Hasil untuk "Chemical technology"

M. Kakihana

R. Datta, S. Tsai, P. Bonsignore et al.

YUE Yan, LI Yu, ZHOU Xianxian et al.

[Purposes] Because of the high charging overpotential and lagging electrochemical reaction kinetics caused by the low electronic conductivity of Li2O2 in Li-O2 batteries, it is important to develop cathode catalysts with high activity. [Methods] By coating nitrogen-doped molybdenum disulfide ultra-thin nanosheets on the surface of nitrogen-doped carbon nanotubes, the N-MoS2/N-CNTs composite was prepared through hydrothermal method combined with ammonia annealing method. The morphology, surface element state, and Li-O2 battery electrochemical performance of N-MoS2/N-CNTs were characterized by X-ray diffraction, scanning electron microscopy, X-ray photoelectron spectroscopy, and electrochemical tests. [Results] The cathode obtains high initial charge/discharge capacity (7909/10015 mAh g-1), low charging overpotential, and high catalytic activity. Moreover, the performance of Li-O2 battery is further improved at large O2 mass transfer area. According to electrochemical reaction engineering, it is proposed that the possible initial discharge reaction interface is electrode/Li2O2 interface, and the charging reaction interface is electrode/electrolyte/Li2O2 interface. Three overpotential theories are used to explain the capacity and rate performance improvement mechanism of N-MoS2/N-CNTs cathode Li-O2 batteries, which is the decrease of electrochemical reaction overpotential (ηR) providing more space for the increase of concentration overpotential (ηC).

The CMS collaboration, A. Tumasyan, W. Adam et al.

Sundas Khalid, Rafay Naseer, Aqsa Zaheen et al.

The present study focuses on developing novel gradient thin films for surface-based magnetic resonance imaging of fluids such as water. Four types of magnetic-polymer colloids were investigated as T2 contrast agents, including Janus magnetic-polystyrene and core–shell magnetic-poly(styrene/divinylbenzene/methacrylic acid) particles. These colloids were coated with amino dextran to enhance their performance. Key factors such as emulsion composition, particle size, and surface properties were systematically examined. Gradient thin films were fabricated on glass slides using a layer-by-layer self-assembled multilayer (LbL-SAMu) technique. The films consisted of positively charged poly(dimethyl diallyl ammonium chloride) and negatively charged magnetic-polymer colloids. The developed colloids and thin films were characterized by their surface wettability, surface morphology, and zeta potential. These films exhibited relatively improved hydrophilicity and T2 contrast. The utilization of such gradient thin films as molecular probes could enhance clinical MRI for in vitro diagnosis. This study indicated that thin-film gradients can offer a facile technique for unique cellular imaging via a lab-on-chip device to enable effective point-of-care molecular diagnostics.

Zhanpeng Chen, Weihao Gao, Shunyu Wang et al.

Generating precise 3D molecular geometries is crucial for drug discovery and material science. While prior efforts leverage 1D representations like SELFIES to ensure molecular validity, they fail to fully exploit the rich chemical knowledge entangled within 1D models, leading to a disconnect between 1D syntactic generation and 3D geometric realization. To bridge this gap, we propose MolSculpt, a novel framework that "sculpts" 3D molecular geometries from chemical syntax. MolSculpt is built upon a frozen 1D molecular foundation model and a 3D molecular diffusion model. We introduce a set of learnable queries to extract inherent chemical knowledge from the foundation model, and a trainable projector then injects this cross-modal information into the conditioning space of the diffusion model to guide the 3D geometry generation. In this way, our model deeply integrates 1D latent chemical knowledge into the 3D generation process through end-to-end optimization. Experiments demonstrate that MolSculpt achieves state-of-the-art (SOTA) performance in \textit{de novo} 3D molecule generation and conditional 3D molecule generation, showing superior 3D fidelity and stability on both the GEOM-DRUGS and QM9 datasets. Code is available at https://github.com/SakuraTroyChen/MolSculpt.

M. G. Fontana, R. Staehle

Zhi-Song Liu, Petri Clusius, Michael Boy

Modeling atmospheric chemistry is complex and computationally intense. Given the recent success of Deep neural networks in digital signal processing, we propose a Neural Network Emulator for fast chemical concentration modeling. We consider atmospheric chemistry as a time-dependent Ordinary Differential Equation. To extract the hidden correlations between initial states and future time evolution, we propose ChemNNE, an Attention based Neural Network Emulator (NNE) that can model the atmospheric chemistry as a neural ODE process. To efficiently simulate the chemical changes, we propose the sinusoidal time embedding to estimate the oscillating tendency over time. More importantly, we use the Fourier neural operator to model the ODE process for efficient computation. We also propose three physical-informed losses to supervise the training optimization. To evaluate our model, we propose a large-scale chemical dataset that can be used for neural network training and evaluation. The extensive experiments show that our approach achieves state-of-the-art performance in modeling accuracy and computational speed.

