Hasil untuk "Chemical industries"

Matthias Kellner, Jacob B. Holmes, Ruben Rodriguez-Madrid et al.

Nuclear Magnetic Resonance (NMR) chemical shifts are powerful probes of local atomic and electronic structure that can be used to resolve the structures of powdered or amorphous molecular solids. Chemical shift driven structure elucidation depends critically on accurate and fast predictions of chemical shieldings, and machine learning (ML) models for shielding predictions are increasingly used as scalable and efficient surrogates for demanding ab initio calculations. However, the prediction accuracies of current ML models still lag behind those of the DFT reference methods they approximate, especially for nuclei such as $^{13}$C and $^{15}$N. Here, we introduce ShiftML3.0, a deep-learning model that improves the accuracy of predictions of isotropic chemical shieldings in molecular solids, and does so while also predicting the full shielding tensor. On experimental benchmark sets, we find root-mean-squared errors with respect to experiment for ShiftML3.0 that approach those of DFT reference calculations, with RMSEs of 0.53 ppm for $^{1}$H, 2.4 ppm for $^{13}$C, and 7.2 ppm for $^{15}$N, compared to DFT values of 0.49 ppm, 2.3 ppm, and 5.8 ppm, respectively.

Haochen Zhang, Zengyu Yan, Hanxu Zhang et al.

The specific hybridization states of carbon atoms shape diamond and graphite, two well-known allotropes. Under specific conditions, diamond undergoes graphitization, resulting in distinct microstructural and macroscopic property changes. The graphitization of diamond has emerged as a core technology in many application-driven fields. This paper comprehensively reviewed studies on diamond graphitization across various domains to address urgent demands in these industries and the forthcoming semiconductor revolution. Beginning with the definition of diamond graphitization, the article explored its manifestations, thermodynamic and kinetic mechanisms, and influencing factors. The research on diamond graphitization was divided into three stages, focusing on fields such as heating/heat treatment, diamond tools/coatings, irradiation/ion implantation, dissolution/chemical etching, polishing, and simulation/emulation. The key distinctions between graphitization, amorphization, and oxidation were clarified, and the effects of temperature, pressure, atmosphere, and processing parameters on graphitization were summarized. The article introduced the functional application technology from the perspective of utilizing or suppressing diamond graphitization, followed by prospects for future developments in the field of diamond graphitization research.

Shen Fang, Siyi Zhang, Zeyu Li et al.

Reduction of detailed chemical reaction mechanisms is one of the key methods for mitigating the computational cost of reactive flow simulations. Exploitation of species and elementary reaction sparsity ensures the compactness of the reduced mechanisms. In this work, we propose a novel sparse statistical learning approach for chemical reaction mechanism reduction. Specifically, the reduced mechanism is learned to explicitly reproduce the dynamical evolution of detailed chemical kinetics, while constraining on the sparsity of the reduced reactions at the same time. Compact reduced mechanisms are be achieved as the collection of species that participate in the identified important reactions. We validate our approach by reducing oxidation mechanisms for $n$-heptane (194 species) and 1,3-butadiene (581 species). The results demonstrate that the reduced mechanisms show accurate predictions for the ignition delay times, laminar flame speeds, species mole fraction profiles and turbulence-chemistry interactions across a wide range of operating conditions. Comparative analysis with directed relation graph (DRG)-based methods and the state-of-the-art (SOTA) methods reveals that our sparse learning approach produces reduced mechanisms with fewer species while maintaining the same error limits. The advantages are particularly evident for detailed mechanisms with a larger number of species and reactions. The sparse learning strategy shows significant potential in achieving more substantial reductions in complex chemical reaction mechanisms.

Jun-Hyung Park, Yeachan Kim, Mingyu Lee et al.

Chemical representation learning has gained increasing interest due to the limited availability of supervised data in fields such as drug and materials design. This interest particularly extends to chemical language representation learning, which involves pre-training Transformers on SMILES sequences -- textual descriptors of molecules. Despite its success in molecular property prediction, current practices often lead to overfitting and limited scalability due to early convergence. In this paper, we introduce a novel chemical language representation learning framework, called MolTRES, to address these issues. MolTRES incorporates generator-discriminator training, allowing the model to learn from more challenging examples that require structural understanding. In addition, we enrich molecular representations by transferring knowledge from scientific literature by integrating external materials embedding. Experimental results show that our model outperforms existing state-of-the-art models on popular molecular property prediction tasks.

