Neighboring Elements Research Articles

Absolute nuclear charge radius by Na-like spectral line separation in high-Z elements

Abstract We describe a novel technique to determine absolute nuclear radii of high-Z nuclides. Utilizing accurate theoretical atomic structure calculations together with precise measurements of extreme ultraviolet transitions in highly charged ions this method allows for precise determinations of absolute nuclear charge radii based upon the well-known nuclear radii of their neighboring elements. This method can work for elements without stable isotopes, and its accuracy may be competitive with current methods (electron scattering and muonic x-ray spectroscopy).

A bound preserving cut discontinuous Galerkin method for one dimensional hyperbolic conservation laws

In this paper we present a family of high order cut finite element methods with bound preserving properties for hyperbolic conservation laws in one space dimension. The methods are based on the discontinuous Galerkin framework and use a regular background mesh, where interior boundaries are allowed to cut through the mesh arbitrarily. Our methods include ghost penalty stabilization to handle small cut elements and a new reconstruction of the approximation on macro-elements, which are local patches consisting of cut and un-cut neighboring elements that are connected by stabilization. We show that the reconstructed solution retains conservation and order of convergence. Our lowest order scheme results in a piecewise constant solution that satisfies a maximum principle for scalar hyperbolic conservation laws. When the lowest order scheme is applied to the Euler equations, the scheme is positivity preserving in the sense that positivity of pressure and density are retained. For the high order schemes, suitable bound preserving limiters are applied to the reconstructed solution on macro-elements. In the scalar case, a maximum principle limiter is applied, which ensures that the limited approximation satisfies the maximum principle. Correspondingly, we use a positivity preserving limiter for the Euler equations, and show that our scheme is positivity preserving. In the presence of shocks additional limiting is needed to avoid oscillations, hence we apply a standard TVB limiter to the reconstructed solution. The time step restrictions are of the same order as for the corresponding discontinuous Galerkin methods on the background mesh. Numerical computations illustrate accuracy, bound preservation, and shock capturing capabilities of the proposed schemes.

Camouflaged named entity recognition in 2D sentence representation

Organizing all spans of a sentence into a two-dimensional (2D) representation unfolds a semantic plane. It has the advantage to resolve nested semantic structures and to build linguistic dependencies across a whole sentence. The main problem is that neighboring elements in the semantic plane are span representations referred to overlapped phrases. Because these representations share the same contextual features in a sentence, a true entity representation is early faded into the background surroundings. It leads to camouflaged named entities in the 2D sentence representation. In this paper, we propose a finer-scale and coarse-scale sentence representation to support camouflaged named entity recognition. The mixed-scale representation has the ability to encode differential clues between entity representations. It is effective to distinguish entity representations from the background surroundings for recognizing camouflaged named entities. Compared with the state-of-the-art models, the results show that our method achieves competitive performance on five public datasets.

Resolving the chromatin impact of mosaic variants with targeted Fiber-seq.

Accurately quantifying the functional consequences of non-coding mosaic variants requires the pairing of DNA sequence with both accessible and closed chromatin architectures along individual DNA molecules-a pairing that cannot be achieved using traditional fragmentation-based chromatin assays. We demonstrate that targeted single-molecule chromatin fiber sequencing (Fiber-seq) achieves this, permitting single-molecule, long-read genomic and epigenomic profiling across targeted >100 kilobase loci with ~10-fold enrichment over untargeted sequencing. Targeted Fiber-seq reveals that pathogenic expansions of the DMPK CTG repeat that underlie Myotonic Dystrophy 1 are characterized by somatic instability and disruption of multiple nearby regulatory elements, both of which are repeat length-dependent. Furthermore, we reveal that therapeutic adenine base editing of the segmentally duplicated γ-globin (HBG1/HBG2) promoters in primary human hematopoietic cells induced towards an erythroblast lineage increases the accessibility of the HBG1 promoter as well as neighboring regulatory elements. Overall, we find that these non-protein coding mosaic variants can have complex impacts on chromatin architectures, including extending beyond the regulatory element harboring the variant.

