancing of chemical equations is a basic problem in chemistry. A commonly employed method is to convert the task to a linear algebra problem, and then solve the null space of the constructed formula matrix. However, in this method, the directly obtained solution may be invalid, and there is no canonical choice of independent basis reactions. Here, we show that these drawbacks originate from the fact that the fundamental structure of solutions here is not a linear space but a positive affine monoid. This new understanding enables a systematic approach and a complete description of all possible reactions by a unique set of independent elementary reactions, called Hilbert-basis reactions. By clarifying its underlying mathematical structure, our work offers a new perspective on this old problem of balancing chemical equations.

Bo Liang, Qun Yang, Xinping Zhang et al.

Abstract Background Sesquiterpenes are designated as a large class of plant-derived natural active compounds, which have wide applications in industries of energy, food, cosmetics, medicine and agriculture. Neither plant extraction nor chemical synthesis can meet the massive market demands and sustainable development goals. Biosynthesis in microbial cell factories represents an eco-friendly and high-efficient way. Among several microorganisms, Saccharomyces cerevisiae exhibited the potential as a chassis for bioproduction of various sesquiterpenes due to its native mevalonate pathway. However, its inefficient nature limits biosynthesis of diverse sesquiterpenes at industrial grade. Results Herein, we exploited an artificial synthetic malonic acid-acetoacetyl-CoA (MAAC) metabolic pathway to switch central carbon metabolic flux for stable and efficient biosynthesis of sesquiterpene-based high-density biofuel precursor in S. cerevisiae. Through investigations at transcription and metabolism levels, we revealed that strains with rewired central metabolism can devote more sugars to β-caryophyllene production. By optimizing the MVA pathway, the yield of β-caryophyllene from YQ-4 was 25.8 mg/L, which was 3 times higher than that of the initial strain YQ-1. Strain YQ-7 was obtained by introducing malonic acid metabolic pathway. Combing the optimized flask fermentation process, the target production boosted by about 13-fold, to 328 mg/L compared to that in the strain YQ-4 without malonic acid metabolic pathway. Conclusion This designed MAAC pathway for sesquiterpene-based high-density biofuel precursor synthesis can provide an impressive cornerstone for achieving a sustainable production of renewable fuels.

Ekin Sucu

Two in vitro studies were carried out on nonlactating dairy cows. Experiment 1 compared the methanogenesis and rumen fermentation parameters of various microalgae (<i>Spirulina platensis</i>, <i>Chlorella vulgaris</i>, and <i>Schizochytrium</i> spp.) and protein feeds (sunflower meal, soybean meal, and alfalfa hay) with monensin (MON). Rumen fermentation parameters were determined by an in vitro gas production system. Experiment 2 compared the ability of three microalgae to prevent acidosis. They were tested for 6 h against oat straw (100 mg) and MON (12 g/mL) to ameliorate ruminal acidosis caused by the addition of glucose (0.1 g/mL) as a fermentable carbohydrate with rumen fluid. In experiment 1, there were variations in the nutrient content of microalgae and protein sources. The dry matter content of the substrates ranged from 90 to 94%, and the organic matter content ranged from 82 to 88%, with <i>Schizochytrium</i> spp. having the highest. Protein content in algae and protein feeds ranged from 18–62% of dry matter (DM) to 16–48% DM, with <i>S. platensis</i> and <i>C. vulgaris</i> having the highest. The ether extract of <i>Schizochytrium</i> spp. (45.5% DM) was the highest of any substrate. In vitro rumen fermentation revealed that protein feeds increased the cumulative gas production at the highest level while MON caused a decrease. Ruminal pH was found to be higher in MON (6.95) and protein feeds (6.77–6.81) than in algae (6.37–6.50). In addition, in terms of metabolizable energy and digestible organic matter, protein feeds outperformed algae. The MON produced the least amount of methane (CH₄) of any substrate, but <i>Schizochytrium</i> spp. demonstrated potential for CH₄ reduction. In these groups, the decrease in CH₄ production was accompanied by a decrease in total volatile fatty acids, acetate, and the acetate-to-propionate ratio, but an increase in propionate. Experiment 2 revealed MON as the most effective cure for controlling acidosis. However, <i>C. vulgaris</i> and <i>Schizochytrium</i> spp. had an effect on medium culture pH and demonstrated potential for acidosis prevention. This study found that algae can influence ruminal fermentation, have the potential to reduce CH₄ production, and may reduce acidosis incidence rates. These assumptions, however, must be validated through in vivo studies.

