Hasil untuk "Chemical industries"

Daniel Julian, Jesús Pérez-Ríos

Recent progress in machine learning has sparked increased interest in utilizing this technology to predict the outcomes of chemical reactions. The ultimate aim of such endeavors is to develop a universal model that can predict products for any chemical reaction given reactants and physical conditions. In pursuit of ever more universal chemical predictors, machine learning models for atom-diatom and diatom-diatom reactions have been developed, yet no such models exist for termolecular reactions. Accordingly, we introduce neural networks trained to predict opacity functions of atom recombination reactions. Our models predict the recombination of Sr$^+$ + Cs + Cs $\rightarrow$ SrCs$^+$ + Cs and Sr$^+$ + Cs + Cs $\rightarrow$ Cs$_2$ + Sr$^+$ over multiple orders of magnitude of energy, yielding overall results with a relative error $\lesssim 10\%$. Even far beyond the range of energies seen during training, our models predict the atom recombination reaction rate accurately. As a result, the machine is capable of learning the physics behind the atom recombination reaction dynamics.

Manajit Das, Ajnabiul Hoque, Mayank Baranwal et al.

Prediction of complete step-by-step chemical reaction mechanisms (CRMs) remains a major challenge. Whereas the traditional approaches in CRM tasks rely on expert-driven experiments or costly quantum chemical computations, contemporary deep learning (DL) alternatives ignore key intermediates and mechanistic steps and often suffer from hallucinations. We present DeepMech, an interpretable graph-based DL framework employing atom- and bond-level attention, guided by generalized templates of mechanistic operations (TMOps), to generate CRMs. Trained on our curated ReactMech dataset (~30K CRMs with 100K atom-mapped and mass-balanced elementary steps), DeepMech achieves 98.98+/-0.12% accuracy in predicting elementary steps and 95.94+/-0.21% in complete CRM tasks, besides maintaining high fidelity even in out-of-distribution scenarios as well as in predicting side and/or byproducts. Extension to multistep CRMs relevant to prebiotic chemistry, demonstrates the ability of DeepMech in effectively reconstructing 2 pathways from simple primordial substrates to complex biomolecules such as serine and aldopentose. Attention analysis identifies reactive atoms/bonds in line with chemical intuition, rendering our model interpretable and suitable for reaction design.

Liping Lu, Xue Zhang, Ziyi Yin et al.

This study aimed to enhance the value of agricultural by-products by developing seed melon compound fermented feed (SMFF) using <i>Saccharomyces cerevisiae</i> L23. A two-stage optimization strategy was implemented. First, seed melon juice seed culture medium (SMCM) composition and fermentation conditions were optimized to maximize <i>S. cerevisiae</i> L23 biomass through single-factor and response surface methodology (RSM) approaches. The SMCM medium was optimized to contain 0.06% MgSO₄·7H₂O, 0.2% KH₂PO₄, 0.65% (NH₄)₂SO₄, 0.1% pectinase, and 1.0% urea, and fermentation conditions with inoculation amount, fermentation time, fermentation temperature, and glucose addition were 6%, 28 h, 30 °C, and 0.5%, respectively. Furthermore, SMFF fermentation parameters were optimized via RSM, achieving <i>S. cerevisiae</i> L23 (10.35 lg CFU/g) and sensory evaluation score (83.1) at substrate ratio of 7:3 (seed melon juice: <i>Zanthoxylum bungeanum</i> seed meal), inoculation amount of 8%, and fermentation time of 36 h. Fermentation process significantly improved the nutritional profile of SMFF, increasing crude protein (13%) and vitamin C (VC) content (21%) while reducing neutral detergent fiber/acid detergent fiber (NDF/ADF) levels. SMFF also improved in vitro antioxidant capacity, with higher DPPH, ABTS, hydroxyl radical, and superoxide anion scavenging activities compared to SMFF control. This process efficiently valorized agricultural by-products into nutritionally enriched functional feed.

