Hasil untuk "Chemical industries"

S. Iravani, H. Korbekandi, Seyed Vahid Mirmohammadi et al.

Silver nanoparticles (NPs) have been the subjects of researchers because of their unique properties (e.g., size and shape depending optical, antimicrobial, and electrical properties). A variety of preparation techniques have been reported for the synthesis of silver NPs; notable examples include, laser ablation, gamma irradiation, electron irradiation, chemical reduction, photochemical methods, microwave processing, and biological synthetic methods. This review presents an overview of silver nanoparticle preparation by physical, chemical, and biological synthesis. The aim of this review article is, therefore, to reflect on the current state and future prospects, especially the potentials and limitations of the above mentioned techniques for industries.

Stéphanie Laurichesse, L. Avérous

A. Verma, R. Dash, P. Bhunia

A. Castañeda‐Ovando, M. D. L. Pacheco-Hernández, M. Páez-Hernández et al.

K. Hansen, Franziska Biegler, R. Ramakrishnan et al.

Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstrate prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the “holy grail” of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. In addition, the same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies.

J. Szargut, D. Morris, F. Steward

Xiang Wang, Lixin Huang, Caihong Zhang et al.

Starch has received research focus due to its low cost, excellent film-forming ability, bio-compatibility, extensive sources, renewability and biodegradability. However, native starch with relatively strong hydrophilicity greatly limits its application in industries. Therefore, in this paper, the recent research advances in chemical modifications of starch for hydrophobicity, e.g., esterification, etherification, crosslinking, grafting and condensing reaction etc., were discussed. The changes of hydrophobicity and other properties due to chemical modifications were described, as well. Different applications of modified starch with better hydrophobicity, i.e., packaging industries, Pickering emulsion and pharmaceutical, are presented, too. Finally, the future research and prospects on chemical modifications of starch for hydrophobicity and their applications are proposed.

K. Piaskowski, R. Świderska-Dąbrowska, P. Zarzycki

Synthetic dyes or colorants are key chemicals for various industries producing textiles, food, cosmetics, pharmaceutics, printer inks, leather, and plastics. Nowadays, the textile industry is the major consumer of dyes. The mass of synthetic colorants used by this industry is estimated at the level of 1 ÷ 3 × 105 tons, in comparison with the total annual consumption of around 7 × 105 tons worldwide. Synthetic dyes are relatively easy to detect but difficult to eliminate from wastewater and surface water ecosystems because of their aromatic chemical structure. It should be highlighted that the relatively high stability of synthetic dyes leads to health and ecological concerns due to their toxic, mutagenic, and carcinogenic nature. Currently, removal of such chemicals from wastewater involves various techniques, including flocculation/coagulation, precipitation, photocatalytic degradation, biological oxidation, ion exchange, adsorption, and membrane filtration. In this review, a number of classical and modern technologies for synthetic dye removal from industry-originated wastewater were summarized and discussed. There is an increasing interest in the application of waste organic materials (e.g., compounds extracted from orange bagasse, fungus biosorbent, or green algal biomasses) as effective, low-cost, and ecologically friendly sorbents. Moreover, a number of dye removal processes are based on newly discovered carbon nanomaterials (carbon nanotubes and graphene as well as their derivatives).

