Hasil untuk "Physical and theoretical chemistry"

Mijie Pang, Jianbing Jin, Arjo Segers et al.

Atmospheric chemistry encapsulates the emission of various pollutants, the complex chemistry reactions, and the meteorology dominant transport, which form a dynamic system that governs air quality. While deep learning (DL) models have shown promise in capturing intricate patterns for forecasting individual atmospheric component - such as PM2.5 and ozone - the critical interactions among multiple pollutants and the combined influence of emissions and meteorology are often overlook. This study introduces an advanced DL-based atmospheric chemistry transport model Zeeman for multi-component atmospheric chemistry simulation. Leveraging an attention mechanism, our model effectively captures the nuanced relationships among these constituents. Performance metrics demonstrate that our approach rivals numerical models, offering an efficient solution for atmospheric chemistry. In the future, this model could be further integrated with data assimilation techniques to facilitate efficient and accurate atmospheric emission estimation and concentration forecast.

Hans Gundlach, Keeper Sharkey, Jayson Lynch et al.

For decades, computational chemistry has been posited as one of the areas in which quantum computing would revolutionize. However, the algorithmic advantages that fault-tolerant quantum computers have for chemistry can be overwhelmed by other disadvantages, such as error correction, processor speed, etc. To assess when quantum computing will be disruptive to computational chemistry, we compare a wide range of classical methods to quantum computational methods by extending the framework proposed by Choi, Moses, and Thompson. Our approach accounts for the characteristics of classical and quantum algorithms, and hardware, both today and as they improve. We find that in many cases, classical computational chemistry methods will likely remain superior to quantum algorithms for at least the next couple of decades. Nevertheless, quantum computers are likely to make important contributions in two important areas. First, for simulations with tens or hundreds of atoms, highly accurate methods such as Full Configuration Interaction are likely to be surpassed by quantum phase estimation in the coming decade. Secondly, in cases where quantum phase estimation is most efficient less accurate methods like Couple Cluster and Moller-Plesset, could be surpassed in fifteen to twenty years if the technical advancements for quantum computers are favorable. Overall, we find that in the next decade or so, quantum computing will be most impactful for highly accurate computations with small to medium-sized molecules, whereas classical computers will likely remain the typical choice for calculations of larger molecules.

Mikołaj Martyka, Lina Zhang, Fuchun Ge et al.

Abstract We present a robust protocol for affordable learning of electronic states to accelerate photophysical and photochemical molecular simulations. The protocol solves several issues precluding the widespread use of machine learning (ML) in excited-state simulations. We introduce a novel physics-informed multi-state ML model that can learn an arbitrary number of excited states across molecules, with accuracy better or similar to the accuracy of learning ground-state energies, where information on excited-state energies improves the quality of ground-state predictions. We also present gap-driven dynamics for accelerated sampling of the small-gap regions, which proves crucial for stable surface-hopping dynamics. Together, multi-state learning and gap-driven dynamics enable efficient active learning, furnishing robust models for surface-hopping simulations and helping to uncover long-time-scale oscillations in cis-azobenzene photoisomerization. Our active-learning protocol includes sampling based on physics-informed uncertainty quantification, ensuring the quality of each adiabatic surface, low error in energy gaps, and precise calculation of the hopping probability.

Xiwei Wang, Jun Huang

Juris Kalvans, Juris Freimanis

Context. Dust grains in circumstellar envelopes are likely to have a spread-out temperature distribution. Aims. To investigate how trends in temperature distribution between small and large grains affect the hot corino chemistry of complex organic molecules (COMs) and warm carbon-chain chemistry (WCCC). Methods. A multi-grain multi-layer astrochemical code with an up-to-date treatment of surface chemistry was used with three grain temperature trends: grain temperature proportional to grain radius to the power -1/6 (Model M-1/6), to 0 (M0), and to 1/6 (M1/6). The cases of hot corino and WCCC chemistry were investigated, for a total of six models. The essence of these changes is for the main ice reservoir - small grains - having higher (M-1/6) or lower (M1/6) temperature than the surrounding gas. Results. The chemistry of COMs shows better agreement with observations in models M-1/6 and M1/6 than in Model M0. Model M-1/6 shows best agreement for WCCC because earlier mass-evaporation of methane ice from small grains induces the WCCC phenomenon at lower temperatures. Conclusions. Models considering several grain populations with different temperatures can more precisely reproduce circumstellar chemistry.