Yufei Li, Kaifeng Shuai, Juan Li et al.

This study compared the decomposition effects of different microbial agents added to rice straw to screen for efficient and stable microbial agents and achieve effective utilization of rice straw resources. Different microbial agents can accelerate the decomposition of rice straw. The E4/E6 value of rice straw added with the <i>Bacillus subtilis</i> agent was significantly lower than that of rice straw added with other microbial agents on the 30th day. The lignin degradation rates for the <i>Bacillus subtilis</i> agent and <i>Trichoderma viride</i> agent treatments were higher than those of the other treatments from the 5th to 30th days. After adding the <i>Bacillus subtilis</i> agent for 30 days, the degradation rates of hemicellulose and cellulose in rice straw were higher than others, reaching 33.62% and 41.31%, respectively. Through principal component analysis and grey relational analysis, it was determined that the C/N ratio, organic carbon, E4/E6 value, conductivity value, and pH value are important evaluation indicators for the maturity promotion effect. Using the membership function analysis method, it was found that the <i>Bacillus subtilis</i> agent had the best overall performance in straw decomposition. This research provides a new viewpoint for the efficient utilization of straw resources.

Nicolás Gabriel Orsetti, Domingo Pérez-Coll, Gabriel Lorenzo et al.

Ti-doped lithium zirconate (LZTO) sheets were successfully made by aqueous tape casting, by starting from an innovative colloidal synthesis route and optimizing the colloidal processing parameters. Zeta potential and particles size measurements, together with rheological characterization, were performed to adjust the slip composition. Then, the suspension with 1.0 wt% of ammonium polyacrylate dispersant (APA) and solid loading of 31 vol.% resulted in flat and flexible sheets, thinner than 450 µm, by adding 20 wt% of acrylic binder emulsion. After annealing at 1150 °C/15 h, LZTO sheets densified up to 85.5 %, presented a microstructure with grains of 6.4 µm in diameter, and exhibited electrical conductivity values in the order of 10−7, 10−6 and 10−5 S·cm−1 at 350, 450 and 600 °C, respectively. Besides, XRD phase analysis revealed monoclinic Li2ZrO3 and minor ZrO2 impurities originated from Li2O volatilization during sintering, but no sign of TiO2 segregation, indicating the formation of Li2Zr1-XTiXO3 solid solution.

Sabreen Bashir, Navneet Kaur, Agrataben Vadhel et al.

Abstract Background Cannabis sativa L., renowned for its versatility in pharmaceutical, textile, and cosmetic industries, is highly susceptible to several agronomic and environmental factors, particularly herbicides. These chemical agents, while commonly used for weed control, can adversely affect plant growth, physiology, and secondary metabolite production. Understanding the plant’s response to such external stressors is essential for optimizing its cultivation and ensuring the quality of its bioactive compounds. Methods In our current work, we studied the impact of two herbicides- glyphosate and metribuzin on the morpho-physiological and biochemical characteristics of cannabis plants. The secondary metabolite production analysis was carried out using Gas Chromatography-Mass S pectrometry (GC-MS). Furthermore, in silico studies using molecular modelling and optimization via Density Functional Theory (DFT) were performed, followed by molecular docking. Results It was observed that both herbicides greatly impact overall plant productivity including primary and secondary metabolite production. Further, glyphosate treatment caused an increase in fatty acid synthesis while the contrary was observed in case of metribuzin. Also, herbicide stress leads to the synthesis of cannabidivarol and cannabidiol although they were absent in the untreated group. These findings provide crucial insights for optimizing agricultural practices in cannabis cultivation. Moreover, molecular simulation results showed that both metribuzin and glyphosate bind at the active pocket of Tetrahydrocannabinolic acid synthase (THCA synthase) and offer a mechanistic explanation for the observed variations in Δ9 -tetrahydocannabinol (THC) levels by suggesting that both herbicides inhibit THCA synthase activity, contributing to a deeper understanding of herbicide-plant interactions at the molecular level. Conclusions Our findings indicate that herbicide stress impacts overall cannabis productivity and alters biosynthesis. The stress notably stimulates the production of cannabidivarol and cannabidiol. In addition, molecular docking studies revealed that metribuzin binds to the same active channel as Cannabigerolic acid (CBGA)- the THC precursor, while glyphosate binds at the entrance, thereby hindering THC production. This multifaceted approach guides sustainable farming strategies and has implications for manipulating cannabinoid profiles in pharmaceutical and other industrial applications.

