Semiempirical Calculations Research Articles

Toward Humidity-Independent Sensitive and Fast Response Temperature Sensors Based on Reduced Graphene Oxide/Poly(vinyl alcohol) Nanocomposites.

Flexible temperature sensors are becoming increasingly important these days. In this work, we explore graphene oxide (GO)/poly(vinyl alcohol) (PVA) nanocomposites for potential application in temperature sensors. The influence of the mixing ratio of both materials, the reduction temperature, and passivation on the sensing performance has been investigated. Various spectroscopic techniques revealed the composite structure and atomic composition. These were complemented by semiempirical quantum chemical calculations to investigate rGO and PVA interaction. Scanning electron and atomic force microscopy measurements were carried out to evaluate dispersion and coated film quality. The temperature sensitivity has been evaluated for several composite materials with different compositions in the range from 10 to 80 °C. The results show that a linear temperature behavior can be realized based on rGO/PVA composites with temperature coefficients of resistance (TCR) larger than 1.8% K-1 and a fast response time of 0.3 s with minimal hysteresis. Furthermore, humidity influence has been investigated in the range from 10% to 80%, and a minor effect is shown. Therefore, we can conclude that rGO/PVA composites have a high potential for excellent passivation-free, humidity-independent, sensitive, and fast response temperature sensors for various applications. The GO reduction is tunable, and PVA improves the rGO/PVA sensor performance by increasing the tunneling effect and band gap energy, consequently improving temperature sensitivity. Additionally, PVA exhibits minimal water absorption, reducing the humidity sensitivity. rGO/PVA maintains its temperature sensitivity during and after several mechanical deformations.

Mechanistic insights into the role of cyclodextrin in the regioselective radical CH trifluoromethylation of aromatic compounds.

The regioselective radical CH trifluoromethylation of aromatic compounds have been shown to proceed in good yield and high regioselectivity when cyclodextrin (CD) is present. Yet, the reaction mechanism and the role of CD during the reaction have remained obscure. To this end, here we performed density functional theory (DFT) calculations to the conformations obtained by semiempirical quantum mechanical molecular dynamics calculations to reveal the reaction mechanism and the role of CD in controlling regioselectivity. The results show that metal salt increases the yield but do not affect the regioselectivity, which we further confirmed by an experiment. In contrast, multiple CD-substrate complex conformations and reaction pathways were obtained, and CD was shown to contribute to improving the regioselectivity by stabilizing the intermediate state via encapsulation. The present study indicates that CDs can increase the regioselectivity by stabilizing the intermediate and product states while only marginally affecting the transition state.

Enhancing hydrogen sulphide removal efficiency: A DFT study on selected functionalized graphene-based materials

In response to the escalating demand for cleaner energy sources, this study investigates the potential of carefully selected functionalized graphene-based materials for enhancing hydrogen sulphide (H2S) removal in fuel streams, utilizing semi-empirical and density functional theory (DFT) calculations for molecular-level insights. A particular focus is placed on aliphatic methyl (-CH), alcohol (-COH), carboxylate (-COO), carbonyl (-CO), and acid (-COOH) -functionalized graphene, aiming to bridge gaps between desulphurization methods and graphene applications, specifically targeting H2S removal. Through extensive computational analyses, the research unravels the intricate interactions between chosen functionalized graphene materials and sulfur compounds like H2S, emphasizing mechanisms contributing to improved desulphurization efficiency. Our study's analysis highlights the superior performance of carboxylate (-COO)-functionalized graphene, mainly through dissociative adsorption mechanisms. The study systematically evaluates the influence of selected functional groups on adsorption activity, emphasizing the significance of dissociation. Overall, this research advances desulphurization strategies and underscores the potential of functionalized graphene in sustainable energy solutions.