Debarshi Banerjee, Khatereh Azizi, Colin K. Egan et al.

The use of computer simulations to study the properties of aqueous systems is, today more than ever, an active area of research. In this context, during the last decade there has been a tremendous growth in the use of data-driven approaches to develop more accurate potentials for water as well as to characterize its complexity in chemical and biological contexts. We highlight the progress, giving a historical context, on the path to the development of many-body and reactive potentials to model aqueous chemistry, including the role of machine learning strategies. We focus specifically on conceptual and methodological challenges along the way in performing simulations that seek to tackle problems in modeling the chemistry of aqueous solutions. In conclusion, we summarize our perspectives on the use and integration of advanced data-science techniques to provide chemical insights in physical chemistry and how this will influence computer simulations of aqueous systems in the future.

Hiroki Sayama

Hash Chemistry, a minimalistic artificial chemistry model of open-ended evolution, has recently been extended to non-spatial and cellular versions. The non-spatial version successfully demonstrated continuous adaptation and unbounded growth of complexity of self-replicating entities, but it did not simulate multiscale ecological interactions among the entities. On the contrary, the cellular version explicitly represented multiscale spatial ecological interactions among evolving patterns, yet it failed to show meaningful adaptive evolution or complexity growth. It remains an open question whether it is possible to create a similar minimalistic evolutionary system that can exhibit all of those desired properties at once within a computationally efficient framework. Here we propose an improved version called Structural Cellular Hash Chemistry (SCHC). In SCHC, individual identities of evolving patterns are explicitly represented and processed as the connected components of the nearest neighbor graph of active cells. The neighborhood connections are established by connecting active cells with other active cells in their Moore neighborhoods in a 2D cellular grid. Evolutionary dynamics in SCHC are simulated via pairwise competitions of two randomly selected patterns, following the approach used in the non-spatial Hash Chemistry. SCHC's computational cost was significantly less than the original and non-spatial versions. Numerical simulations showed that these model modifications achieved spontaneous movement, self-replication and unbounded growth of complexity of spatial evolving patterns, which were clearly visible in space in a highly intuitive manner. Detailed analysis of simulation results showed that there were spatial ecological interactions among self-replicating patterns and their diversity was also substantially promoted in SCHC, neither of which was present in the non-spatial version.

Monalisa Sahoo, Vivek Kumar, S.N. Naik

The objective of this research endeavor was to enhance the quality and storability of Dioscorea bulbifera through the investigation of the effects of pre-treatment (soaking) and different dehydration temperatures (50, 60, and 70°C) on its mass transfer, color kinetics, texture, microstructure, and rehydration properties. Ten drying and four-color kinetics models were employed to describe drying behavior and color changes. The drying process demonstrated a falling rate, with reduced drying time (from 960 to 540 min) as the convective temperature increased from 50 to 70°C. Moisture diffusivity increased with increasing hot air temperatures (4.15 ×10–10 – 1.03 ×10–9 m2/s), and the activation energy was determined as 41.82 kJ/mol. Slices dried at 70 °C exhibited higher color change than those dried at 50°C. The modified color model fitted the best color parameters, followed by the fraction model. Slices dried at 60°C showed lower hardness (34.73 N) and higher porosity (27.03 %) as compared with 50°C (49.33 N and 19.82 %) and 70°C (40.16 N and 21.90 %) temperature. Microstructure, moisture diffusion, and texture were closely linked to temperature and moisture content. Boiling and potassium metabisulfite significantly affected drying rate and texture of yam slices . MATLAB analysis provided detailed pore information for each hot air-dried sample, correlating with texture characteristics. This research offers substantial industrial significance by providing methods to enhance dried yam products' quality, shelf-life enhancement, and further exploration of starch extraction and recovery of valuable bioactive compounds.

