Hasil untuk "Chemical industries"

S. Mussatto, E. Machado, S. Martins et al.

G. Vougioukalakis, R. Grubbs

Alexander Czaja, N. Trukhan, U. Müller

R. Sheldon

M. Manley

Near-infrared (NIR) spectroscopy has come of age and is now prominent among major analytical technologies after the NIR region was discovered in 1800, revived and developed in the early 1950s and put into practice in the 1970s. Since its first use in the cereal industry, it has become the quality control method of choice for many more applications due to the advancement in instrumentation, computing power and multivariate data analysis. NIR spectroscopy is also increasingly used during basic research performed to better understand complex biological systems, e.g. by means of studying characteristic water absorption bands. The shorter NIR wavelengths (800-2500 nm), compared to those in the mid-infrared (MIR) range (2500-15 000 nm) enable increased penetration depth and subsequent non-destructive, non-invasive, chemical-free, rapid analysis possibilities for a wide range of biological materials. A disadvantage of NIR spectroscopy is its reliance on reference methods and model development using chemometrics. NIR measurements and predictions are, however, considered more reproducible than the usually more accurate and precise reference methods. The advantages of NIR spectroscopy contribute to it now often being favoured over other spectroscopic (colourimetry and MIR) and analytical methods, using chemicals and producing chemical waste, such as gas chromatography (GC) and high performance liquid chromatography (HPLC). This tutorial review intends to provide a brief overview of the basic theoretical principles and most investigated applications of NIR spectroscopy. In addition, it considers the recent development, principles and applications of NIR hyperspectral imaging. NIR hyperspectral imaging provides NIR spectral data as a set of images, each representing a narrow wavelength range or spectral band. The advantage compared to NIR spectroscopy is that, due to the additional spatial dimension provided by this technology, the images can be analysed and visualised as chemical images providing identification as well as localisation of chemical compounds in non-homogenous samples.

W. Stark, Philipp R. Stoessel, W. Wohlleben et al.

N. Convery, N. Gadegaard

Abstract Microfluidics provides a great opportunity to create devices capable of outperforming classical techniques in biomedical and chemical research. In this review, the origins of this emerging field in the microelectronics industry are detailed. We also appraise how factors such as government funding influenced the development of new materials and fabrication techniques. Current applications of microfluidics are also examined and we highlight areas where work should be focussed in the future to ensure that the technology realises its full potential.

Talha Ahmad, Rana Muhammad Aadil, H. Ahmed et al.

Abstract Background Demand of dairy products is increasing in different countries, which results in the development of the dairy industry and increases in the generation of wastes. The main wastes generated are whey, dairy sludges and wastewater (processing, cleaning and sanitary). They have high nutrient concentration, biological oxygen demand (BOD), chemical oxygen demand (COD) and organic and inorganic contents. Furthermore, they can contain different sterilizing agents and a wide range of acid and alkaline detergents. Pollution due to dairy industry affects the air, soil and water quality. Scope and approach This review aims to describe the different methods used by the dairy industry to treat wastes, highlighting their effects on quality and efficiency removal of the pollution. Especially, it focusses on biotechnological alternatives to utilize the dairy wastes. Key findings and conclusions Physico-chemical, biological, and biotechnological methods can be used for treatment of dairy wastewaters. The physico-chemical treatment is used for reduction of milk fat and protein colloids, but it has the disadvantages of the high cost of the reagents and limited removal of COD. Biological treatments are used to remove organic material from dairy waste, however, the formation of sludge during aerobic biodegradation is a disadvantage. Aerobic and anaerobic process treatments can be used together in order to reach the effluents discharge limits for dairy wastewater. Biotechnological processes are the most recent alternatives, and can result in important products to the industries, such as whey-derived products, bioplastics, biofuels, bioenergy, organic acids, bioactive peptides, enzymes, among others.

Dennis U. Nielsen, Xin‐Ming Hu, K. Daasbjerg et al.

S. Dixit, A. Yadav, P. Dwivedi et al.

T. Keijer, V. Bakker, J. Slootweg

By expanding the scope of sustainability to the entire lifecycle of chemical products, the concept of circular chemistry aims to replace today’s linear ‘take–make–dispose’ approach with circular processes. This will optimize resource efficiency across chemical value chains and enable a closed-loop, waste-free chemical industry.