Yuqing Huang, Rongyang Zhang, Xuesong He et al.

There is a growing interest in the role that LLMs play in chemistry which lead to an increased focus on the development of LLMs benchmarks tailored to chemical domains to assess the performance of LLMs across a spectrum of chemical tasks varying in type and complexity. However, existing benchmarks in this domain fail to adequately meet the specific requirements of chemical research professionals. To this end, we propose \textbf{\textit{ChemEval}}, which provides a comprehensive assessment of the capabilities of LLMs across a wide range of chemical domain tasks. Specifically, ChemEval identified 4 crucial progressive levels in chemistry, assessing 12 dimensions of LLMs across 42 distinct chemical tasks which are informed by open-source data and the data meticulously crafted by chemical experts, ensuring that the tasks have practical value and can effectively evaluate the capabilities of LLMs. In the experiment, we evaluate 12 mainstream LLMs on ChemEval under zero-shot and few-shot learning contexts, which included carefully selected demonstration examples and carefully designed prompts. The results show that while general LLMs like GPT-4 and Claude-3.5 excel in literature understanding and instruction following, they fall short in tasks demanding advanced chemical knowledge. Conversely, specialized LLMs exhibit enhanced chemical competencies, albeit with reduced literary comprehension. This suggests that LLMs have significant potential for enhancement when tackling sophisticated tasks in the field of chemistry. We believe our work will facilitate the exploration of their potential to drive progress in chemistry. Our benchmark and analysis will be available at {\color{blue} \url{https://github.com/USTC-StarTeam/ChemEval}}.

Enrico Storti, Patricia Kaiser, Marc Neumann et al.

In this work, refractory components based on alumina were produced by binder jetting using a large-scale 3D printer. The formulation contained several particle fractions up to a grain size of 3 mm, equal to the printer resolution. The binder system contained fine dead burnt magnesia, milled citric acid and reactive alumina, which were added to the aggregate mixture to create the powder bed. Deionized water was deposited from the printer's nozzles and triggered the binding reaction between the magnesia and citric acid. After 24 h, the printed samples were removed from the powder bed, dried and sintered at 1600 °C for 5 h. Reactive alumina contributed to the in situ creation of magnesium aluminate spinel at high temperature. The samples were characterized in terms of Young's modulus of elasticity, bending and compressive strength in 2 directions (parallel and perpendicular to the printing direction). The broken parts were used to investigate physical properties such as the open porosity and bulk density. The microstructure was studied by means of computed tomography. Finally, powder samples were used to determine the phase composition at different stages of production by means of XRD.

Mengting Lin, Zhangtian Wu, Ji Zou et al.

In this work, nanocrystalline high-entropy ceramics (HECs) were prepared by the combination of combustion synthesis and ultra-high pressure sintering. The prepared high-entropy (La0.2Nd0.2Sm0.2Eu0.2Gd0.2)2Zr2O7 ceramic powder had the average grain size of 11 nm and displayed the disordered defective fluorite structure. The HECs sintered under ultra-high pressure showed the defective fluorite structure, whereas the control samples fabricated by pressureless sintering showed the ordered pyrochlore structure. The HECs sintered under pressure of 10 GPa possessed much smaller grain size than that obtained by pressureless sintering. In particular, the grain size of ceramics sintered under 10 GPa at 600 °C was not significantly larger than that of raw powder and its Vickers hardness was 11.9 GPa. Ultra-high pressure sintering could remarkably increase the density of ceramics and restrain the growth of grains. The plastic deformation under ultra-high pressure was believed as the main densification mechanism for the grain refinement and performance improvement.