Alessandro Pinto

Several sources of uncertainty have to be taken into account in the analysis and design of CPS. The set of parameters used in the model of the physical plant of a CPS may be uncertain due, for example, to manufacturing processes that are precise up to some bounded tolerance. Physical quantities are sensed by electronic components that add noise to the sensed signals. Abstraction of the physical world, which is often necessary to limit the complexity of the models used in analysis and at run-time in decision-making, leads to non-determinism. The cyber side of a CPS, which includes both hardware and software components, exposes several types of uncertainty such as failures, latency, and implementation errors. Design processes and tools allow engineers to minimize the impact of these types of uncertainty, and to deliver systems which can be operated with an acceptable level of risk. In the past several years, cyber-physical systems have evolved, primarily due to pervasive connectivity, miniaturization, cost-effectiveness of hardware, and advances in the area of Artificial Intelligence. This new class of applications features an environment that is much more complex to model than traditional physical systems due not only to their scale, but also to new sources and types of uncertainty. Consider, for example, the typical case of echo chambers which is attributed to the effect that machine learning algorithms have on the bias of people. Such behavior is not easily predictable because of high uncertainty in the environment (people), which is only approximately represented by machine learning models, but that is inherently due to lack of knowledge. New models and analysis methods are therefore needed to capture different types of uncertainties, and to analyze these new classes of systems.

Takashi KAKIUCHI, Ryunosuke TANIGO, Atsushi TANI et al.

A reference electrode equipped with ionic liquid salt bridge consisting of tributyl(2-methoxyethyl)phosphonium bis(pentafluoroethanesulfonyl)amide has been employed for potentiometric precipitation titration of chloride with silver ions in water at 25 °C. A model for the titration curve was regressed to experimental curves, taking into account the change in the activity coefficients of relevant ionic species in the course of the titration, to obtain the least square estimates of two adjustable parameters in the model, the solubility product (Ksp) and the analyte concentration. The least-square estimate of Ksp, (1.840 ± 0.060) × 10−10, i.e., pKsp = 9.736 ± 0.014, is in good agreement with literature data, but with higher precision.

Ryan-Rhys Griffiths, Leo Klarner, Henry B. Moss et al.

We introduce GAUCHE, a library for GAUssian processes in CHEmistry. Gaussian processes have long been a cornerstone of probabilistic machine learning, affording particular advantages for uncertainty quantification and Bayesian optimisation. Extending Gaussian processes to chemical representations, however, is nontrivial, necessitating kernels defined over structured inputs such as graphs, strings and bit vectors. By defining such kernels in GAUCHE, we seek to open the door to powerful tools for uncertainty quantification and Bayesian optimisation in chemistry. Motivated by scenarios frequently encountered in experimental chemistry, we showcase applications for GAUCHE in molecular discovery and chemical reaction optimisation. The codebase is made available at https://github.com/leojklarner/gauche

Yui Kawashima, Michiel Min

The retrieval approach is currently a standard method for deriving atmospheric properties from observed spectra of exoplanets. However, the approach ignores disequilibrium chemistry in most current retrieval codes, which can lead to misinterpretation of the metallicity or elemental abundance ratios of the atmosphere. We have implemented the disequilibrium effect of vertical mixing or quenching for the major species in hydrogen/helium-dominated atmospheres, namely $\mathrm{CH_4}$, $\mathrm{CO}$, $\mathrm{H_2O}$, $\mathrm{NH_3}$, $\mathrm{N_2}$, and $\mathrm{CO_2}$, for the spectral retrieval code ARCiS with a physical basis using the chemical relaxation method. Then, using ARCiS updated with this module, we have performed retrievals of the observed transmission spectra of 16 exoplanets with sizes ranging from Jupiter to mini-Neptune. As a result, we find indications of disequilibrium chemistry for HD 209458b ($\geq 4.1σ$) and WASP-39b ($\geq 2.7σ$). The retrieved spectrum of HD 209458b exhibits a strong $\mathrm{NH_3}$ absorption feature at 10.5 $μ$m accessible by JWST owing to an enhanced abundance of $\mathrm{NH_3}$ due to the quenching effect. This feature is absent in the spectrum retrieved assuming equilibrium chemistry, which makes HD 209458b an ideal target for studying disequilibrium chemistry in exoplanet atmospheres. Moreover, for HAT-P-11b and GJ 436b, we obtain relatively different results than for the retrieval with the equilibrium assumption, such as a $2.9σ$ difference for the C/O ratio. We have also examined the retrieved eddy diffusion coefficient, but could not identify a trend over the equilibrium temperature, possibly due to the limits of the current observational precision.