Yowe Kidwe, Djakba Raphaël, Wangmene Bagamla et al.

Compatible and environmentally clean activated carbon material was prepared via physicochemical method and used for harmful pollutant removal from aqueous solution. The performance of the pristine cottonseed cakes and its activated carbon was examined towards copper ions removal as targeted pollutant through adsorption process. The physicochemical properties of adsorbents were evaluated by numerous experimental techniques such as Fourier transform infra-red spectroscopy, Raman spectroscopy, scanning electron microscopy, the point of zero charge, iodine number and specific surface area. The effect of several key operational parameters such as contact time, adsorbent dose, pH, concentration and temperature were considered. Results of the adsorption tests exhibited significant sensitivity towards copper ions elimination at optimum conditions; the copper uptake capacity was enhanced with time up to equilibrium of 30 min with a minimum adsorbent dose of 0.1 g at alkaline pH of 10 for maximum concentration of 50 mg/L at room temperature (25 °C) and achieved appropriate adsorbed quantities of 51.56 mg/g for cottonseed cakes activated carbon (CCAC) and 48.5 mg/g for cottonseed cakes biosorbent (CCB). The values of point of zero charge are 2.63 and 6.32 for CCB and CCAC respectively which present high electrostatic attraction between positive charge of copper ions and negative charge of the surface at basic medium. Iodine number of 30.35 and 41.92 mg/g indicates random distribution of micropores. The specific surface area of CCAC (30.35 m2/g) is higher than the one of CCB (11.94 m2/g). FTIR shows good surface chemistry with various functional groups while Raman spectroscopy and SEM analyses revealed myriad morphological features and carbon phases (graphite and diamond). The adsorption of copper ions was described by pseudo second order kinetic model and favoured by Redlich Peterson isotherm corresponding to physisorption on CCB while the one CCAC involves chemical bonding and can be qualified as chemisorption mechanism as confirm by ΔH° of both materials.

Mahboobeh Majnouni, Mahmoud Reza Sohrabi, Saeid Mortazavinik

In this study, chitosan (CS) stabilized bimetallic zero-valent iron (ZVI)-nickel (CS-nZVI-Ni) nanoparticles were synthesized for the removal of Direct Blue 86 (DB 86) from aqueous media. Analysis of the CS-nZVI-Ni characteristics was investigated by scanning electron microscope, energy dispersive X-ray analysis, Fourier transform infrared spectroscopy, X-ray diffraction, and Brunauer-Emmett-Teller methods. The effective experimental parameters, including pH of the solution, contact time, adsorbent dosage, and initial dye concentration were evaluated to obtain optimum conditions. Results disclosed that the maximum removal values were related to the pH of 4, an adsorbent dosage of 0.2 g, contact time of 15 min, and a dye concentration of 20 mg/L. Experimental data were fitted to the Freundlich isotherm and Pseudo-second-order with the coefficient of determination (R2) equal to 0.9991 and 0.9994, respectively. The maximum adsorption capacity (qmax) was 61.72 mg/g from the Langmuir isotherm. All these results prove that prepared CS-nZVI-Ni could be considered as an adsorbent with high efficiency and cost-effectiveness for dye removal and as an alternative to other adsorbents.

Miheer Dinesh Kadam, Prakash M. Gore, Balasubramanian Kandasubramanian

Fiber-based thermoelectric generators are a versatile development in the field of power generation that has numerous applications in waste heat recovery and wearable technology. They differ from conventional thermoelectric generators, due to their flexible nature, which allows them to conform to the shapes of irregular surfaces and efficiently convert thermal energy to electrical energy, however, they retain the merits of reliability and longevity of their conventional counterparts. Their flexibility comes from the substrate onto which the thermoelectric materials are coated, several materials have been used as substrates, and of these materials, some have been more suitable for use than others. This review aims to assess the properties of some of these materials, their shapes, as well as material treatment methods that if implemented would enhance substrate surface properties by up to 139%, as well as changes to the shape and structure of fibers which would significantly enhance the thermoelectric performance and the longevity of fiber-based thermoelectric generators.