Efrain Gatuzz, J. S. Sanders, K. Dennerl et al.

The analysis of the elemental abundances in galaxy clusters offers valuable insights into the formation and evolution of galaxies. In this study, we explore the chemical enrichment of the intergalactic medium (ICM) in the Ophiuchus cluster by utilizing {\it XMM-Newton} EPIC-pn observations. We explore the radial profiles of Si, S, Ar, Ca, and Fe. Due to the high absorption of the system, we have obtained only upper limits for O, Ne, Mg, and Ni. We model the X/Fe ratio profiles with a linear combination of core-collapse supernovae (SNcc) and type~Ia supernovae (SNIa) models. We found a flat radial distribution of SNIa ratio over the total cluster enrichment $10-30\%$ for all radii. However, the absence of light $α$-elements abundances may lead to over-estimation of the SNcc contribution.

Hyun-Myung Chun, Jordan M. Horowitz

We study the response of chemical reaction networks driven far from equilibrium to logarithmic perturbations of reaction rates. The response of the mean number of a chemical species is observed to be quantitively limited by number fluctuations as well as the maximum thermodynamic driving force. We prove these trade-offs for linear chemical reaction networks and a class of nonlinear chemical reaction networks with a single chemical species. Numerical results for several model systems support the conclusion that these trade-offs continue to hold for a broad class of chemical reaction networks, though their precise form appears to depend sensitively on the deficiency of the network.

Ivar Svalheim Haugerud, Pranay Jaiswal, Christoph A. Weber

Recent experimental studies suggest that wet-dry cycles and coexisting phases can each strongly alter chemical processes. The mechanisms of why and to which degree chemical processes are altered when subject to evaporation and condensation are unclear. To close this gap, we developed a theoretical framework for non-dilute chemical reactions subject to non-equilibrium conditions of evaporation and condensation. We find that such conditions can change the half-time of the product's yield by more than an order of magnitude, depending on the substrate-solvent interaction. We show that the cycle frequency strongly affects the chemical turnover when maintaining the system out of equilibrium by wet-dry cycles. There exists a resonance behavior in the cycle frequency where the turnover is maximal. This resonance behavior enables wet-dry cycles to select specific chemical reactions suggesting a potential mechanism for chemical evolution in prebiotic soups at early Earth.

Clayton W. Kosonocky, Aaron L. Feller, Claus O. Wilke et al.

Chemical similarity searches are widely used in-silico methods for identifying new drug-like molecules. These methods have historically relied on structure-based comparisons to compute molecular similarity. Here, we use a chemical language model to create a vector-based chemical search. We extend implementations by creating a prompt engineering strategy that utilizes two different chemical string representation algorithms: one for the query and the other for the database. We explore this method by reviewing the search results from five drug-like query molecules (penicillin G, nirmatrelvir, zidovudine, lysergic acid diethylamide, and fentanyl) and three dye-like query molecules (acid blue 25, avobenzone, and 2-diphenylaminocarbazole). We find that this novel method identifies molecules that are functionally similar to the query, indicated by the associated patent literature, and that many of these molecules are structurally distinct from the query, making them unlikely to be found with traditional chemical similarity search methods. This method may aid in the discovery of novel structural classes of molecules that achieve target functionality.

Magdalena Janota, Ors Istok, David A. Faux et al.