Baharudin, Khairul Basyar, Abdullah, Nurulhuda, Derawi, Darfizzi

Highly crystalline ZnO nanoparticles with a pure phase and raspberry-like structure were synthesized using three different techniques (glycol-solvothermal, low-temperature solvothermal, and coprecipitation methods). Physisorption analysis and field emission scanning electron microscopy confirmed the large specific surface area of the ZnO nanoparticles with a mesoporous–macroporous structure due to the interstices of aggregated and agglomerated secondary and tertiary ZnO particles. The ZnO nanoparticles from the coprecipitation method presented the best performance among the three products, owing to their highest purity and crystalline phase, large Brunauer–Emmett–Teller surface area (23.0 m2${\cdot }$g$^{-1}$) and pore volume, and the finest mesoporous–macroporous structure. ZnO nanoparticles can be used in various applications such as catalysis, biosensing, imaging, drug delivery, and pollution absorption for the purpose of environmental remediation.

Sun Xiaolong, Yuan Junshen, Zhang Yikang et al.

In order to clarify the effect of thermal aging on the nano-stabilized modified asphalt binder, TINUVIN 770 (T770) hindered amine light stabilizer of nanoscale was selected as aging modifying agent to prepare modified asphalt. The impact of thermal aging on the rheological properties of T770-modified asphalt was investigated using dynamic shear rheological test of frequency sweep and multiple stress creep recovery test. The surface roughness variation of T770-modified asphalt was characterized by using atomic force microscope throughout the thermal aging process. Furthermore, the typical morphology and parameter features of T770-modified asphalt were identified. The results showed that with the extension of thermal aging time, the high temperature rutting resistance of T770-modified asphalt was improved, while the unrecoverable creep compliance was degraded. When the thermal aging time exceeded 6 h, the bee structure appeared on the surface of T770-modified asphalt. Meanwhile, with the increase in thermal aging time, the special structure formed on the surface of asphalt gradually became smaller. The surface fluctuation difference of T770-modified asphalt reflected the better thermal aging resistance property of T770-modified asphalt than 70# asphalt.

Sabry Khalil, Salman S. Alharthi

An optimal composition for magnesium liquid membrane sensor based on the reaction of magnesium ions with the macro cyclic reagent 1,4,7 - triazacyclononane - 1,4,7 - tris - methylene methylphosphinic acid. The characteristics slope (30.5 mV), the limit of detection (6.2 × 10-7 M), the coefficient of selectivity toward some metal ions, response time (15 s), lifetime (180 days), the effect of pH on the sensor potential and the basic analytical parameters were studied. The sensor was used to estimate the concentration magnesium ions concentration in pharmaceutical preparations. The obtained results by the developed sensor were statistically analyzed and compared with those of other different reported methods.

Sivanathan Amende, Dou Qingqing, Wang Yuxuan et al.

Buildings contribute to 40% of total global energy consumption, which is responsible to 38% of greenhouse gas emissions. It is critical to enhance the energy efficiency of buildings to mitigate global warming. In the last decade, advances in thermal energy storage (TES) techniques using phase change material (PCM) have gained much attention among researchers, mainly to reduce energy consumption and to promote the use of renewable energy sources such as solar energy. PCM technology is one of the most promising technologies available for the development of high performance and energy-efficient buildings and, therefore, considered as one of the most effective and on-going fields of research. The main limitation of PCM is its leakage problem which limits its potential use in building construction and other applications such as TES and textiles, which can be overcome by employing nano-/micro-encapsulation technologies. This paper comprehensively overviews the nano-/micro-encapsulation technologies, which are mainly classified into three categories including physical, physiochemical and chemical methods, and the properties of microcapsules prepared. Among all encapsulation technologies available, the chemical method is commonly used since it offers the best technological approach in terms of encapsulation efficiency and better structural integrity of core material. There is a need to develop a method for the synthesis of nano-encapsulated PCMs to achieve enhanced structural stability and better fracture resistance and, thus, longer service life. The accumulated database of properties/performance of PCMs and synthesised nano-/micro-capsules from various techniques presented in the paper should serve as the most useful information for the production of nano-/micro-capsules with desirable characteristics for building construction application and further innovation of PCM technology.