Model order reduction by convex displacement interpolation

We present a nonlinear interpolation technique for parametric fields that exploits optimal transportation of coherent structures of the solution to achieve accurate performance. The approach generalizes the nonlinear interpolation procedure introduced in [Iollo, Taddei, J. Comput. Phys., 2022] to multi-dimensional parameter domains and to datasets of several snapshots. Given a library of high-fidelity simulations, we rely on a scalar testing function and on a point set registration method to identify coherent structures of the solution field in the form of sorted point clouds. Given a new parameter value, we exploit a regression method to predict the new point cloud; then, we resort to a boundary-aware registration technique to define bijective mappings that deform the new point cloud into the point clouds of the neighboring elements of the dataset, while preserving the boundary of the domain; finally, we define the estimate as a weighted combination of modes obtained by composing the neighboring snapshots with the previously-built mappings. We present several numerical examples for compressible and incompressible, viscous and inviscid flows to demonstrate the accuracy of the method. Furthermore, we employ the nonlinear interpolation procedure to augment the dataset of simulations for linear-subspace projection-based model reduction: our data augmentation procedure is designed to reduce offline costs — which are dominated by snapshot generation — of model reduction techniques for nonlinear advection-dominated problems.

Comparison of simplified bone-screw interface models in materially nonlinear μFE simulations

Micro finite-element (μFE) simulations serve as a crucial research tool to assist laboratory experiments in the biomechanical assessment of screw anchorage in bone. However, accurately modelling the interface between bone and screw threads at the microscale poses a significant challenge. Currently, the gold-standard approach involves employing computationally intensive physical contact models to simulate this interface. This study compared nonlinear μFE predictions of deformations, whole-construct stiffness, maximum force and damage patterns of three different computationally efficient simplified interface approaches to the general contact interface in Abaqus Explicit, which was defined as gold-standard and reference model. The μCT images (resolution: 32.8 μm) of two human radii with varying bone volume fractions were utilized and a screw was virtually inserted up to 50% and 100% of the volar-dorsal cortex distance. Materially nonlinear μFE models were generated and loaded in tension, compression and shear. In a first step, the common simplification of using a fully-bonded interface was compared to the general contact interface, revealing overestimations of whole-construct stiffness (19% on average) and maximum force (26% on average), along with inaccurate damage pattern replications. To enhance predictions, two additional simplified interface models were compared: tensionally strained element deletion (TED) and a novel modification of TED (TED-M). TED deletes interface elements strained in tension based on a linear-elastic simulation before the actual simulation. TED-M extends the remaining contact interface of TED by incorporating neighboring elements to the contact area. Both TED and TED-M reduced the errors in whole-construct stiffness and maximum force and improved the replication of the damage distributions in comparison to the fully-bonded approach. TED was better in predicting whole-construct stiffness (average error of 1%), while TED-M showed lowest errors in maximum force (1% on average). In conclusion, both TED and TED-M offer computationally efficient alternatives to physical contact modelling, although the fully-bonded interface may deliver sufficiently accurate predictions for many applications.

A single mesh approximation for modeling multiphase flow in heterogeneous porous media

The control volume finite element (CVFE) approach is based on the discretization of primary flow and transport unknowns on two different meshes. The element mesh, used to represent the physical properties of the medium, differs from the control volume mesh necessary to ensure a mass conservative solution. The inherent two mesh feature of the CVFE approximation introduces inconsistency in the transport solution due to the averaging of physical and computed quantities between neighboring elements in the mesh. In this work, we present a consistent approach for modeling multiphase flow and transport in heterogeneous porous media. The approach is applied in the CVFE framework by enabling the discretization of primary unknowns on a single mesh. We combine the discretization of an element-wise, discontinuous pressure approximation with a first-order, discontinuous velocity approximation to resolve the elliptic or parabolic flow problem. The effectiveness of the formulation is achieved by exploiting the same finite element mesh as the flow problem, when updating the saturation solution. This direct mapping between the flow and transport mesh is simple yet effective for establishing a consistent solution in addition to circumventing the non-physical mass leakage exhibited in the classical CVFE method. We describe the interface approximations and the discontinuous terms needed for consistent solutions. The method is well suited to model flow in complex geometrical subsurface domains and is shown to be numerically stable while providing locally and globally mass conservative solutions. We apply the approach to several domains with complex geometrical features to emphasize the superiority of the approximation over conventional methods. The analysis shows over two orders of magnitude reduction in solution error compared to the classical CVFE. The new formulation effectively captures accurate transport solutions, even in the presence of varying material properties, without the need for mesh refinement.