E. Aneggi, V. Rico-Pérez, C. Leitenburg et al.

Shixin Xu, Robert Eisenberg, Zilong Song et al.

Chemical reactions involve the movement of charges, and this work presents a mathematical model for describing chemical reactions in electrolytes. The model is developed using an energy variational method that aligns with classical thermodynamics principles. It encompasses both electrostatics and chemical reactions within consistently defined energetic and dissipative functionals. Furthermore, the energy variation method is extended to account for open systems that involve the input and output of charge and mass. Such open systems have the capability to convert one form of input energy into another form of output energy. In particular, a two-domain model is developed to study a reaction system with self-regulation and internal switching, which plays a vital role in the electron transport chain of mitochondria responsible for ATP generation crucial process for sustaining life. Simulations are conducted to explore the influence of electric potential on reaction rates and switching dynamics within the two-domain system. It shows that the electric potential inhibits the oxidation reaction while accelerating the reduction reaction.

Matthew S. Robinson, Jochen Küpper

In this perspective, we discuss how one can initiate, image, and disentangle the ultrafast elementary steps of thermal-energy chemical dynamics, building upon advances in technology and scientific insight. We propose that combinations of ultrashort mid-infrared laser pulses, controlled molecular species in the gas phase, and forefront imaging techniques allow to unravel the elementary steps of general-chemistry reaction processes in real time. We detail, for prototypical first reaction systems, experimental methods enabling these investigations, how to sufficiently prepare and promote gas-phase samples to thermal-energy reactive states with contemporary ultrashort mid-infrared laser systems, and how to image the initiated ultrafast chemical dynamics. The results of such experiments will clearly further our understanding of general-chemistry reaction dynamics.

Nazanin Hoghooghi, Peter Chang, Scott Egbert Matt Burch et al.

Molecular diagnostics are a primary tool of modern chemistry, enabling researchers to map chemical reaction pathways and rates to better design and control chemical systems. Many chemical reactions are complex and fast, and existing diagnostic approaches provide incomplete information. For example, mass spectrometry is optimized to gather snapshots of the presence of many chemical species, while conventional laser spectroscopy can quantify a single chemical species through time. Here we optimize for multiple objectives by introducing a high-speed and broadband, mid-infrared dual frequency comb absorption spectrometer. The optical bandwidth of >1000 cm-1 covers absorption fingerprints of many species with spectral resolution <0.03 cm-1 to accurately discern their absolute quantities. Key to this advance are 1 GHz pulse repetition rate frequency combs covering the 3-5 um region that enable microsecond tracking of fast chemical process dynamics. We demonstrate this system to quantify the abundances and temperatures of each species in the complete reactants-to-products breakdown of 1,3,5-trioxane, which exhibits a formaldehyde decomposition pathway that is critical to modern low temperature combustion systems. By maximizing the number of observed species and improving the accuracy of temperature and concentration measurements, this spectrometer advances understanding of chemical reaction pathways and rates and opens the door for novel developments such as combining high-speed chemistry with machine learning.

Moritz Bensberg, Markus Reiher

Exploring large chemical reaction networks with automated exploration approaches and accurate quantum chemical methods can require prohibitively large computational resources. Here, we present an automated exploration approach that focuses on the kinetically relevant part of the reaction network by interweaving (i) large-scale exploration of chemical reactions, (ii) identification of kinetically relevant parts of the reaction network through microkinetic modeling, (iii) quantification and propagation of uncertainties, and (iv) reaction network refinement. Such an uncertainty-aware exploration of kinetically relevant parts of a reaction network with automated accuracy improvement has not been demonstrated before in a fully quantum mechanical approach. Uncertainties are identified by local or global sensitivity analysis. The network is refined in a rolling fashion during the exploration. Moreover, the uncertainties are considered during kinetically steering of a rolling reaction network exploration. We demonstrate our approach for Eschenmoser-Claisen rearrangement reactions. The sensitivity analysis identifies that only a small number of reactions and compounds are essential for describing the kinetics reliably, resulting in efficient explorations without sacrificing accuracy and without requiring prior knowledge about the chemistry unfolding.

Sihao Yuan, Xu Han, Jun Zhang et al.