A. Das, M. Islam, Md. Omar Faruk et al.

Abstracts Tannins are found in most of the species throughout the plant kingdom, where their functions are to protect the plant against predation and might help in regulating the plant growth. There are two major groups of tannins, i.e., hydrolyzable and condensed tannins. The tannins are being used as important and effective chemicals for the tanning of animal hides in the leather processing industry since the beginning of the industry. Additionally, the tannins have been using as mineral absorption and protein precipitation purposes since 1960s. These are also used for iron gall ink production, adhesive production in wood-based industry, anti-corrosive chemical production, uranium recovering chemical from seawater, and removal of mercury and methylmercury from solution. Presently, tannins are considering as bioactive compound in nutrition science. It has also been considered for advanced applications, i.e., 3D printing and biomedical devices. The application of tannins as medicine is another new dimension in medical science. This paper outlines the general information about tannins followed by their extraction process. The utilization of tannins has also been presented in a broader scale. Depending on all these information, the article also describes the impending utilization of tannins for ensuring high-sustainability and better environmental performance.

Zaharia Carmen, Suteu Daniela

The residual dyes from different sources (e.g., textile industries, paper and pulp industries, dye and dye intermediates industries, pharmaceutical industries, tannery, and Kraft bleaching industries, etc.) are considered a wide variety of organic pollutants introduced into the natural water resources or wastewater treatment systems. One of the main sources with severe pollution problems worldwide is the textile industry and its dye-containing wastewaters (i.e. 10,000 different textile dyes with an estimated annual production of 7.105 metric tonnes are commercially available worldwide; 30% of these dyes are used in excess of 1,000 tonnes per annum, and 90% of the textile products are used at the level of 100 tonnes per annum or less) (Baban et al., 2010; Robinson et al., 2001; Soloman et al., 2009). 10-25% of textile dyes are lost during the dyeing process, and 2-20% are directly discharged as aqueous effluents in different environmental components. In particular, the discharge of dye-containing effluents into the water environment is undesirable, not only because of their colour, but also because many of dyes released and their breakdown products are toxic, carcinogenic or mutagenic to life forms mainly because of carcinogens, such as benzidine, naphthalene and other aromatic compounds (Suteu et al., 2009; Zaharia et al., 2009). Without adequate treatment these dyes can remain in the environment for a long period of time. For instance, the half-life of hydrolysed Reactive Blue 19 is about 46 years at pH 7 and 25°C (Hao et al., 2000). In addition to the aforementioned problems, the textile industry consumes large amounts of potable and industrial water (Tables 1, 2 and Fig. 1) as processing water (90-94%) and a relatively low percentage as cooling water (6-10%) (in comparison with the chemical industry where only 20% is used as process water and the rest for cooling). The recycling of treated wastewater has been recommended due to the high levels of contamination in dyeing and finishing processes (i.e. dyes and their breakdown products, pigments, dye intermediates, auxiliary chemicals and heavy metals, etc.) (Tables 3, 4 and 5) (adapted from Bertea A. and Bertea A.P., 2008; Bisschops and Spanjers, 2003; Correia et al., 1994; Orhon et al., 2001).

Sonal Rajoria, M. Vashishtha, V. Sangal

Yun-Wen Mao, Roman V. Krems

Efficient optimization of molecules with targeted properties remains a significant challenge due to the vast size and discrete nature of chemical compound space. Conventional machine-learning-based optimization approaches typically require large datasets to construct accurate surrogate models, limiting their applicability in data-scarce settings. In this study, we present a Bayesian optimization (BO) framework that identifies optimal molecular structures with high precision using fewer than 2,000 training data points within a chemical subspace containing more than 133,000 molecules. The framework employs a low-dimensional and physics-informed molecular descriptor vector that facilitates data-efficient surrogate modelling and optimization. A key innovation of the proposed framework is a reliable inverse mapping scheme that translates optimized points in the descriptor space back into chemically valid molecular structures, thereby bridging continuous optimization and discrete molecular design. We demonstrate the effectiveness of our approach on the QM9 benchmark dataset, where the framework successfully identifies organic molecules with the target entropy and zero-point vibrational energy (ZPVE) values.For entropy optimization, our approach achieves a 100% success rate while requiring fewer than 1,000 molecular evaluations in more than 80% of test cases. For ZPVE, the success rate exceeds 80% for molecules containing more than two heavy atoms. These results highlight the critical role of low-dimensional, interpretable descriptors in enabling data-efficient optimization and robust inverse molecular design, and establish Bayesian optimization as a practical tool for molecular discovery in small-data regimes.

M. Piryaee, H.R. Ghadami Karder, M. Khodaei

High-touch surfaces pose a significant risk of nosocomial infections, making the development of antibacterial surfaces crucial for inactivating bacteria. In this study, we engineered a one-pot anodic oxidation process incorporating CuO nanoparticles, resulting in a hierarchical porous microstructure. Energy-dispersive spectroscopy (EDS) confirmed the successful embedding of CuO nanoparticles on the anodized aluminum surface. Since the anodic oxidation of aluminum and CuO nanoparticle embedding occurred simultaneously, precise control over nanoparticle distribution was limited. Following stearic acid modification, the CuO-embedded hydrophobic aluminum surface achieved a water contact angle of 121° The antibacterial assay demonstrated a 68 % reduction in Escherichia coli colonies, for the stearic acid-modified CuO-embedded sample, highlighting the synergistic effect of hydrophobicity and CuO nanoparticles. This study presents a novel strategy for fabricating hydrophobic aluminum surfaces capable of disrupting bacterial colonies effectively. The one-pot, cost-effective approach underscores its suitability for industrial and practical applications.