Cecilia Naveira-Pazos, María C. Veiga, Christian Kennes

The ability of <i>Rhodotorula toruloides</i> DSM 4444 to metabolize low-cost carbon sources such as fatty acids was comprehensively studied. This organism is shown, for the first time, to simultaneously accumulate microbial oils (biofuel precursors) and carotenoids from acetic acid obtained from CO₂ fermentation. This fatty acid is typically the single end product of acetogenic bioconversion of one-carbon gas pollutants (e.g., CO₂ and CO). In the first set of experiments, different aerobic fermentations were carried out in automated bioreactors, with acetic acid in one case and with glucose, a more conventional carbon source, as a control, in another bioreactor. <i>R. toruloides</i> consumed around 80 g/L substrate under both conditions. Maximum lipid content (27.2% g/g dry weight) was reached from 38 g/L glucose, while carotenoid content was higher with acetic acid (1.4 mg/g cell after 54.1 g/L acetic acid consumed), representing a 40% increase compared to glucose (1.0 mg/g cell after 64.2 g/L glucose consumed). Additionally, in the second set of assays, a fermented broth produced by <i>Acetobacterium woodii</i> from CO₂ fermentation, containing residual nutrients and metabolites, was tested. Despite its complex composition, <i>R. toruloides</i> grew and produced carotenoids (up to 0.141 mg/g), showing potential adaptability. To the best of our knowledge, this is the first report on a greenhouse gas-based biotechnological process as a promising sustainable alternative for the valorization of pollutants, e.g., gas emissions, their bioconversion to VFAs, such as acetic acid, and subsequent fermentation of the carboxylic acid into microbial oils, as a source of renewable energy, as well as carotenoids as a high-value nutraceutical product.

Monica Ferraris, Stefano De la Pierre, Valentina Casalegno et al.

Silicon carbide fiber-reinforced composites (SiC/SiC) are leading candidates to replace zirconium-based alloys as cladding in light water reactors (LWR), owing to their exceptional oxidation resistance and mechanical performance under accident conditions.However, pressure-less joining methods compatible with the extreme chemical and thermal environment of LWRs remain a major technological hurdle.This work evaluates two promising joining materials—Mo-wrap (a MoSi₂/Si composite) and SAY (a silica–alumina–yttria glass-ceramic)—under simulated LWR conditions.Joining was performed using both conventional furnaces and laser-assisted techniques.Joint integrity and microstructure were assessed by SEM/EDS and X-ray computed tomography. Hydrothermal stability was evaluated in static and flowing-water (loop) autoclaves up to 30 days at 330 °C and 150–155 bar.Mo-wrap joints showed partial degradation due to silicon dissolution, while SAY joints retained good structural integrity in static tests but suffered phase-selective corrosion under flowing conditions, with keivite emerging as the most stable crystalline phase.Laser-processed amorphous SAY joints exhibited improved corrosion resistance, though still limited under prolonged exposure.These findings advance the understanding of joining performance in nuclear-relevant environments and support the development of accident-tolerant fuel cladding.

Sophie Jaffard, Ivo F. Sbalzarini

We mathematically prove that chemical reaction networks without hidden layers can solve tasks for which spiking neural networks require hidden layers. Our proof uses the deterministic mass-action kinetics formulation of chemical reaction networks. Specifically, we prove that a certain reaction network without hidden layers can learn a classification task previously proved to be achievable by a spiking neural network with hidden layers. We provide analytical regret bounds for the global behavior of the network and analyze its asymptotic behavior and Vapnik-Chervonenkis dimension. In a numerical experiment, we confirm the learning capacity of the proposed chemical reaction network for classifying handwritten digits in pixel images, and we show that it solves the task more accurately and efficiently than a spiking neural network with hidden layers. This provides a motivation for machine learning in chemical computers and a mathematical explanation for how biological cells might exhibit more efficient learning behavior within biochemical reaction networks than neuronal networks.

Judith Wurmel, John M. Simmie

Tunnelling reactions of molecules embedded on cryogenic noble-gas matrices are being used in fundamental studies of how reactivity varies with the nature of the supposedly inert matrix as well as pointers to the chemistry occurring in the interstellar medium on ice-grains. To these ends we present chemical kinetic rate constants for the \textit{cis} to \textit{trans} isomerisation of seleno-, thio- and monomeric formic acids and that of their three dimeric species, based on multidimensional calculations in the gas-phase, from 10~K to 300~K as a guide to the matrix reactions.

Chuanyan Fan, Tieyan Chang, Longlong Fan et al.