Theoretical studies on chemical reaction mechanisms have been crucial in organic chemistry. Traditionally, calculating the manually constructed molecular conformations of transition states for chemical reactions using quantum chemical calculations is the most commonly used method. However, this way is heavily dependent on individual experience and chemical intuition. In our previous study, we proposed a research paradigm that uses enhanced sampling in molecular dynamics simulations to study chemical reactions. This approach can directly simulate the entire process of a chemical reaction. However, the computational speed limits the use of high-precision potential energy functions for simulations. To address this issue, we present a scheme for training high-precision force fields for molecular modeling using a previously developed graph-neural-network-based molecular model, molecular configuration transformer. This potential energy function allows for highly accurate simulations at a low computational cost, leading to more precise calculations of the mechanism of chemical reactions. We applied this approach to study a Claisen rearrangement reaction and a Carbonyl insertion reaction catalyzed by Manganese.

Afqir Mohamed, Fasquelle Didier, Tachafine Amina et al.

Lu-doped SrBi2Ta2O9 (SrBi2-xLuxTa2O9 where x = 0, 0.025, 0.05, 0.75 and 0.1) powders were synthesized by combination of molten salt method and solid-state route. FTIR, Raman and XRD techniques were performed to follow the transformation of reactants into the desired products. Characterization of all samples shows pure and single-phase orthorhombic structured materials obtained with plate-like morphology that is composed of fine and coarse-grained particles. The prepared powders were pressed and sintered at different temperatures up to 1200°C. Microstructure of the sintered samples is also likely to be affected by doping. The first study of dielectric measurements describes the effect of the application of DC bias, at roomtemperature, on the undoped and Lu-doped ceramics and shows that there is little or no effect of DC bias. The sample SrBi1.95Lu0.05Ta2O9 had maximal dielectric constant (ε′) and minimal dielectric loss (tanδ). In the second part of this work, the temperature dependence of ε′ and tan_ was considered. It was concluded that Lu-doping not only reduces the Curie temperature, but also brings a diffused phase transition, showing a crossover between displacive and diffusive behaviour.

Mohammadamin Tavakoli, Alexander Shmakov, Francesco Ceccarelli et al.

It is fundamental for science and technology to be able to predict chemical reactions and their properties. To achieve such skills, it is important to develop good representations of chemical reactions, or good deep learning architectures that can learn such representations automatically from the data. There is currently no universal and widely adopted method for robustly representing chemical reactions. Most existing methods suffer from one or more drawbacks, such as: (1) lacking universality; (2) lacking robustness; (3) lacking interpretability; or (4) requiring excessive manual pre-processing. Here we exploit graph-based representations of molecular structures to develop and test a hypergraph attention neural network approach to solve at once the reaction representation and property-prediction problems, alleviating the aforementioned drawbacks. We evaluate this hypergraph representation in three experiments using three independent data sets of chemical reactions. In all experiments, the hypergraph-based approach matches or outperforms other representations and their corresponding models of chemical reactions while yielding interpretable multi-level representations.

Martin Šípka, Andreas Erlebach, Lukáš Grajciar

On the time scales accessible to atomistic numerical modelling, chemical reactions are considered rare events. Atomistic simulations are typically biased along a low-dimensional representation of a chemical reaction in an atomic structure space, i.e., along the collective variable, to accelerate sampling of these improbable events. However, suitable collective variables are often complicated to guess due to the complexity of the transitions. Therefore, we present an automatic method of generating robust collective variables from atomic representation vectors, using either fixed Behler-Parrinello functions or representations extracted from pre-trained machine learning potentials. Variational autoencoder with these representations as inputs is trained while its latent space with arbitrary dimension gives us the set of collective variables. The resulting collective variables inherit all necessary invariances from the atomic representations and can be trained entirely unsupervised. The method's effectiveness is demonstrated using three different chemical reactions, one being the complex hydrolysis of a heterogeneous aluminosilicate catalyst. Lastly, we consider the method in the context of unseen atomic structure prediction, efficiently creating structures for different values of collective variables in a generative model fashion.