Jun Ouyang, Yinxiu Xue, Chuanqi Song et al.

In the research field of energy storage dielectrics, the “responsivity” parameter, defined as the recyclable/recoverable energy density per unit electric field, has become critically important for a comprehensive evaluation of the energy storage capability of a dielectric. In this work, high recyclable energy density and responsivity, i.e., Wrec = 161.1 J·cm–3 and ξ = 373.8 J·(kV·m2)–1, have been simultaneously achieved in a prototype perovskite dielectric, BaTiO3, which is integrated on Si at 500 ℃ in the form of a submicron thick film. This ferroelectric film features a multi-scale polar structure consisting of ferroelectric grains with different orientations and inner-grain ferroelastic domains. A LaNiO3 buffer layer is used to induce a {001} textured, columnar nanograin microstructure, while an elevated deposition temperature promotes lateral growth of the nanograins (in-plane diameter increases from ~10–20 nm at lower temperatures to ~30 nm). These preferably oriented and periodically regulated nanograins have resulted in a small remnant polarization and a delayed polarization saturation in the film’s P–E behavior, leading to a high recyclable energy density. Meanwhile, an improved polarizability/dielectric constant of the BaTiO3 film has produced a much larger maximum polarization than those deposited at lower temperatures at the same electric field, leading to a record-breaking responsivity for this simple perovskite.

Fumio Hirata

The generalized Langevin mode analysis (GLMA) is applied to chemical reactions in biomolecules in solution. The theory sees a chemical reaction in solution as a barrier crossing process, similar to the Marcus theory. The barrier is defined as the crossing point of two free-energy surfaces which are attributed to the reactant and product of the reaction. It is assumed that the both free-energy surfaces are quadratic or harmonic. The assumption is based on the Kim-Hirata theory of structural fluctuation of protein, which proves that the fluctuation around an equilibrium structure is quadratic with respect to the structure or atomic coordinates. The quadratic surface is a composite of many harmonic functions with different modes or frequencies. The height of the activation barrier will be dependent on the mode or frequency, less the frequency, lower the barrier. So, it is essential to decouple the fluctuational mode into a hierarchical order. GLMA is impeccable for this purpose. It is essential for a theoretical study of chemical reactions to chose a reaction coordinate along which the reaction proceeds. We suppose that the mode whose center of coordinate and/or the frequency changes most before and after the reaction is the one relevant to the chemical reaction, and choose the coordinate as the reaction coordinate. The rate of reaction along the reaction coordinate is , which is similar to the Marcus expression for the electron transfer reaction. In the equation, is the activation barrier defined by , where and denote the free energies at equilibrium , and the crossing point , respectively, both on the free energy surface of the reactant.

Derek S. Wang, Johannes Flick, Susanne F. Yelin

Recent experiments of chemical reactions in optical cavities have shown great promise to alter and steer chemical reactions but still remain poorly understood theoretically. In particular the origin of resonant effects between the cavity and certain vibrational modes in the collective limit is still subject to active research. In this paper, we study unimolecular dissociation reactions of many molecules collectively interacting with an infrared cavity mode through their vibrational dipole moment. We find that the reaction rate can slow down by increasing the number of aligned molecules if the cavity mode is resonant with a vibrational frequency of the molecules. We also discover a simple scaling relation that scales with the collective Rabi splitting to estimate the onset of reaction rate modification by collective vibrational strong coupling and numerically demonstrate these effects for up to 10,000 molecules.

