Hasil untuk "Chemistry"

Alex N. Byrne, Christopher N. Shingledecker, Edwin A. Bergin et al.

Elemental abundances, which are often depleted with respect to the solar values, are important input parameters for kinetic models of interstellar chemistry. In particular, the amount of carbon relative to oxygen is known to have a strong effect on modeled abundances of many species. While previous studies have focused on comparison of modeled and observed abundances to constrain the C/O ratio, the effects of this parameter on the underlying chemistry have not been well-studied. We investigated the role of the C/O ratio on dark cloud chemistry using the NAUTILUS code and machine learning techniques for molecular representation. We find that modeled abundances are quite sensitive to the C/O ratio, especially for carbon-rich species such as carbon chains and polycyclic aromatic hydrocarbons (PAHs). CO and simple ice-phase species are found to be major carbon reservoirs under both oxygen-poor and oxygen-rich conditions. The appearance of C3H4 isomers as significant carbon reservoirs, even under oxygen-rich conditions, indicates the efficiency of gas-phase C3 formation followed by adsorption and grain-surface hydrogenation. Our model is not able to reproduce the observed, gas-phase C/H ratio of TMC-1 CP at the time of best fit with any C/O ratio between 0.1 and 3, suggesting that the modeled freeze-out of carbon-bearing molecules may be too rapid. Future investigations are needed to understand the reactivity of major carbon reservoirs and their conversion to complex organic molecules.

A. I. Safonov, F. V. Golubev

Relevance. In the industrially tense region (Donbass), as a result of socio-economic upheavals since 2014, many lands have been withdrawn from agricultural use and are now abandoned and degrading. Areas of active military action create beligerative landscapes characterized by profound geophysical and geochemical transformations. These areas are hotbeds of toxic environmental impacts and require targeted restoration measures. Phytoremediation stands out among the most effective methods for optimizing natural-territorial complexes of the DPR as the most effective, economically advantageous and aesthetically attractive.Materials and Methods. Agricultural and recreational ecotopes in the Central Donbass were studied. A field assessment of the state of local geosystems was conducted. Morphological analysis and description of plants, as well as calculations for determining life strategies (CSR), were applied. Analytical methods (atomic absorption, inductively coupled plasma mass spectrometry, and neutron activation) were used.Results. A difference in the range of informative structural features variation of some indicator plants for use in phytoremediation purposes in post-conflict areas – sites of active military operations in Donbass – has been established. New geochemical anomalies were identified in post-conflict areas for a number of technophile elements (Mn, Р, Zn, Cu, Mo, Ni, Pb, Cr, La, Co, Se, As, Cd). For the plant species Cichorium intybus L., Taraxacum officinale F.H.Wigg, Plantago major L., and Diplotaxis muralis (L.) DC., the implementation patterns of life-sustaining strategies (visualization of CSR in the Grime-Ramensky triangle) and ecological plasticity in areas affected by the militarization of the region were determined. Anatomical and morphological pathologies of the studied species were identified. The ecological valence of species allows them to support the initial stages of active succession during the first two to three years, forming a vegetation cover that performs anti-erosion and habitatforming functions. Based on plant morphopathologies and elemental composition data, geochemical anomalies were identified and a range of geochemical background values for elemental composition in plant samples was described. A phosphorus-lanthanum anomaly (P-La), a consequence of military operations in the DPR, is described for the first time.

Ryotaro Koshoji

Mathematical crystal chemistry views crystal structures as the optimal solutions of mathematical optimization problem formalizing inorganic structural chemistry. This paper introduces the minimum and maximum atomic radii depending on the types of geometrical constraints, extending the concept of effective atomic sizes. These radii define permissible interatomic distances instead of interatomic forces, constraining feasible types and connections of coordination polyhedra. The definition shows the aspect that crystal structures are packings of atomic spheres. Additionally, creatability functions for geometrical constraints, which give a choice of creatable types of geometrical constraints depending on the spatial order of atoms, are implemented to guide randomly generated structures toward optimal solutions. The framework identifies unique optimal solutions corresponding to the structures of spinel, pyrochlore ($α$ and $β$), pyroxene, quadruple perovskite, cuprate superconductor $\mathrm{YBa}_2 \mathrm{Cu}_3 \mathrm{O}_{7-x}$, and iron-based superconductor $\mathrm{LaFeAsO}$. Notably, up to $95\%$ of oxide crystal structure types in Inorganic Crystal Structure Database align with the optimal solutions preserving experimental structures despite the discretized feasible atomic radii. These findings highlight the role of mathematical optimization problem as a theoretical foundation for mathematical crystal chemistry, enabling efficient structure prediction.