A method for semi-empirical calculation of isospin mixing probability in nuclei

Abstract A method has been proposed for an isolated two-level model calculation of isospin mixing probability in nuclei. It uses minimum experimental inputs. This method is capable of locating the unperturbed levels of an observed isospin mixed doublet. Isospin mixing probabilities, unperturbed level energies of the observed doublets and isospin mixing matrix elements are calculated for five self-conjugate isotopes of (Mg, P, S, Cl, Ar). Using experimental Gamow-Teller strength data, large isospin mixing probability ( 39.572 − 8.271 + 9.082 %) has been found in the observed doublet (1+, 1+) at 9828.0 and 9965.3 keV, respectively, in 24Mg. This is probably the largest isospin mixing ever found in a nucleus. This is much larger than that ( 20.000 − 9.904 + 9.142 %) has been found recently in non self-conjugate 26Si sd-shell nucleus. This is also larger than that ( 27.5 + 2.5 − 2.5 %) has been measured in non self-conjugate fp-shell nucleus 55Ni. Calculated mixing probability in 24Mg has been used to predict the unknown Gamow-Teller strength for the transitions from the levels of 24Al and 24Alm to the levels in 24Mg. The method is general enough to be applicable to other two-level model applications.

Semi-Empirical and Theoretical Calculation of L1, L2, and L3 Subshell Fluorescence Yields

In this study, semi-empirical Li subshells fluorescence yields (ωLi) were calculated from the available experimental data covering the period from 1955 to 2022 for elements with 28 ≤ Z ≤ 96 for L1 subshell, and 23 ≤ Z ≤ 96 for L2 and L3 subshells. First, we critically examine these data and obtain weighted average values (ωL1W,ωL2W,andωL3W) using a formula based on experimental (ωEXP) values and measurement errors. New recommended values ωWR are obtained by calculating the ratio S = ωEXP/ωW and removing out-of-range values (less than 0.8 or greater than 1.2). Semi-empirical values were derived for the three subshells using two interpolations: the analytical function [ωWR/(1 − ωWR)]1/4 and the fitting ratio S = ωEXP/ωWR, both as function of the atomic number Z. Furthermore, new theoretical calculations based on the Multiconfiguration Dirac-Fock Method have been performed for some elements and are presented in this work. Finally, our semi-empirically and theorical calculated Li subshell fluorescence yields were compared with other theoretical, experimental, and empirical values from the literature.

Classification of substances by health hazard using deep neural networks and molecular electron densities

In this paper we present a method that allows leveraging 3D electron density information to train a deep neural network pipeline to segment regions of high, medium and low electronegativity and classify substances as health hazardous or non-hazardous. We show that this can be used for use-cases such as cosmetics and food products. For this purpose, we first generate 3D electron density cubes using semiempirical molecular calculations for a custom European Chemicals Agency (ECHA) subset consisting of substances labelled as hazardous and non-hazardous for cosmetic usage. Together with their 3-class electronegativity maps we train a modified 3D-UNet with electron density cubes to segment reactive sites in molecules and classify substances with an accuracy of 78.1%. We perform the same process on a custom food dataset (CompFood) consisting of hazardous and non-hazardous substances compiled from European Food Safety Authority (EFSA) OpenFoodTox, Food and Drug Administration (FDA) Generally Recognized as Safe (GRAS) and FooDB datasets to achieve a classification accuracy of 64.1%. Our results show that 3D electron densities and particularly masked electron densities, calculated by taking a product of original electron densities and regions of high and low electronegativity can be used to classify molecules for different use-cases and thus serve not only to guide safe-by-design product development but also aid in regulatory decisions.Scientific contributionWe aim to contribute to the diverse 3D molecular representations used for training machine learning algorithms by showing that a deep learning network can be trained on 3D electron density representation of molecules. This approach has previously not been used to train machine learning models and it allows utilization of the true spatial domain of the molecule for prediction of properties such as their suitability for usage in cosmetics and food products and in future, to other molecular properties. The data and code used for training is accessible at https://github.com/s-singh-ivv/eDen-Substances.