Physics-informed neural network simulation of two-phase flow in heterogeneous and fractured porous media

Physics-informed neural networks (PINNs) have received great attention as a promising paradigm for forward, inverse, and surrogate modeling of various physical processes with limited or no labeled data. However, PINNs are rarely used to predict two-phase flow in heterogeneous and fractured porous media, which is critical to lots of subsurface applications, due to the significant challenges in their training. In this work, we present an Enriched Physics-Informed Neural Network (E-PINN) to overcome these barriers and realize the simulation of such flow. Specifically, the Embedded Discrete Fracture Model (EDFM) is adopted to explicitly represent fractures, and then the finite volume method (FVM) instead of the Automatic Differentiation (AD) is used to evaluate spatial derivatives and construct the physics-informed loss function, so that the flux continuity between neighboring elements with different properties (e.g. matrix and fracture) can be defined rigorously. Besides, we develop a novel physics-informed neural network (NN) architecture adopting the adjacency-location anchoring, adaptive activation function, skip connection and gated updating to enrich the pressure information and enhance the learning ability of NN. Additionally, the initial and boundary conditions are constrained through a hard approach, which encodes them into network design, to improve the accuracy and efficiency of network training. In order to further reduce the difficulty of training, the Implicit-Pressure Explicit-Saturation (IMPES) scheme is used to calculate pressure and saturation, in which only the pressure needs to be solved by training NN. Finally, the superiority and applicability of E-PINN to complex practical problems is demonstrated through the simulations of immiscible displacement in 2D/3D heterogeneous and fractured reservoirs.

Image segmentation with Cellular Automata

Image segmentation is a computer vision technique that involves dividing an image into distinct and meaningful regions or segments. The objective was to partition the image into areas that share similar visual characteristics. Noise and undesirable artifacts introduce inconsistencies and irregularities in image data. These inconsistencies severely affect the ability of most segmentation algorithms to distinguish between true image features, leading to less reliable and lower-quality results. Cellular Automata (CA) is a computational concept that consists of a grid of cells, each of which can be in a finite number of states. These cells evolve over discrete time steps based on a set of predefined rules that dictate how a cell's state changes according to its own state and the states of its neighboring cells. In this paper, a new segmentation approach based on the CA model was introduced. The proposed approach consisted of three phases. In the initial two phases of the process, the primary objective was to eliminate noise and undesirable artifacts that can interfere with the identification of regions exhibiting similar visual characteristics. To achieve this, a set of rules is designed to modify the state value of each cell or pixel based on the states of its neighboring elements. In the third phase, each element is assigned a state that is chosen from a set of predefined states. These states directly represent the final segmentation values for the corresponding elements. The proposed method was evaluated using different images, considering important quality indices. The experimental results indicated that the proposed approach produces better-segmented images in terms of quality and robustness.

Synthesis of Antenna Array Radiation Pattern at Large Scanning Angles Using Genetic Algorithm

At present, in most modern communication systems, for example, in modern satellite terminals, the use of scanning beam antennas, i. e. antenna arrays is assumed. At the same time, at large scanning angles, the side lobe level (SLL) increases strongly and decrease in the gain is observed. In this regard, the problem of finding a procedure for synthesizing an amplitude-phase distribution (APD) with low SLL and high gain (G) at large scanning angles comes up. One of the ways to reduce SLL and compensate for the decrease in G is to synthesize the optimal APD (in terms of the maximum G and minimum SLL) using optimization algorithms. At the same time, taking into account the characteristics of the radiation pattern of the array emitters requires numerical electrodynamic calculation. The goal of this paper is to develop a procedure for the synthesis of APD with low SLL for linear and rectangular antenna arrays at various, including large, scanning angles and compensation for G reduction using a genetic algorithm and numerical electrodynamic calculation. The methods for studying the characteristics of antenna radiators are numerical electrodynamic modeling by the finite element method (FEM) in Ansys HFSS computer-aided design system and optimization of the APD for a given radiation pattern(RP) by a random search method using partial diagrams of antenna elements. The novelty is the combination of accurate numerical electrodynamic calculation of the RP of antenna elements and optimization search for APD for the synthesis of the required RP using partial diagrams. As a result, a procedure for APD synthesis of linear and uniform rectangular equidistant (for example, 8- and 64-element) antenna arrays has been developed, taking into account the exact electrodynamic characteristics of antenna elements and their mutual resistance. Radiation patterns were obtained taking into account the effect of neighboring elements, with the help of which, using a genetic algorithm, APDs on emitters were found at different scanning angles. The change in SLL and G of the antenna array is analyzed at different scanning angles using different APDs. The proposed algorithm allows to synthesize APD for a RP with low SLL and high G at scanning angles up to 40° for linear antenna array and up to 80° in the case of a uniform rectangular antenna array. The results of this work are relevant in the problems of radiation pattern synthesis, since the proposed solution provides a significant gain in the radiation pattern synthesis rate of APD of linear and rectangular antenna arrays, especially for systems with a large number of antenna elements. At the same time, it is possible to maintain a high G at large scanning angles, and achieve a significant reduction of SLL.