Aida Meto

Bearing in mind the expression, “<i>The art challenges the technology, and the technology inspires the art</i>”, we say that oral surgery is changing rapidly due to the introduction of new technologies that improve the way surgical treatments are planned and performed [...]

Daniel Marti-Dafcik, Nicholas Lee, Hugh G. A. Burton et al.

Molecular orbital theory is powerful both as a conceptual tool for understanding chemical bonding, and as a theoretical framework for ab initio quantum chemistry. Despite its undoubted success, MO theory has well documented shortcomings, most notably that it fails to correctly describe diradical states and homolytic bond fission. In this contribution, we introduce a generalised MO theory that includes spin-coupled radical states. We show through archetypical examples that when bonds break, the electronic state transitions between a small number of valence configurations, characterised by occupation of both delocalised molecular orbitals and spin-coupled localised orbitals. Our theory provides a model for chemical bonding that is both chemically intuitive and qualitatively accurate when combined with ab initio theory. Although exploitation of our theory presents significant challenges for classical computing, the predictable structure of spin-coupled states is ideally suited to algorithms that exploit quantum computers. Our approach provides a systematic route to overcoming the initial state overlap problem and unlocking the potential of quantum computational chemistry.

Gregory D Scholes, Alexandra Olaya-Castro, Shaul Mukamel et al.

This document summarizes the context and main outcomes of the discussions that took place during the NSF-UKRI bilateral workshop on Quantum Information Science in Chemistry, held on 12-13 February 2024, in Alexandria, Virginia (US). The workshop was jointly funded by the National Science Foundation (NSF) and UK Research and Innovation (UKRI) through the Engineering and Physical Sciences Research Council (EPSRC). It brought together scientific delegations from the United States of America (US) and the United Kingdom (UK).

Ryota Kojima, Masahiko Kamoshita, Keita Kanno

Quantum chemistry is a key target for quantum computing, but benchmarking quantum algorithms for large molecular systems remains challenging due to the lack of exactly solvable yet structurally realistic models. In particular, molecular Hamiltonians typically contain $O(N^4)$ Pauli terms, significantly increasing the cost of quantum simulations, while many exactly solvable models, such as the one-dimensional Fermi-Hubbard (1D FH) model, contain only $O(N)$ terms. In this work, we introduce the orbital-rotated Fermi-Hubbard (ORFH) model as a scalable and exactly solvable benchmarking problem for quantum chemistry algorithms. Starting from the 1D FH model, we apply a spin-involved orbital rotation to construct a Hamiltonian that retains the exact ground-state energy but exhibits a Pauli term count scaling as $O(N^4)$, similar to real molecular systems. We analyze the ORFH Hamiltonian from multiple perspectives, including operator norm and electronic correlation. We benchmark variational quantum eigensolver (VQE) optimizers and Pauli term grouping methods, and compare their performance with those for hydrogen chains. Furthermore, we show that the ORFH Hamiltonian increases the computational difficulty for classical methods such as the density matrix renormalization group (DMRG), offering a nontrivial benchmark beyond quantum algorithms. Our results demonstrate that the ORFH model provides a versatile and scalable testbed for benchmarking quantum chemistry algorithms under realistic structural conditions, while maintaining exact solvability even at large system sizes.

Emily Rauscher

This chapter provides an overview of the basic concepts foundational to atmospheric physics and chemistry. We discuss the retention of atmospheres against thermal evaporation and the global energy balance of planets. We present simple derivations of the vertical profile of an atmosphere, which may be shaped by convective and radiative transport. We then briefly touch upon the three-dimensional atmospheric structure, as shaped by circulation patterns. We describe how the abundances of chemical species in the atmosphere are determined, starting with the assumption of chemical equilibrium and then expanding to various disequilibrium effects. We introduce the particles that can be important components of atmospheres (clouds and hazes) and sketch out some of their complexity. Finally, we review some of the differences between atmospheres of terrestrial and gaseous worlds.

Xiaojie Wu, Qiming Sun, Zhichen Pu et al.