The search for quantum spin liquids (QSL) and chemical doping in such materials to explore superconductivity have continuously attracted intense interest. Here, we report the discovery of a potential QSL candidate, pyrochlore-lattice beta-NaYbO2. Colorless and transparent NaYbO2 single crystals, layered alpha-NaYbO2 (~250 um on edge) and octahedral beta-NaYbO2 (~50 um on edge), were grown for the first time. Synchrotron X-ray single crystal diffraction unambiguously determined that the newfound beta-NaYbO2 belongs to the three-dimensional pyrochlore structure characterized by the R-3m space group, corroborated by synchrotron X-ray and neutron powder diffraction and pair distribution function. Magnetic measurements revealed no long-range magnetic order or spin glass behavior down to 0.4 K with a low boundary spin frustration factor of 17.5, suggesting a potential QSL ground state. Under high magnetic fields, the potential QSL state was broken and spins order. Our findings reveal that NaYbO2 is a fertile playground for studying novel quantum states.

Jill E. Johnston, L. Cushing

Bastian B. Brandt, Gergely Endrodi, G. Markó

We extend our measurement of the equation of state of isospin asymmetric QCD to small baryon and strangeness chemical potentials, using the leading order Taylor expansion coefficients computed directly at non-zero isospin chemical potentials. Extrapolating the fully connected contributions to vanishing pion sources is particularly challenging, which we overcome by using information from isospin chemical potential derivatives evaluated numerically. Using the Taylor coefficients, we present, amongst others, first results for the equation of state along the electric charge chemical potential axis, which is potentially of relevance for the evolution of the early Universe at large lepton flavour asymmetries.

Carolina Brito, Hans-Jürgen Butt, Alberto Giacomello

Wetting phenomena are widespread in both natural and technological contexts. Despite the well-established nature of this scientific field and our extensive knowledge of its underlying principles, wetting remains a dynamic and vibrant area of study. It continues to pose fundamental questions while offering innovative avenues for controlling these phenomena to develop novel applications. By tailoring the wetting properties of surfaces, researchers and engineers can design materials with specific functionalities, such as self-cleaning surfaces, anti-fog coatings, and enhanced slipperiness. Recent years have witnessed significant advancements in wetting research, owing to the exquisite control achieved in surface topography and chemistry and to the development of novel experimental techniques. Additionally, simulations and theory have played a crucial role in these advancements. They provid the fundamental knowledge and quantitative tools to control wettability and design surfaces with enhanced properties. Given these recent breakthroughs, this special collection Chemical Physics of Controlled Wettability and Super Surfaces becomes particularly timely and significant. It serves as a platform to showcase some of the latest developments in the field of wetting. It highlights the exciting progress and potential applications in controlling wetting properties that are enabled by the synergy between theory, simulations, and experiments.

Violeta Gallego-Rodríguez, Adrián Martínez-Bonilla, Nuria Rodríguez et al.

Microbial diversity that thrives in the deep subsurface remains largely unknown. In this work, we present the characterization of <i>Citrobacter</i> sp. T1.2D-1, isolated from a 63.6 m-deep core sample extracted from the deep subsurface of the Iberian Pyrite Belt (IPB). A genomic analysis was performed to identify genes that could be ecologically significant in the IPB. We identified all the genes that encoded the formate–hydrogen lyase and hydrogenase-2 complexes, related to hydrogen production, as well as those involved in glycerol fermentation. This is particularly relevant as some of the substrates and byproducts of this process are of industrial interest. Additionally, we conducted a phylogenomic study, which led us to conclude that our isolate was classified within the <i>Citrobacter telavivensis</i> species. Experimentally, we verified the strain’s ability to produce hydrogen from glucose and glycerol and, thus, of performing dark fermentation. Moreover, we assessed the activity of the nitrate and tetrathionate reductase complexes and the isolate’s ability to tolerate high concentrations of heavy metals, especially Zn. These results suggest that <i>C. telavivensis</i> T1.2D-1 can play a role in the carbon, hydrogen, iron, nitrogen, and sulfur cycles that occur in the deep subsurface of the IPB, making it a candidate worthy of further study for possible biotechnological applications.