P. M. Biesheuvel

In this tutorial we discuss the chemical potential of ions in water (i.e., in a salt solution, in an electrolyte phase) and inside (charged) nanoporous materials such as porous membranes. In water treatment, such membranes are often used to selectively remove ions from water by applying pressure (which pushes water through the membrane while most ions are rejected) or by current (which transports ions through the membrane). Chemical equilibrium across a boundary (such as the solution-membrane boundary) is described by an isotherm for neutral molecules, and for ions by an electrical double layer (EDL) model. An EDL model describes concentrations of ions inside a porous material as function of the charge and structure of the material. There are many contributions to the chemical potential of an ion, and we address several of these in this tutorial, including ion volume and the effect of ion-ion Coulombic interactions. We also describe transport and chemical reactions in solution, and how they are affected by Coulombic interactions.

Kevin Fiederling, Mostafa Abasifard, Martin Richter et al.

Tip-enhanced Raman spectroscopy (TERS) is a valuable method for surface analysis with nanometer to angstrom-scale resolution, however, the accurate simulation of particular TERS signals remains a computational challenge. We present a unique approach to this challenge by combining the two main contributors to plasmon-enhanced Raman spectroscopy and to the high resolution in TERS in particular, the electromagnetic and the chemical effect, into one quantum mechanical simulation. The electromagnetic effect describes the sample's interaction with the strong, highly localized and inhomogeneous electric fields associated with the plasmonic tip, and is typically the thematic focus for most mechanistic studies. On the other hand, the chemical effect covers the different responses to the extremely close-range and highly position-sensitive chemical interaction between the apex tip atom(s) and the sample, and, as we could show in previous works, plays an often underestimated role. Starting from a (time-dependent) density functional theory description of the chemical model system, comprised of a tin(II) phthalocyanine (SnPc) sample molecule and a single silver atom as tip, we introduce the electromagnetic effect through a series of point charges that recreate the electric field in the vicinity of the plasmonic Ag nanoparticle. By scanning the tip over the molecule along a 3D grid, we can investigate the system's Raman response on each position for non-resonant and resonant illumination. Simulating both effects on their own already hints at the achievable signal enhancement and resolution, but the combination of both creates even stronger evidence that TERS is capable of resolving sub-molecular features.

Christophe Gourlaouen, Jean-Philip Piquemal

We discuss the quantum chemical nature of the Lead(II) valence basins, sometime called the Lead "lone pair". Using various chemical interpretation tools such as the molecular orbital analysis, Natural Bond Orbitals (NBO), Natural Population Analysis (NPA) and Electron Localization Function (ELF) topological analysis, we study a variety of Lead(II) complexes. A careful analysis of the results show that the optimal structures of the lead complexes are only govern by the 6s and 6p subshells whereas no involvement of the 5d orbitals is found. Similarly, we do not find any significant contribution of the 6d. Therefore, the Pb(II) complexation with its ligand can be explained through the interaction of the 6s2 electrons and the accepting 6p orbitals. We detail the potential structural and dynamical consequences of such electronic structure organization of the Pb (II) valence domain.

M. Meuwly

Machine learning techniques applied to chemical reactions has a long history. The present contribution discusses applications ranging from small molecule reaction dynamics to platforms for reaction planning. ML-based techniques can be of particular interest for problems which involve both, computation and experiments. For one, Bayesian inference is a powerful approach to include knowledge from experiment in improving computational models. ML-based methods can also be used to handle problems that are formally intractable using conventional approaches, such as exhaustive characterization of state-to-state information in reactive collisions. Finally, the explicit simulation of reactive networks as they occur in combustion has become possible using machine-learned neural network potentials. This review provides an overview of the questions that can and have been addressed using machine learning techniques and an outlook discusses challenges in this diverse and stimulating field. It is concluded that ML applied to chemistry problems as practiced and conceived today has the potential to transform the way with which the field approaches problems involving chemical reactions, both, in research and academic teaching.