We describe our contribution as industrial stakeholders to the existing open-source GPU4PySCF project (https: //github.com/pyscf/gpu4pyscf), a GPU-accelerated Python quantum chemistry package. We have integrated GPU acceleration into other PySCF functionality including Density Functional Theory (DFT), geometry optimization, frequency analysis, solvent models, and density fitting technique. Through these contributions, GPU4PySCF v1.0 can now be regarded as a fully functional and industrially relevant platform which we demonstrate in this work through a range of tests. When performing DFT calculations on modern GPU platforms, GPU4PySCF delivers 30 times speedup over a 32-core CPU node, resulting in approximately 90% cost savings for most DFT tasks. The performance advantages and productivity improvements have been found in multiple industrial applications, such as generating potential energy surfaces, analyzing molecular properties, calculating solvation free energy, identifying chemical reactions in lithium-ion batteries, and accelerating neural-network methods. With the improved design that makes it easy to integrate with the Python and PySCF ecosystem, GPU4PySCF is natural choice that we can now recommend for many industrial quantum chemistry applications.

Zigeng Huang, Zhen Guo, Changsu Cao et al.

Predictive simulation of surface chemistry is of paramount importance for progress in fields from catalysis to electrochemistry and clean energy generation. Ab-initio quantum many-body methods should be offering deep insights into these systems at the electronic level, but are limited in their efficacy by their steep computational cost. In this work, we build upon state-of-the-art correlated wavefunctions to reliably converge to the `gold standard' accuracy in quantum chemistry for application to extended surface chemistry. Efficiently harnessing graphics processing unit acceleration along with systematically improvable multiscale resolution techniques, we achieve linear computational scaling up to 392 atoms in size. These large-scale simulations demonstrate the importance of converging to these extended system sizes, achieving a validating handshake between simulations with different boundary conditions for the interaction of water on a graphene surface. We provide a new benchmark for this water-graphene interaction that clarifies the preference for water orientations at the graphene interface. This is extended to the adsorption of carbonaceous molecules on chemically complex surfaces, including metal oxides and metal-organic frameworks, where we consistently achieve chemical accuracy compared to experimental references, and well inside the scatter of traditional density functional material modeling approaches. This pushes the state of the art for simulation of molecular adsorption on surfaces, and marks progress into a post-density functional era for more reliable and improvable approaches to first-principles modeling of surface problems at an unprecedented scale and accuracy using ab-initio quantum many-body methods.

Luis D. Loor-Urgilés, Tabata N. Feijoó, Carlos A. Martínez-Huitle et al.

Abstract Chemical oxygen demand-COD is essential for water pollution control and monitoring and is also used to validate wastewater treatment technologies. Conventional COD determination use of costly toxic inputs that do not align with Sustainable Development Goals 6. To address these environmental challenges, photocatalytic (PC)- and photoelectrocatalytic (PEC)-COD sensors have emerged as a solution. This comprehensive review examines PC-COD and PEC-COD sensors in terms of nanomaterials used and their properties, focusing on how multiple variables influence PC activity and sensor performance. Analytical principles and operational variables affecting performance in COD determination are discussed. Finally, a series of materials and conditions are proposed to improve the viability of PEC-COD sensors currently and in the future.

Nicolas Jolly, Yuriel Núñez Fernández, Xavier Waintal

Choosing a basis set is the first step of a quantum chemistry calculation and it sets its maximum accuracy. This choice of orbitals is limited by strong technical constraints as one must be able to compute a large number of six dimensional Coulomb integrals from these orbitals. Here we use tensor network techniques to construct representations of orbitals that essentially lift these technical constraints. We show that a large class of orbitals can be put into ``tensorized'' form including the Gaussian orbitals, Slater orbitals, linear combination thereof as well as new orbitals beyond the above. Our method provides a path for building more accurate and more compact basis sets beyond what has been accessible with previous technology. As an illustration, we construct optimized tensorized orbitals and obtain a 85% reduction of the error on the energy of the $H_2$ molecules with respect to a reference double zeta calculation (cc-pvDz) of the same size.

Ramin Dehghan, Seyyed Ali Seyyed Ebrahimi, Zahra Lalegani et al.