Jiachen Li, Weitao Yang

We develop a functional derivative approach to calculate the chemical potentials of the second-order perturbation theory (MP2). In the functional derivative approach, the correlation part of the MP2 chemical potential, which is the derivative of the MP2 correlation energy with respect to the occupation number of frontier orbitals, is obtained from the chain rule via the non-interacting Green's function. First, the MP2 correlation energy is expressed in terms of the non-interacting Green's function and its functional derivative to the non-interacting Green's function is the second-order self-energy. Then the derivative of the non-interacting Green's function to the occupation number is obtained by including the orbital relaxation effect. We show that the MP2 chemical potentials obtained from the functional derivative approach agrees with that obtained from the finite difference approach. The one-electron Hamiltonian, defined as the derivative of the MP2 energy with respect to the one particle density matrix, is also derived using the functional derivative approach, which can be used in the self-consistent calculations of MP2 and double-hybrid density functionals. The developed functional derivative approach is promising for calculating the chemical potentials and the one-electron Hamiltonian of approximate functionals and many-body perturbation approaches dependent explicitly on the non-interacting Green's function.

Arif Ullah, Yuxinxin Chen, Pavlo O. Dral

The field of computational chemistry is increasingly leveraging machine learning (ML) potentials to predict molecular properties with high accuracy and efficiency, providing a viable alternative to traditional quantum mechanical (QM) methods, which are often computationally intensive. Central to the success of ML models is the quality and comprehensiveness of the data sets on which they are trained. Quantum chemistry data sets and databases, comprising extensive information on molecular structures, energies, forces, and other properties derived from QM calculations, are crucial for developing robust and generalizable ML potentials. In this review, we provide an overview of the current landscape of quantum chemical data sets and databases. We examine key characteristics and functionalities of prominent resources, including the types of information they store, the level of electronic structure theory employed, the diversity of chemical space covered, and the methodologies used for data creation. Additionally, an updatable resource is provided to track new data sets and databases at https://github.com/Arif-PhyChem/datasets_and_databases_4_MLPs. Looking forward, we discuss the challenges associated with the rapid growth of quantum chemical data sets and databases, emphasizing the need for updatable and accessible resources to ensure the long-term utility of them. We also address the importance of data format standardization and the ongoing efforts to align with the FAIR principles to enhance data interoperability and reusability. Drawing inspiration from established materials databases, we advocate for the development of user-friendly and sustainable platforms for these data sets and databases.

C. Wyman

Jocimar Costa Rosa, Renan Santos Uhdre, Marcos Ventura Faria et al.

This study aimed to apply, in unprecedented depth, a Bayesian approach to the non-linear regression model developed by Toler for evaluating the stability and adaptability of genotypes. Twenty-five soybean cultivars were evaluated in twenty-one plots across the midwestern of Brazil. A complete block design was employed, with three replications. The evaluated variable was grain yield. The proposed methodology was implemented in the R program by means of the BRugs package. The methodology was capable of differentiating the effect of the environment on soybean cultivars in terms of yield in the different environments, allowing exploration of the response of each genotype to environmental variations. Cultivars 6266RSF, NS6990, GD19I435, GD19I439, GD19C443, RC0496 and IA18661 presented good stability and general adaptability, being the most recommended for future evaluations. The other cultivars presented specific adaptability and high responsiveness to unfavorable environments.

Sheela Katuwal, Nur-Al-Sarah Rafsan, Amanda J. Ashworth et al.

Poultry litter is a useful product as a fertilizer, energy feedstock for thermochemical conversion, and a precursor for synthesis of adsorbents and catalysts. Detailed characterization of baseline properties is necessary for enhanced environmental and economic utilization of this valuable resource. Baseline physicochemical characterization was carried out at two broiler production facilities (Arkansas, PL1, and North Carolina, PL2). Greater concentrations of inorganic nitrogen, phosphorus, and potassium were obtained for PL1, suggesting greater nutrient value compared to PL2. PL2 had greater carbon content and water-holding capacity than PL1. X-ray photoelectron spectroscopy (XPS) of PL1 and PL2 indicated a similarity between litters in terms of the presence of carbon, nitrogen, and oxygen bonds. Both poultry litters had oxygen, nitrogen, sulfur, and phosphorous functional groups, as confirmed by infrared spectroscopy. Time of flight - secondary ion mass spectroscopy of negative ions also indicated similarity of the surface charge distribution between PL1 and PL2. Overall, poultry litters evaluated had similar surface chemistries, with nutrient composition varying based on rearing conditions, which has implications for downstream use in thermochemical conversion and other value-added products.

Hans-Christian Herbig

I present a universal method, called pivotal condensation, for calculating stoichiometric factors of chemical reactions. It can be done by hand, even for rather complicated reactions. The main trick, which I call kernel pivotal condensation (ker pc), to calculate the kernel of a matrix might be of independent interest. The discussion is elaborated for matrices with entries in a principal ideal domain $R$. The ker pc calculates a basis with coefficients in $R$ for the kernel of a matrix, seen as e $Q$-vector space, where $Q$ is the quotient field of $R$. If $W$ is a free saturated $R$-submodule of $R^n$ I address the question how to modify an $R$-basis of the $Q$-vector subspace $Q\otimes _R W$ over the quotient field $Q$ to obtain a basis of the $R$-module $W$. I also indicate how one can solve inhomogeneous linear systems, invert matrices and determine the four subspaces using pivotal condensation. I formulate the balancing by inspection method that is widely used to reduce the size of a linear system arising in chemical balancing in mathematical language.