Yan-Zhong Wu, Chun-Lin Lee

Alzheimer’s disease (AD) is a common neurodegenerative disease characterized by continuous accumulation of β-amyloid (Aβ) in the brain. Deep ocean water (DOW) with rich inorganic salts and minerals was proven to promote fungi growth and metabolism. <i>Cordyceps cicada</i>, a functional food fungus, can produce higher anti-oxidant and anti-inflammatory compounds including adenosine, polysaccharide, and N(6)-(2-Hydroxyethyl) adenosine (HEA). This study used DOW as the culture water of <i>C. cicadae</i> NTTU 868 for producing DOW-cultured <i>C. cicadae</i> (DCC), and further investigated the effects and mechanisms on improving the memory deficit and repressing risk factors expressions in Aβ40 and streptozotocin (STZ)-induced Alzheimer’s disease rats model. In the results, DCC including mycelium and filtrate had adenosine, HEA, polysaccharide, and intracellular Mg²⁺ after fermentation with DOW. DCC had more effect on the improvement of memory deficit because it suppressed Aβ40 and streptozotocin (STZ) infusion caused BACE, pro-inflammatory factors expressions, and Aβ40 accumulation by increasing sRAGE expression in the brain. Furthermore, DCC enhanced the MAGT1 expression due to high organic magnesium, which can reverse Aβ40-induced cortex magnesium deficiency and further repress Aβ40 accumulation.

Nicolas Somers, Florian Jean, Marie Lasgorceix et al.

In this work, β-tricalcium phosphate (β-TCP) is doped with Mg2+ and Sr2+ in order to postpone the problematic β-TCP → α-TCP transition occurring from 1125 °C. Indeed, this phase transition occurs with a large lattice expansion during sintering causing microcracks and a reduced shrinkage leading to poor mechanical properties of ceramic parts. The substitution of calcium by cations like Mg2+ and Sr2+ allows to increase the temperature corresponding to β→α-TCP transition and therefore to increase the sintering temperature and achieve higher densification level. Three doping rates for each dopant individually (2.25, 4.50 and 9.00 mol%) and two co-doped compositions (2.00 mol% and 4.00 mol% of Mg2+ and Sr2+ simultaneously) were tested. Thermal and dilatometric analyses were used to evaluate the effects of Mg2+ and Sr2+ doping on the thermal stability of β-TCP. It has been shown that all doping, except the 2.25 mol% Sr-TCP, postpone the β→α transition. These results were confirmed after conventional and microwave sintering. Indeed, X-ray diffraction analyses of sintered pellets showed that the only phase present is β-TCP up to 1300 °C in all compositions except for the 2.25 mol% Sr-TCP with both sintering ways. Moreover, a higher densification rate is observed with the presence of dopants compared to undoped β-TCP according to the microstructures and relative densities close to 100%. Finally, the duration of microwave sintering is almost sixteen times shorter compared to conventional sintering allowing rapid densification with similar final relative densities and microstructures with finer grains.

Jin Quan Zhou, Wen Jin He

The industrial life cycle theory has proved to be helpful for describing the evolution of industries from birth to maturity. This paper is to highlight the historical evolution stage of Atlantic City's gambling industry in a structural framework covered by industrial market, industrial organization, industrial policies and innovation. Data mining was employed to obtain from local official documents, to verify the module of industrial life cycle in differential phases as introduction, development, maturity and decline. The trajectory of Atlantic City's gambling sector evolution reveals the process from the stages of introduction to decline via a set of variables describing structural properties of this industry such as product, market and organization of industry under a special industry environment in which industry recession as a result of theory of industry life cycle is a particular evidence be proved again. Innovation of the gambling industry presents the ongoing recovering process of the Atlantic City gambling industry enriches the theory of industrial life cycle in service sectors.

Joydip Sengupta

Nanomaterials have much improved properties compared to their bulk counterparts, which promotes them as ideal material for applications in various industries. Among the various nanomaterials, different nanoallotropes of carbon, namely fullerene, carbon nanotubes, and graphene, are the most important as indicated by the fact that their discoverers gained prestigious awards such as Nobel Prize or Kavli Prize. Carbon forms different nano-allotropes by varying the nature of orbital hybridization. Since all nanoallotropes of carbon possess exotic physical and chemical properties, they are extensively used in different applications, especially in the electronic industry.