We use 1H nuclear magnetic resonance (NMR) methods to show that the relaxation time governing the redistribution of the gel-pore porosity in cement pastes during sorption depends, not surprisingly, on the dry state saturation and also, more surprisingly, on the sample size. The relaxation time is typically in the range 20 to 40 h for cylindrical samples 60 mm long dried to saturations between about 40 and 55%. It increases up to 200 h for samples dried to between 20 and 30% saturation. The times are all very much longer than for 1 mm samples. There is additional evidence to support the idea that the relaxation of hydrate inter-layer sized spaces occurs on at least two timescales, one of which is very much longer (months) than any of those listed above.

Bing Huang, O. Anatole von Lilienfeld, Jaron T. Krogel et al.

In the past decade, quantum diffusion Monte Carlo (DMC) has been demonstrated to successfully predict the energetics and properties of a wide range of molecules and solids by numerically solving the electronic many-body Schrödinger equation. We show that when coupled with quantum machine learning (QML) based surrogate methods the computational burden can be alleviated such that QMC shows clear potential to undergird the formation of high quality descriptions across chemical space. We discuss three crucial approximations necessary to accomplish this: The fixed node approximation, universal and accurate references for chemical bond dissociation energies, and scalable minimal amons set based QML (AQML) models. Numerical evidence presented includes converged DMC results for over one thousand small organic molecules with up to 5 heavy atoms used as amons, and 50 medium sized organic molecules with 9 heavy atoms to validate the AQML predictions. Numerical evidence collected for $Δ$-AQML models suggests that already modestly sized QMC training data sets of amons suffice to predict total energies with near chemical accuracy throughout chemical space.

Kiran H. Kanekal, Joseph F. Rudzinski, Tristan Bereau

Compared to top-down coarse-grained (CG) models, bottom-up approaches are capable of offering higher structural fidelity. This fidelity results from the tight link to a higher-resolution reference, making the CG model chemically specific. Unfortunately, chemical specificity can be at odds with compound-screening strategies, which call for transferable parametrizations. Here we present an approach to reconcile bottom-up, structure-preserving CG models with chemical transferability. We consider the bottom-up CG parametrization of 3,441 C$_7$O$_2$ small-molecule isomers. Our approach combines atomic representations, unsupervised learning, and a large-scale extended-ensemble force-matching parametrization. We first identify a subset of 19 representative molecules, which maximally encode the local environment of all gas-phase conformers. Reference interactions between the 19 representative molecules were obtained from both homogeneous bulk liquids and various binary mixtures. An extended-ensemble parametrization over all 703 state points leads to a CG model that is both structure-based and chemically transferable. Remarkably, the resulting force field is on average more structurally accurate than single-state-point equivalents. Averaging over the extended ensemble acts as a mean-force regularizer, smoothing out both force and structural correlations that are overly specific to a single state point. Our approach aims at transferability through a set of CG bead types that can be used to easily construct new molecules, while retaining the benefits of a structure-based parametrization.

Ahmed Faris Al-Refaie, Olivia Venot, Quentin Changeat et al.

We introduce a new Python 1D chemical kinetic code FRECKLL (Full and Reduced Exoplanet Chemical Kinetics distiLLed) to evolve large chemical networks efficiently. FRECKLL employs `distillation' in computing the reaction rates, which minimizes the error bounds to the minimum allowed by double precision values ($ε\leq 10^{-15}$). Compared to summation of rates with traditional algorithms like pairwise summation, distillation provides a tenfold reduction in solver time for both full and reduced networks. Both the full and reduced Venot2020 networks are packaged in FRECKLL as well as a TauREx 3.1 plugin for usage in forward modelling and retrievals of exoplanet atmospheres. We present TauREx retrievals performed on a simulated HD189733 JWST spectra using the full and reduced Venot2020 chemical networks and demonstrate the viability of total disequilibrium chemistry retrievals and the ability for JWST to detect disequilibrium processes.