The microstructure and magnetic properties of methane (CH₄) heat-treated Sr-hexaferrite powders during the re-calcination process were investigated and compared with the magnetic properties of conventionally synthesized Sr-hexaferrite powder. Gradual changes in the magnetic behavior of the produced powder in each re-calcination stage were investigated using magnetization curves obtained from the vibration sample magnetometry (VSM) technique. First, the initial Sr-hexaferrite powder was prepared by the conventional route. Then the powder was heat treated in a dynamic CH₄ atmosphere in previously optimized conditions (temperature: 950 °C, gas flow rate:15 cc min⁻¹ and time: 30 min), and finally, re-calcined in various temperatures from 200 to 1200 °C. By investigating the hysteresis loops, we found the transition temperature of soft to hard magnetic behavior to be 700 °C. The maximum ratio M_r/M_s was obtained at temperatures of 800–1100 °C. At 1100 °C, and despite the Sr-hexaferrite single phase, the magnetic behavior showed a multiphase behavior that was demonstrated by a kink in the hysteresis loop. Uniform magnetic behavior was observed only at 900 °C and 1000 °C. Although the ratio M_r/M_s was almost the same at these temperatures, the values of M_r and M_s at 1000 °C were almost double of 900 °C. At 1000 °C, the second quadrant of hysteresis curve had the maximum area. Therefore, 1000 °C was the optimum temperature for re-calcination after CH₄ gas heat treatment in the optimized conditions. Due to the presence of a small amount of hematite soft phase at 1000 °C, the most probable reason for the exclusive properties of the optimized product may be the exchange coupling phenomenon between the hard Sr-hexaferrite phase and the impurity of the soft hematite phase.

Agnieszka Ziółkiewicz, Kamila Kasprzak-Drozd, Agnieszka Wójtowicz et al.

The phenol content of sorghum is a unique feature among all cereal grains; hence this fact merits the special attention of scientists. It should be remembered that before polyphenols can be used in the body, they are modified within the digestive tract. In order to obtain more accurate data on the level and activity of tested ingredients after ingestion and digestion in the in vivo digestive tract, in vitro simulated digestion may be used. Thus, the aim of this study was to determine the content of polyphenols, flavonoids, and individual phenolic acids, as well as the antiradical properties, of sorghum and sorghum-enriched pasta before and after in vitro simulated gastrointestinal digestion. We observed that the total content of polyphenols decreased after gastric digestion of sorghum, and slightly increased after duodenal digestion. Moreover, the flavonoid content decreased after the first stage of digestion, while antioxidant properties increased after the first stage of digestion and slightly decreased after the second stage. The digestion of polyphenolics in sorghum is completely different to that in pasta—both in varieties with, and without, the addition of sorghum. For pasta, the content of total polyphenols and flavonoids, and free radical scavenging properties, decrease after each stage of digestion.

Kai Trepte, Johannes Voss

Reliable predictions of surface chemical reaction energetics require an accurate description of both chemisorption and physisorption. Here, we present an empirical approach to simultaneously optimize semi-local exchange and non-local correlation of a density functional approximation to improve these energetics. A combination of reference data for solid bulk, surface, and gas-phase chemistry and physical exchange-correlation model constraints leads to the VCML-rVV10 exchange-correlation functional. Owing to the variety of training data, the applicability of VCML-rVV10 extends beyond surface chemistry simulations. It provides optimized gas phase reaction energetics and an accurate description of bulk lattice constants and elastic properties.

Amany M. Fekry

A new electrochemical sensor for the detection of levofloxacin (LV) was efficiently realized. The aim was to develop a new, cheap, and simple sensor for the detection of LV, which is used in various infections due to its pharmacological importance. It consists of carbon paste (CP) enhanced with nano-sized fumed silica (NFS). NFS has a very low bulk density and a large surface area. The carbon paste-enhanced NFS electrode (NFS/CPE) showed great electrocatalytic activity in the oxidation of 1.0 mM LV in Britton–Robinson buffer (BR) at pH values ranging from 3.0 to 8.0. Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) were used; the peak current value (I_p) of the NFS/CPE sensor was 2.7 times that of the bare electrode, ensuring its high electrocatalytic activity. Electrochemical impedance spectroscopy (EIS) was performed at a peak potential (E_p) of +1066 mV, yielding a resistance of 10 kΩ for the designed NFS/CPE sensor compared to 2461 kΩ for the bare electrode, indicating the high conductivity of the modified sensor and verifying the data observed using the CV technique. Surface descriptions were determined by scanning electron microscopy (SEM) and energy dispersive X-ray analysis (EDX). The variation in the concentration of LV (2.0 to 1000 µM) was considered in BR buffer (pH = 5.0) at a scan rate (SR) of 10 mV/s by the NFS/CPE. The detection and quantification limits were 0.09 µM and 0.30 µM, respectively. To evaluate the application of LV in real samples, this procedure was established on Quinostarmax 500 mg tablets and human plasma samples. Reasonable results were obtained for the detection of LV.