Matthew P. A. Jones, Robert M. Potvliege, Michael Spannowsky

We consider the role of high-lying Rydberg states of simple atomic systems such as $^1$H in setting constraints on physics beyond the Standard Model. We obtain highly accurate bound states energies for a hydrogen atom in the presence of an additional force carrier (the energy levels of the Hellmann potential). These results show that varying the size and shape of the Rydberg state by varying the quantum numbers provides a way to probe the range of new forces. By combining these results with the current state-of-the-art QED corrections, we determine a robust global constraint on new physics that includes all current spectroscopic data in hydrogen. Lastly we show that improved measurements that fully exploit modern cooling and trapping methods as well as higher-lying states could lead to a strong, statistically robust global constraint on new physics based on laboratory measurements only.

N.Yu. Sdobnyakov, V.M. Samsonov, A.Yu. Kolosov et al.

In this work, we performed atomistic modeling of the behavior of Co (core) – Au (shell) and Au (core) – Co (shell) during thermal exposure in order to study the stability/instability problem for the above nanostructures using the tight binding potential and two alternative computer programs based on the molecular dynamics and Monte Carlo methods. It has been shown that both the Co (core) – Au (shell) and Au (core) – Co (shell) structures can be stable. However, molecular dynamics results predict a short-term decay of Au2500 (core) / Co2500 (shell) nanostructures in a high-temperature region followed by a self-assembly, while the Monte Carlo method predicts the appearance of defects – cavities in the particle core and on the boundary between two components. It is concluded that the size effect can be the main factor determining stability/instability for the Co (core) – Au (shell) and Au (core) – Co (shell) structures.

Juris Kalvans, Ivar Shmeld

The deuterium enrichment in molecules in dark molecular cloud cores and starforming regions is usually attributed to gas-phase chemistry. Here we examine the effects of surface and mantle chemical reactions on the deuteration of species. We use a simple kinetic chemistry model that includes gas, surface and mantle pore phase reactions of deuterated species. The mantle is assumed to be partially reactive due to pores with sufficient surface area for chemical reactions, that are continuously transformed by cosmic-rays. Calculation results show that surface reactions generally enhance the deuteration for at least several molecules. However, once they are buried and become mantle molecules, they lose their deuteration over a timescale of 10 million years due to processes in the mantle. If deuterated species in young star-forming regions come from grain mantles, a cautious conclusion is that the freeze-out of molecules, perhaps, should not occur more than 10 Myr before the mantle evaporates to the gas phase.

Liyun Yang, Xiaoming Qian, Zhi Wang et al.

This study investigates the removal effectiveness and characteristics of phenanthrene and naphthalene using low-cost steel slag with batch experiments. The adsorption characteristics of steel slag were measured and analysed using X-ray fluorescence, X-ray diffraction, and Fourier transform infrared spectroscopy. The batch experiments investigated the effect of the time gradient, pH, and steel slag dosage gradient on the adsorption of the steel slag. The results show that with time and dosage of steel slag increased, the adsorption capacity of phenanthrene and naphthalene increased and gradually became balanced, but pH had no obvious effect on the adsorption of phenanthrene and naphthalene. The Langmuir isotherm model best describes the phenanthrene and naphthalene removal by the steel slag, which shows the adsorption occurring in a monolayer. The maximum adsorption capacity of the steel slag to phenanthrene and naphthalene is 0.043 and 0.041 mg/g, respectively. A pseudo-first-order kinetic model can better represent the adsorption of phenanthrene and naphthalene by steel slag. The research demonstrates that the steel slag has a certain adsorption capacity for phenanthrene and naphthalene.