Online and desktop graphical user interfaces for xtb programme from atomistica.online platform

ABSTRACT In this paper, we introduce our development of both online and desktop graphical user interfaces (GUIs) for the xtb programme to enhance accessibility and user-friendliness. The field of molecular modelling has been profoundly transformed by the advent of modern semiempirical calculations, with the xtb programme from Prof. Stefan Grimme's group emerging as an exceptionally powerful tool in this domain. xtb programme operates through a command prompt interface, necessitating a certain level of technical proficiency from its users. The need to use a command prompt might be repealing for some users, such as beginners or users who are not dedicated to computational techniques. This was the primary motivation to develop GUIs for this programme, considering that experienced molecular modellers would also appreciate a user-friendly GUI application that helps them automate their modelling tasks. The online GUI represents a significant leap forward, allowing users to execute xtb calculations directly from their web browsers. Complementing the online version, our desktop GUI extends the functionalities available to users, harnessing the more extensive capabilities of the xtb programme. As all other tools of atomistica.online project, xtb GUIs are freely available at the project's official website – https://atomistica.online.

The role of chemical functional groups in dewaterability of hydrochar: A molecular simulation study

Hydrothermal carbonization (HTC) is a promising technique for conversion of wet biomass materials to hydrochar, a carbon-rich material with a wide range of applications. A crucial property of hydrochar, particularly that obtained from HTC of sewage sludge, is the dewaterability, which may be controlled by certain chemical functional groups of the hydrochar itself. This paper presents results from our recent study on the role of chemical functional groups in the dewaterability of hydrochar, employing a novel approach that integrates molecular dynamics simulations with extended semi-empirical tight-binding calculations. By examining various hydrochar models with distinct chemical functional groups such as hydroxyl, carboxyl, and ketone, multiple factors affecting the dewaterability were investigated. The results show that removal of specific functional groups led to substantial reductions in hydration energy of hydrochar molecules, from -340 kJ/mol to -61 kJ/mol. Findings from this work not only contribute to better understanding the dewaterability of hydrochar in connection to the functional groups, but also provide valuable knowledge for customizing the design and production of hydrochar with specific chemical functional groups to suit diverse applications.

Fine- and hyperfine structure semi-empirical studies of atomic cobalt. Even-parity configurations system

We present the results of multiconfiguration semi–empirical calculations of the fine structure (fs) and the hyperfine structure (hfs) for the even-parity level system of atomic cobalt (Co I), using all available experimental data. These results were obtained using our proprietary software package designed for complex atom structure analysis. Through the iterative fine-structure calculation procedure, predictions of energy levels along with their Landé gJ factors and wave functions were obtained for each electronic state. Good agreement was achieved between the theoretical hyperfine structures and the experimental constants, which confirmed the validity of the propositions in the spectroscopic descriptions of observed Co I levels. For some levels no experimental hfs constants were known; in such cases these values are predicted.

Three-Center Tight-Binding Together with Multipolar Auxiliary Functions.

We present an ab initio tight-binding method that allows to improve on the effective potential and minimal basis approximations employed in semiempirical calculations. Three-center expansions are used to evaluate the zeroth-order Hamiltonian matrix elements and repulsive energy terms in the spirit of the Horsfield method. Self-consistency is handled by expanding atomic orbital products in an auxiliary basis following the work of Giese and York, combined with a two-center expansion of the exchange-correlation kernels. Together with nonminimal main basis sets (double-ζ plus polarization), we show that the resulting method trades a modest amount of accuracy for a significant gain in speed, compared to that of numerical atomic orbital density functional theory, in calculations on small molecules, bulk compounds, and metal nanoclusters.

A General Model of Impact Sensitivity for Nitrogen-Rich Energetic Materials: A Combined Incremental Theory and Genetic Function Approximation Study.