Jaewon Byun, Jeehoon Han

Food waste (FW) has traditionally been disposed by incineration or landfilling; however, it can be converted to green methane (GM) via anaerobic digestion, and GM can be used as fuel for light-duty natural gas vehicles (LDNGVs). A lifecycle assessment (LCA) of FW-based GM production and LDNGV operation in China, a new scenario, was performed. The LCA results were compared with those for the conventional FW treatment, where a “well-to-wheel” system boundary including FW collection, GM production from FW, and vehicle manufacturing, operation, and disposal was defined. The LCA results showed that the global warming impacts of the new FW scenario are 44.3% lower than those of the conventional option. The fine particulate matter formation impact of the new FW scenario was dominated by the displacement effect of electricity supply to anaerobic digestion, followed by CO₂ adsorption by the primary source. The sensitivity analysis showed that hydroelectric power as the best primary source for electricity supply could substantially reduce both global warming and FRS in the new scenario. In the short term, the proposed FW scenario could be a feasible option for achieving sustainable society by minimizing environmental impacts of FW treatment.

Juntao Huang, Yizhou Zhou, Wen-An Yong

In this paper, we propose a data-driven method to discover multiscale chemical reactions governed by the law of mass action. First, we use a single matrix to represent the stoichiometric coefficients for both the reactants and products in a system without catalysis reactions. The negative entries in the matrix denote the stoichiometric coefficients for the reactants and the positive ones for the products. Second, we find that the conventional optimization methods usually get stuck in the local minima and could not find the true solution in learning the multiscale chemical reactions. To overcome this difficulty, we propose a partial-parameters-freezing (PPF) technique to progressively determine the network parameters by using the fact that the stoichiometric coefficients are integers. With such a technique, the dimension of the searching space is gradually reduced in the training process and the global mimina can be eventually obtained. Several numerical experiments including the classical Michaelis-Menten kinetics, the hydrogen oxidation reactions, and the simplified GRI-3.0 mechanism verify the good performance of our algorithm in learning the multiscale chemical reactions. The code is available at \url{https://github.com/JuntaoHuang/multiscale-chemical-reaction}.

Tetsuya J. Kobayashi, Dimitri Loutchko, Atsushi Kamimura et al.

The theory of chemical kinetics form the basis to describe the dynamics of chemical systems. Owing to physical and thermodynamic constraints, chemical reaction systems possess various structures, which can be utilized to characterize important physical properties of the systems. In this work, we reveal the Hessian geometry which underlies chemical reaction systems and demonstrate how it originates from the interplay of stoichiometric and thermodynamic constraints. Our derivation is based on kinetics, we assume the law of mass action and characterize the equilibrium states by the detailed balance condition. The obtained geometric structure is then related to thermodynamics via the Hessian geometry appearing in a pure thermodynamic derivation. We demonstrate, based on the fact that both equilibrium and complex balanced states form toric varieties, how the Hessian geometric framework can be extended to nonequilibrium complex balanced steady states. We conclude that Hessian geometry provides a natural framework to capture the thermodynamic aspects of chemical reaction kinetics.

Kevin Fiederling, Mostafa Abasifard, Martin Richter et al.

Lately, experimental evidence of unexpectedly extremely high spatial resolution of tip-enhanced Raman scattering (TERS) has been demonstrated. Theoretically, two different contributions are discussed: an electromagnetic effect, leading to a spatially confined near field due to plasmonic excitations; and the so-called chemical effect originating from the locally modified electronic structure of the molecule due to the close proximity of the plasmonic system. Most of the theoretical efforts have concentrated on the electromagnetic contribution or the chemical effect in case of non-resonant excitation. In this work, we present a fully quantum mechanical description including non-resonant and resonant chemical contributions as well as charge-transfer phenomena of these molecular-plasmonic hybrid system at the density functional and the time-dependent density functional level of theory. We consider a surface-immobilized tin(II) phthalocyanine molecule as the molecular system, which is minutely scanned by a plasmonic tip, modeled by a single silver atom. These different relative positions of the Ag atom to the molecule lead to pronounced alterations of the Raman spectra. These Raman spectra vary substantially, both in peak positions and several orders of magnitude in the intensity patterns under non-resonant and resonant conditions, and also, depending on, which electronic states are addressed. Our computational approach reveals that unique - non-resonant and resonant - chemical interactions among the tip and the molecule significantly alter the TERS spectra and are mainly responsible for the high, possibly sub-Angstrom spatial resolution.