M.F.B. Stodt, J. Kiefer, U. Fritsching

Flame spray pyrolysis (FSP) is a technique for the synthesis of metal oxide nanoparticles by combusting precursor solutions in a spray flame. The combustion of certain precursor solutions is known to lead to severe droplet disruptions (μ-explosions) in the spray flame that are linked to the synthesis of homogeneous and phase-pure nanoparticles. In this work, a broad spectrum of suitable subsonic operating conditions for the synthesis of iron oxide nanoparticles by FSP is investigated to understand the influence of the jet Reynolds number and turbulence on the onset of μ-explosions and droplet dynamics in spray flames. In order to enable a coherent comparison between differently operated spray flames using an iron(III) nitrate nonahydrate solution, the gas-to-liquid mass ratio and, hence, the oxygen/fuel ratio have been kept constant in order to identify the influence of flow conditions on the droplet dynamics. From the analysis of the droplet sizes in the spray and in the spray flame, it is found that in all combusting sprays, the droplet sizes convert from unimodal (after atomization) to bimodal droplet size distribution (DSD) due to the presence of μ-explosions. The occurrence and evolution of the bimodal DSD reveal that high jet Reynolds numbers result in narrower DSD and in a sharper separation of both DSD probability peaks (modal values). A straightforward 1-step kinematic model is presented to describe the conversion of unimodal to bimodal DSD considering the evaporation of droplets as well as the disruption of droplets to mimic the effect of μ-explosions. The temporal evolution of droplets in FSP is investigated by spatially resolved velocity data that reveal the formation of a temporal self-similarity. The resulting iron oxide nanoparticle size decreases with increasing jet Reynolds number. The turbulent mixing and residence times in the flame, primarily set by the jet Reynolds number, are identified as key design parameters for FSP.

Nana Li, Alei Geng, Zhuowei Tu et al.

The production of lactic acid (LA) from lignocellulosic biomass is an important route for the exploitation of renewable resources; nevertheless, effective LA production from this feedstock is challenged by several limitations, such as pentose and oligosaccharide utilization. In this study, a new strain, <i>Lactococcus</i> sp. X1, which is capable of fermenting glucose, xylose, and several disaccharides to produce L-lactic acid, was isolated from the gut of a wood-feeding termite, <i>Coptotermes formosanus</i>. Compared to conventional lactic acid bacteria, <i>Lactococcus</i> sp. X1 requires less complex nitrogen sources, which might in turn reduce the cost of LA production. In addition, <i>Lactococcus</i> sp. X1 was able to completely ferment 50 g/L of glucose within 3 days, giving a high LA yield of 99.9%, and its LA yield from 50 g/L of pretreated corncob reached up to 0.34 g/g substrates in the presence of a commercial cellulase. Strain X1 was also capable of excreting two kinds of nutritional factors, namely biotin and vitamin C, indicating its crucial role in the nourishment of the termite. In conclusion, <i>Lactococcus</i> sp. X1 is a new lactic acid bacterium, which may hold promise for application in cost-effective LA production as well as in the field of food additives.

Roman Remme, Tobias Kaczun, Maximilian Scheurer et al.

Orbital-free density functional theory (OF-DFT) holds the promise to compute ground state molecular properties at minimal cost. However, it has been held back by our inability to compute the kinetic energy as a functional of the electron density only. We here set out to learn the kinetic energy functional from ground truth provided by the more expensive Kohn-Sham density functional theory. Such learning is confronted with two key challenges: Giving the model sufficient expressivity and spatial context while limiting the memory footprint to afford computations on a GPU; and creating a sufficiently broad distribution of training data to enable iterative density optimization even when starting from a poor initial guess. In response, we introduce KineticNet, an equivariant deep neural network architecture based on point convolutions adapted to the prediction of quantities on molecular quadrature grids. Important contributions include convolution filters with sufficient spatial resolution in the vicinity of the nuclear cusp, an atom-centric sparse but expressive architecture that relays information across multiple bond lengths; and a new strategy to generate varied training data by finding ground state densities in the face of perturbations by a random external potential. KineticNet achieves, for the first time, chemical accuracy of the learned functionals across input densities and geometries of tiny molecules. For two electron systems, we additionally demonstrate OF-DFT density optimization with chemical accuracy.