In this paper, we report the first example of impact sensitivity prediction based on the genetic function approximation (GFA) as a regression method. The prediction is applicable for a wide variety of chemical families, which include nitro compounds, peroxides, nitrogen-rich salts, heterocycles, etc. Within this work, we have obtained 7 empirical models (with 27-32 basis functions), which all provide 0.80≤R2≤0.83 and 7.2 J≤RMSE≤7.8 J (for 450 training set compounds) and 0.64≤R2≤0.70 and 11.2 J≤RMSE≤12.4 J (for 170 test set compounds). The models were developed using Friedman Lack-of-Fit as a scoring function, which allows avoiding an overfitting. All the models have simple descriptors as basis functions and include linear splines. Furthermore, the applied descriptors do not require expensive calculation procedures, namely, non-empirical quantum-chemical calculations, complex iterative procedures, real space electron density analysis, etc. Most descriptors are based on structural and topological analysis and a part of them require very cheap semi-empirical PM6 calculations. The prediction takes a few minutes as an average, and most of the time is for the structure preparation and manual calculation of the descriptor "Increment", which is based on our recent incremental theory.

Adopting chitosan supported Ag and Ag2O nano-clusters for catalytic hydrogenation of CO2 to formic acid: A quantum semi-empirical calculation

This study's objective is to examine the potential of the chitosan-supported Ag and Ag2O nanoparticles on the reduction of CO2. The transition state of the reduction reaction was systematically calculated using the nudged elastic band and the semi-empirical tight-binding calculations. It is found that the large charge polarization on the Ag and Ag2O nanoclusters can modulate chitosan's surface reactivity. The formation of the metal hydrates is the rate-determining step for reducing CO2. The calculated activation energy of the order of 1.5 eV demonstrates that Ag and Ag2O /chitosan could be used as catalysts for converting to CO2 formic acid.

C–H bond dissociation enthalpy prediction with machine learning reinforced semi-empirical quantum mechanical calculations

Abstract We introduce a combined fast semi-empirical quantum mechanical and machine learning (SQM/ML) approach capable of matching the C–H bond dissociation enthalpies (BDEs) computed with the highly accurate (RO)CBS-QB3 method. The usefulness of our proposed SQM/ML model is corroborated by the fact that a single C–H BDE of a molecule is calculated in seconds and the mean absolute error amounts to only 1 to 2 kcal/mol.

Blue-emitting Bi3+-doped K2MGeO5 (M = Hf, Zr) phosphors for full-spectrum WLED: Crystal structure, electronic structure and luminescent properties

Developing efficient and stable blue-emitting phosphors has always been crucial for advancing full-spectrum WLED technology. In this study, the germanates K2MGeO5 (M = Zr, Hf) were chosen as the host to develop novel NUV-excited blue-emitting phosphors. The crystal structure, electronic structure, and luminescence properties of K2MGeO5:Bi3+ were comprehensively investigated using X-ray powder diffraction, Rietveld refinement, photoluminescent spectra analysis, and density functional theory calculations. By integrating experimental data with semi-empirical formula calculations, the excitation and emission sources were elucidated. Furthermore, the influence of Bi3+'s local coordination environment on its luminescence performance and the concentration quenching mechanism in K2MGeO5:Bi3+ were thoroughly examined. A full-spectrum WLED was fabricated by synergistically combining K2ZrGeO5:Bi3+, commercially available green and red phosphors with a 365 nm LED chip. It is anticipated that this study could provide insights for the advancement of blue phosphors excited by near-ultraviolet light.

A simple indicator displacement assay ensemble for rapid and selective determination and naked-eye detection of Cu2+ and citrate through a novel diazo-based colorimetric chemosensor