Soichi Shirai, Takahiro Horiba, Hirotoshi Hirai

Quantum chemical calculations have attracted much attention as a practical application of quantum computing. Quantum computers can prepare superpositions of electronic states with various numbers of electrons on qubits. This special feature could be used to construct an efficient method for analyzing the structural variations of molecules and chemical reactions involving changes in molecular charge. The present work demonstrates a variational quantum eigensolver (VQE) algorithm based on a cost function ($L_{cost}$) having the same form as the grand potential of the grand canonical ensemble of electrons. The chemical potential of the electrons ($w$) is used as an input to these VQE calculations, whereas the molecular charge is not specified in advance but rather is a physical quantity that results from the calculations. Calculations involving model systems are carried out to show the viability of this new approach. Calculations for typical electron-donating and electron-accepting molecules using this technique yielded cationic, neutral or anionic species depending on the value of $w$. Models representing the adsorption of water or ammonia on copper-based catalysts predicted that oxidation would be associated with such adsorption. The molecular structures in which such reactions occurred were found to be dependent on the catalyst model, the adsorbed molecular species, and the value of $w$. These results arise because the electronic state that gives the lowest $L_{cost}$ value depends on the value of $w$ and the molecular structure. This behaviour was successfully simulated by the present VQE calculations.

Izumi Takahara, Kiyou Shibata, Teruyasu Mizoguchi

Crystal Orbital Overlap Population (COOP) is one of the effective tools for chemical-bonding analysis, and thus it has been utilized in the materials development and characterization. In this study, we developed a code to perform the COOP-based chemical-bonding analysis based on the wavefunction obtained from a first principles all-electron calculation with numeric atom-centered orbitals. The chemical-bonding analysis using the developed code was demonstrated for F2 and Si. Furthermore, we applied the method to analyze the chemical-bonding changes associated with a Li intercalation in three representative layered materials: graphite, MoS2, and ZrNCl, because of their great industrial importance, particularly for the applications in battery and superconducting materials. The COOP analysis provided some insights for understanding the intercalation mechanism and the stability of the intercalated materials from a chemical-bonding viewpoint.

Malik Hassanaly, Nicholas T. Wimer, Anne Felden et al.

The Quasi-Steady State Approximation (QSSA) can be an effective tool for reducing the size and stiffness of chemical mechanisms for implementation in computational reacting flow solvers. However, for many applications, stiffness remains, and the resulting model requires implicit methods for efficient time integration. In this paper, we outline an approach to formulating the QSSA reduction that is coupled with a strategy to generate C++ source code to evaluate the net species production rate, and the chemical Jacobian. The code-generation component employs a symbolic approach enabling a simple and effective strategy to analytically compute the chemical Jacobian. For computational tractability, the symbolic approach needs to be paired with common subexpression elimination which can negatively affect memory usage. Several solutions are outlined and successfully tested on a 3D multipulse ignition problem, thus allowing portable application across a chemical model sizes and GPU capabilities. The implementation of the proposed method is available at https://github.com/AMReX-Combustion/PelePhysics under an open-source license.

Tobias Dornheim, Michael Bonitz, Zhandos Moldabekov et al.

We present extensive new \emph{ab initio} path integral Monte Carlo (PIMC) simulation results for the chemical potential of the warm dense uniform electron gas (UEG), spanning a broad range of densities and temperatures. This is achieved by following two independent routes, i) based on the direct estimation of the free energy [Dornheim \emph{et al.}, arXiv:2407.01044] and ii) using a histogram estimator in PIMC simulations with a varying number of particles. We empirically confirm the expected inverse linear dependence of the exchange--correlation (XC) part of the chemical potential on the simulated number of electrons, which allows for a reliable extrapolation to the thermodynamic limit without the necessity for an additional finite-size correction. We find very good agreement (within $Δμ_\textnormal{xc}\lesssim0.5\%$) with the previous parametrization of the XC-free energy by Groth \emph{et al.}~[\emph{Phys.~Rev.~Lett.}~\textbf{119}, 135001 (2017)], which constitutes an important cross validation of current state-of-the-art UEG equations of state. In addition to being interesting in its own right, our study constitutes the basis for the future PIMC based investigation of the chemical potential of real warm dense matter systems starting with hydrogen.