Raisa-Ioana Biega, Yinan Chen, Marina R. Filip et al.

Halide double perovskites are an emerging class of semiconductors with tremendous chemical and electronic diversity. While their bandstructure features can be understood from frontier-orbital models, chemical intuition for optical excitations remains incomplete. Here, we use \textit{ab initio} many-body perturbation theory within the $GW$ and the Bethe-Salpeter Equation approach to calculate excited-state properties of a representative range of Cs$_2$BB$'$Cl$_6$ double perovskites. Our calculations reveal that double perovskites with different combinations of B and B$'$ cations display a broad variety of electronic bandstructures and dielectric properties, and form excitons with binding energies ranging over several orders of magnitude. We correlate these properties with the orbital-induced anisotropy of charge-carrier effective masses and the long-range behavior of the dielectric function, by comparing with the canonical conditions of the Wannier-Mott model. Furthermore, we derive chemically intuitive rules for predicting the nature of excitons in halide double perovskites using electronic structure information obtained from computationally inexpensive DFT calculations.

Jie Li, Jiashu Liang, Zhe Wang et al.

Theoretical predictions of NMR chemical shifts from first-principles can greatly facilitate experimental interpretation and structure identification. However, accurate prediction of chemical shifts using the best coupled cluster methods can be prohibitively expensive for systems larger than ten to twenty non-hydrogen atoms on today's computers. By contrast machine learning methods offer inexpensive alternatives but are hampered by generalization to molecules outside the original training set. Here we propose a novel machine learning feature representation informed by intermediate calculations of atomic chemical shielding tensors within a molecular environment using an inexpensive quantum mechanics method, and training it to predict NMR chemical shieldings of a high-level composite theory that is comparable to CCSD(T) in the complete basis set limit. The inexpensive shift machine learning (iShiftML) algorithm is trained through a new progressive active learning workflow that reduces the total number of expensive calculations required when constructing the dataset, while allowing the model to continuously improve on data it has never seen. Furthermore, we show that the error estimations from our model correlate quite well with actual errors to provide confidence values on new predictions. We illustrate the predictive capacity of iShiftML across gas phase experimental chemical shifts for small organic molecules and much larger and more complex natural products in which we can accurately differentiate between subtle diastereomers based on chemical shift assignments.

Jordan Peccia, P. Westerhoff

K. Liu, Chunshan Song, V. Subramani

J.F.B. De São José, N. J. Andrade, A. M. Ramos et al.

Chong Wang, Jian-Hao Lu, An-Bang Wang et al.

The shuttling effect of soluble lithium polysulfides (LiPSs) and the sluggish conversion kinetics of polysulfides into insoluble Li₂S₂/Li₂S severely hinders the practical application of Li-S batteries. Advanced catalysts can capture and accelerate the liquid–solid conversion of polysulfides. Herein, we try to make use of bismuth tantalum oxide with oxygen vacancies as an electrocatalyst to catalyze the conversion of LiPSs by reducing the sulfur reduction reaction (SRR) nucleation energy barrier. Oxygen vacancies in Bi₄TaO₇ nanoparticles alter the electron band structure to improve instinct electronic conductivity and catalytic activity. In addition, the defective surface could provide unsaturated bonds around the vacancies to enhance the chemisorption capability with LiPSs. Hence, a multidimensional carbon (super P/CNT/Graphene) standing sulfur cathode is prepared by coating oxygen vacancies Bi₄TaO_7−x nanoparticles, in which the multidimensional carbon (MC) with micropores structure can host sulfur and provide a fast electron/ion pathway, while the outer-coated oxygen vacancies with Bi₄TaO_7−x with improved electronic conductivity and strong affinities for polysulfides can work as an adsorptive and conductive protective layer to achieve the physical restriction and chemical immobilization of lithium polysulfides as well as speed up their catalytic conversion. Benefiting from the synergistic effects of different components, the S/C@Bi₃TaO_7−x coin cell cathode shows superior cycling and rate performance. Even under a high level of sulfur loading of 9.6 mg cm⁻², a relatively high initial areal capacity of 10.20 mAh cm⁻² and a specific energy density of 300 Wh kg⁻¹ are achieved with a low electrolyte/sulfur ratio of 3.3 µL mg⁻¹. Combined with experimental results and theoretical calculations, the mechanism by which the Bi₄TaO₇ with oxygen vacancies promotes the kinetics of polysulfide conversion reactions has been revealed. The design of the multiple confined cathode structure provides physical and chemical adsorption, fast charge transfer, and catalytic conversion for polysulfides.