ABSTRACT Citrate as a naturally occurring ion with important biological roles, especially in metabolic cycles, and as an important biomarker in the early diagnosis of many diseases, including prostate cancer, has received special attention. In the present study, a novel, sensitive, fast and simple colorimetric bisazo-based chemosensor was developed for sequential determination and naked-eye detection of Cu2+ and citrate. UV-Vis spectroscopic studies showed that Reactive Red 141 (RR141) exhibits high selectivity and sensitivity towards Cu2+ in the presence of many other cations in deionised water (10 mmol L−1 MES(2-(N-morpholino)ethanesulfonic acid)/NaOH buffer solution, pH 6.0) and complex real samples as well. The obvious colorimetric response while interaction with copper and citrate is due to complexation between RR141 and Cu2+, and then the citrate detection occurred through metal transfer from the RR141-Cu2+ towards the citrate anion releasing RR141 via an indicator displacement route. The mechanism of detection has been carefully investigated using the semi-empirical calculation method of MM2 molecular mechanics. The proposed detection method for citrate ion has a detection limit of 0.15 µmol L−1 and a satisfactory linear range of 1.92–45.96 µmol L−1, which is consistent with the desired average diagnostic concentration in prostate cancer (less than 2 mmol L−1). Our studies’ results indicate the high capability of the developed chemical sensor for quantitative and qualitative citrate detection in various complex media, including biological and clinical real samples.

Investigating corrosion failure in N80 carbon Steel: Experimental and theoretical insights into isonicotinohydrazide derivatives as inhibitors in acidic conditions

This research work is a detailed analysis of the failure mechanisms related to the corrosion of N80 carbon steel (N80-CS) in an acidic environment (15 wt% HCl), commonly encountered in oil and gas industry pipelines. The study investigates the corrosion failure, focusing on the interactions with two isonicotinohydrazide derivatives, namely N'-[(Z)-furan-2-ylmethylidene]pyridine-4-carbohydrazide (FMI) and N'-[(Z)-phenylmethylidene]pyridine-4-carbohydrazide (BIH). A combination of experimental and theoretical methods, including weight loss (WL), electrochemical analysis, Field Emission Scanning Electron Microscopy (FE-SEM), Atomic Force Microscopy (AFM), Density Functional Tight Binding (DFTB) semi-empirical calculations, frontier molecular orbital analyses, Fukui function studies, and molecular dynamics simulations, was utilized to identify the corrosion and corrosion inhibition behavior of N80-CS. Experimental findings demonstrated that the inhibition performance of FMI and BIH increased proportionally with the concentration of these inhibitors, demonstrating inhibition efficiencies of 95 %, and 98 %, respectively. This performance was reflected by the enhancement of polarization resistance and a significant reduction in corrosion current density at optimum concentrations. Additionally, they demonstrated exceptional corrosion protection capabilities over prolonged immersion times (up to 5 days) and only a moderate decrease in efficiency with increasing temperature, highlighting their robustness under varying operating conditions. The adsorption behavior of these inhibitors was observed to be in agreement with the Langmuir adsorption model, suggesting physicochemical interactions occurring at the N80-CS surface, specifically covalent bonding. Theoretical studies from DFT, molecular dynamics, and DFTB strengthened experimental results, confirming strong interactions between the inhibitors’ atoms and the surface. Through the integrative experimental and theoretical approach of this study, a comprehensive understanding of the mechanisms and inhibition performance of these studied compounds was achieved. The findings can serve as an invaluable resource towards the development of more efficient corrosion inhibitors, further contributing to the broader field of corrosion and prevention strategies.

Characterization of Kuwait Crude Oil via Terahertz Fourier Transform Spectroscopy

We report the experimental and theoretical terahertz (THz) absorption characteristics of unprocessed crude oils from Kuwait oil wells. Using THz Fourier transform spectroscopy technique and semi-empirical computational chemistry calculation, five of the 30 crude oils revealed characteristic absorbance spectra. Experimental data showed absorption peaks at 6.0 THz, 7.7 THz, 13 THz, and 16 THz. On the other hand, the calculated spectral bands of 10 nonane molecules were found at around 2.8 THz, 7.7 THz, 10 THz, and 16 THz. Although only two bands were predicted by the calculation, adding alkane molecules of different lengths (pentane to decane) resulted in the formation of new bands. These preliminary results suggest that there is a mixture of different alkanes present in the investigated samples, a typical characteristic of unprocessed crude oil.

Reversible hydrogen storage and release mechanism of a B2N monolayer: a first-principles insight.