Composite Backbone Small Object Detection Based on Context and Multi-Scale Information with Attention Mechanism

Object detection has gained widespread application across various domains; nevertheless, small object detection still presents numerous challenges due to the inherent limitations of small objects, such as their limited resolution and susceptibility to interference from neighboring elements. To improve detection accuracy of small objects, this study presents a novel method that integrates context information, attention mechanism, and multi-scale information. First, to realize feature augmentation, a composite backbone network is employed which can jointly extract object features. On this basis, to efficiently incorporate context information and focus on key features, the composite dilated convolution and attention module (CDAM) is designed, consisting of a composite dilated convolution module (CDM) and convolutional block attention module (CBAM). Then, a feature elimination module (FEM) is introduced to reduce the feature proportion of medium and large objects on feature layers; the impact of neighboring objects on small object detection can thereby be mitigated. Experiments conducted on MS COCO validate the superior performance of the method compared with baseline detectors, while it yields an average enhancement of 0.8% in overall detection accuracy, with a notable enhancement of 2.7% in small object detection.

Flexible switch matrix addressable electrode arrays with organic electrochemical transistor and pn diode technology

Due to their effective ionic-to-electronic signal conversion and mechanical flexibility, organic neural implants hold considerable promise for biocompatible neural interfaces. Current approaches are, however, primarily limited to passive electrodes due to a lack of circuit components to realize complex active circuits at the front-end. Here, we introduce a p-n organic electrochemical diode using complementary p- and n-type conducting polymer films embedded in a 15-μm -diameter vertical stack. Leveraging the efficient motion of encapsulated cations inside this polymer stack and the opposite doping mechanisms of the constituent polymers, we demonstrate high current rectification ratios (105\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${10}^{5}$$\\end{document}) and fast switching speeds (230 μs). We integrate p-n organic electrochemical diodes with organic electrochemical transistors in the front-end pixel of a recording array. This configuration facilitates the access of organic electrochemical transistor output currents within a large network operating in the same electrolyte, while minimizing crosstalk from neighboring elements due to minimized reverse-biased leakage. Furthermore, we use these devices to fabricate time-division-multiplexed amplifier arrays. Lastly, we show that, when fabricated in a shank format, this technology enables the multiplexing of amplified local field potentials directly in the active recording pixel (26-μm diameter) in a minimally invasive form factor with shank cross-sectional dimensions of only 50×8 μm2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mu m}^{2}$$\\end{document}.

Adapting to climate change: snow load assessment of snow galleries on the Iron Ore Line in Northern Sweden

The snow galleries along the Iron Ore railway line in northern Sweden have faced problems in recent years due to increasingly large snow loads, and several galleries have been damaged. These incidents motivated an evaluation of the maximum load supported by the galleries before collapse, which is presented in this study. In 2021, a monitoring system was installed in one of the main frames of two snow galleries built in the 1950s to follow up with temperature and displacements, including a trigger that sends out a warning message when a critical load is reached. A literature review on snow loads was performed, followed by calculations on snow distribution on the galleries based on the Eurocodes and National Swedish Standards. Finite element 2D and 3D models were created using AxisVM to accurately assess the efforts in the structural elements. Analysis and discussion are complemented by observations from site visits. It was concluded that the critical loads supported by the galleries are lower than the requirements of today’s standards, but since secondary construction elements were damaged before the main frames reached their full capacity, no major collapse has yet taken place. The cobweb effect (load re-distribution between the neighboring elements in a 3D structure) influenced the behavior of the galleries in the 3D analysis and the capacity of the main frames proved to be significantly increased compared to the 2D assessment.

ACHIEVING THE REQUIRED MACHINING ACCURACY BY CORRECTING ERRORS USING VARIABLES OF PARAMETRIC FUNCTIONS

Nowadays, the machine-building complex is actively saturated with high-tech equipment, without which an enterprise cannot enter the global market or keep up with its competitors. And it's not just about using systems with artificial intelligence elements as scientific and technical solutions to improve quality and reduce production costs. It is also about reducing the time required to prepare products for production and bringing them to the consumer ahead of schedule. Today, the most preferred method in the competitive struggle is to equip CAD/CAM production with computer-aided design systems. With their help, engineering and technology services cannot only design models of new products but also generate CNC control software for manufacturing parts on CNC machines. At the same time, program frames composed in G-codes have several drawbacks, the main one being a "rigid" algorithm of action, i.e., the lack of variability for final solutions when the required accuracy is achieved by correcting the numerical values of the coordinates of the points of formation of individual surfaces in the PC. In this case, the operator largely intuitively corrects individual program frames based on his or her own experience, which is almost impossible without appropriate calculations for controllers with linear-circular and angular motions. In addition, after some such changes, the program loses its geometric adequacy to the part drawing, and by correcting one element of the shaped profile, we invariably violate the laws of contact with neighboring elements specified in the drawing. The paper considers the analytical geometry apparatus that allows a line on a double curvature surface, the theoretical trajectory of tool movement, to be represented not as a set of scalar points but in a vector representation, considering its possible torsion. This approach is ensured by parametric programming with computational frames of point coordinates and logical transitions, determining the angles of inclination of the cutter axis relative to the normal to the surface in the case of multi-axis machining. However, the main advantage of this method is the ability to correct processing errors not by local changes in the numerical values of the coordinates, but by introducing correction coefficients into the equation of the shape formation trajectory - reactors for the appearance of errors in the shape or location of surfaces, arising, for example, from elastic movements. The value of the coefficients per group of personnel can be set in the process of research and industrial production and change depending on the properties of the blanks. For example, according to the current standards, variations in hardness of up to 10-12% were allowed for the blanks of rolling mill rolls. The article presents the experimental data of the research of the correction task in a parametric form and the results of their application for machining parts with radius cutters in real production..