Ahmad Mohsenimanesh, Evgueniy Entchev, Filip Bosnjak

Forecasting the aggregate charging load of a fleet of electric vehicles (EVs) plays an important role in the energy management of the future power system. Therefore, accurate charging load forecasting is necessary for reliable and efficient power system operation. A hybrid method that is a combination of the similar day (SD) selection, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and deep neural networks is proposed and explored in this paper. For the SD selection, an extreme gradient boosting (XGB)-based weighted k-means method is chosen and applied to evaluate the similarity between the prediction and historical days. The CEEMDAN algorithm, which is an advanced method of empirical mode decomposition (EMD), is used to decompose original data, to acquire intrinsic mode functions (IMFs) and residuals, and to improve the noise reduction effect. Three popular deep neural networks that have been utilized for load predictions are gated recurrent units (GRUs), long short-term memory (LSTM), and bidirectional long short-term memory (BiLSTM). The developed models were assessed on a real-life charging load dataset that was collected from 1000 EVs in nine provinces in Canada from 2017 to 2019. The obtained numerical results of six predictive combination models show that the proposed hybrid SD-CEEMDAN-BiLSTM model outperformed the single and other hybrid models with the smallest forecasting mean absolute percentage error (MAPE) of 2.63% Canada-wide.

Badzlin Nabilah, Adi Setyo Purnomo, Hamdan Dwi Rizqi et al.

Methylene blue (MB) is one of synthetic dyes that is used in the textile industry which is difficult to degrade in nature. Previously, the brown-rot fungus (BRF) Daedalea dickinsii had shown a good ability to degrade MB, however, the decolorization ability was relatively still low and had a long period of incubation. Therefore, improvement of process is needed to increase the ability of D. dickinsii to decolorize MB. In this study, the effect of Ralstonia pickettii bacterium addition on MB biodecolorization by the BRF D. dickinsii in potato dextrose broth (PDB) medium was investigated. The amount of R. picketti that was added to the culture of D. dickinsii were 2, 4, 6, 8, and 10 mL (1 mL ≈ 1.39 × 108 CFU). The cultures had ability to decolorize MB (100 mg/L) at 30 °C after 7 days incubation. The highest percentage of MB biodecolorization was obtained at addition of 10 mL of R. pickettii approximately 89%, while biodecolorization process by particularly D. dickinsii was approximately 17%. The MB degradation metabolites by mixed cultures of D. dickinsii and 10 mL of R. pickettii were Azure A, thionine, glucose-MB, C12H11N3SO6 and C12H13N3O6. This study indicated that the addition of R. pickettii could enhance MB biodecolorization by fungus D. dickinsii. Besides that, this study also indicated that mixed cultures of D. dickinsii and R. pickettii has great potential for high efficiency, fast and cheap dye wastewater treatment.

LIN Shan, YUAN Hong-mei, WANG Dong et al.

YBa₂Cu₃O_7-δ(YBCO) bulk material prepared by high temperature solid state reaction was milled and dispersed through ultrasonic process in ethanol to prepare nanoscale YBCO/ethanol sol. Then it was mixed with aniline or O-phenylenediamine and the organic/YBCO hybrid materials were obtained after concentration and being dried in vacuum. The influence of the organic on YBCO’s chemical composition, phase, elemental valence and magnetic properties was studied by Fourier transform infrared spectroscopy(FT-IR), X-ray diffraction(XRD), X-ray photoelectron spectroscopy(XPS) and vibrating sample magnetometer(VSM). The results show that the infrared absorption of YBCO is not affected by the aniline or O-phenylenediamine within 0.05%-5%(mass fraction, the same below), however the intensity of the XRD peaks is significantly increased. The interaction between the N atom in aniline or O-phenylenediamine and the Y atom in YBCO is stronger compared with N-Ba or N-Cu. The superconducting transition temperature <i>T</i>_c and magnetization <i>M</i> of YBCO are significantly affected by the content of N element in the hybrid materials. When the content of N element exceeds 1%, <i>T</i>_c is significantly decreased and <i>M</i>_min is increased accordingly.