It was found the physical adsorption could be an efficient strategy for high capacity, high efficiency and high safety in hydrogen storage. In this research, a systematic investigation into the potential of the B2N monolayer as an excellent physical adsorption hydrogen storage material is conducted by utilizing the first-principles calculation method. The findings of the investigation demonstrate that the B2N monolayer has a planar lattice and excellent structural stability. It is possible for H2 molecules to adsorb onto the B2N monolayer spontaneously. Both the individual adsorption and saturation adsorption corresponded to average adsorption energies ranging from -0.221 to -0.194 eV, fulfilling the physical adsorption criteria. In the case of saturation adsorption, a 1 × 2 × 1 B2N supercell can store a total of 24 H2 molecules, with the hydrogen gravimetric density up to 14.511 wt% and volumetric density up to 138 g L-1. A semi-empirical calculation method is used to research the performance of the system in terms of adsorption and desorption with actual temperature and pressure conditions. Under the actual conditions with adsorption carried out at 30 atm/233 K and desorption carried out at 3 atm/358 K, the maximal reversible hydrogen storage capacity of the hydrogen storage system based on the B2N monolayer can still reach 12.157 wt%, which is superior to that of many other boron-nitrogen compounds and metal-free functionalized hydrogen storage materials. The findings of this work indicate that the pristine B2N monolayer is one of the promising physical adsorption materials which could achieve excellent reversible hydrogen storage under defined conditions.

Computational and experimental assessment of efficient dye adsorption method from aqueous effluents by halloysite and palygorskite clay minerals.

The removal of dyes from effluents of textile industries represents a technological challenge, due to their significant environmental impact. The application of halloysite (Hal) and palygorskite (Pal) clay minerals as adsorbents for the removal of Congo red (CR) and methylene blue (MB) was evaluated in this work. The materials were applied both in natural and acid-treated forms, and characterized by XRD, XPS, SEM-EDS, FTIR, and N2 adsorption-desorption isotherm techniques to identify their properties and main active sites. The adsorbents showed potential to remove CR (> 98%) and MB (> 85%) within 180min, using 0.3g adsorbent and initial dye concentration of 250mg L-1. Semi-empirical quantum mechanical calculations (SQM) confirmed the interaction mechanism between dyes and the adsorbents via chemisorption (- 69.0kcalmol-1 < Eads < - 28.8kcalmol-1), which was further observed experimentally due to the high fit of adsorption data to pseudo-second order kinetic model (R2 > 0.99) and Langmuir isotherm (R2 > 0.98). The use of Pal and Hal to remove dyes was proven to be economically and environmentally viable for industrial application.

Investigating interactions between derivatives of graphene nanosheets and articaine for prolonged dental anesthetic effects: A multiscale modeling study

The controlled release of anesthetic molecules for extended local anesthesia has emerged as a promising strategy in pain management. In this context, we focus on the interactions between graphene nanosheet derivatives (GND) and the anesthetic molecule articaine (ART). Our motivation for studying ART adsorption stems from our previous extensive computational study, which demonstrated that among local anesthetic molecules, ART shows the strongest binding to pristine graphene. To understand the binding of articaine to graphene nanosheets, we conducted an extensive multiscale modeling study using modern levels of theory. We employed a combination of semiempirical (GFN-xTB) and density functional theory calculations (DFT and TD-DFT) to obtain reliable geometrical parameters, binding energies, and quantum-molecular descriptors, crucial for understanding the binding mechanism of articaine. Detailed electron density analysis within the considered systems provided important insights into the noncovalent binding between graphene derivatives and articaine. Additionally, to observe the dynamics and behavior of the system in a biologically relevant environment, we conducted molecular dynamics (MD) simulations based on the GFN-FF method. This study aims to advance the development of effective drug-carrier systems for sustained pain relief. We intend to identify the graphene modifications that could lead to stronger binding of articaine, thereby enabling slower release and prolonged anesthetic effect.