Chemical Species Change of Radionuclides by Microorganisms: Effects of Exudated Siderophores

Microbial exudates including siderophore, which changes chemical species of actinides and lanthanides. We have investigated effects of desferrioxamine B (DFOB; one of the siderophores) and siderophore-like organic molecules (SLOM) on the adsorption of lanthanides by microbial cells, aluminium oxide (Al2O3), and manganese (Mn) oxides. When DFOB was present, the distribution coefficients of cerium (Ce) were measured to be lower than those of neighboring elements of lanthanum (La) and praseodymium (Pr) (Negative anomaly of Ce adsorption). Even though initial oxidation state of Ce in the solution was III, that was changed to IV after the addition of DFOB, indicating that Ce(III) was oxidized by forming complex with DFOB. When lanthanides were adsorbed by biogenic Mn(IV) oxides, negative anomaly of Ce adsorption was observed in the sorption in alkaline solution. Ce(III) was oxidized to forme the complexes of Ce(IV) with SLOM in the solution. These results show that siderophore possesses high performance of oxidation of Ce(III) to Ce(IV) during association, affectiong the adsorption behavior of Ce. After Fukushima accident, radioactive Cs accumulation by Eleutherococcus sciadophylloides (Koshiabura) caused by the dissolution of Fe from soil around the roots, that was dominated by siderophore releasing microorganisms (SB). These SBs may enhance dissolution of iron (Fe) and uranium (U) phases in the nuclear fuel debris formed in the nuclear reactors in Fukushima Daiichi nuclear power plant. Thus, in the interaction between microorganisms and radionuclides, SLOMs discharged by microorganisms are deeply involved in the chemical state change of radionuclides.

EXPLORING THE INTRICATE ARCHITECTURAL FABRIC OF A HISTORIC ARAB MEDINA: A STUDY OF NABLUS OLD TOWN

Scholars have been fascinated with the complex and intricate urban structure within Islamic cities. Over the years, they have used various terms and designations, such as labyrinthine, maze-like, spontaneous, organic, and similar descriptors, to characterize this intricate nature. However, these terms often suggest a lack of order or systematic organization, which limits our understanding of how urban spaces were created and regulated, especially at the micro-level of city development. This essay seeks to provide a case-based insight into the processes of creating, adapting, and managing spaces in pre-modern Islamic cities by examining original archival documents from the Ottoman period. The study concludes that the dynamic character of the decision-making process, influenced by a framework for decision and counterbalancing practices and amplified by the ability to divide the entire structure or building into components that can be interchanged with neighboring elements, opened the door to a limitless range of spatial arrangements that led to the emergence of complex interconnections and apparent irregularities.

(Invited) Neutrons as a Powerful Tool for Better Understanding Electrochemistry on Various Length and Time Scales

Today, sophisticated methods are applied to gain a deeper understanding of processes in in-situ electrochemistry. There are few ways to study electrochemically relevant processes on different length and/or time scales without affecting the processes themselves. Probes that cover a sufficient volume of the sample non-destructively and are sensitive to the important charge carriers are in great demand. For such purposes, neutrons are an ideal candidate to perform this task because they have relatively high sensitivity to the light chemical elements, can distinguish neighboring elements of the periodic table, have a large beam cross-section, and possess high penetration depth for many types of materials.Therefore, many open questions are addressed to unique neutron techniques tailored to solve the open problems. This article gives an overview of some examples to show the advantages of using neutrons for special questions on different length and time scales [1]. For surface investigations of electrodes, the neutron depth profiling method (NDP) is suitable for characterizing the Li distribution up to the nano range [2]. Neutron diffraction is a powerful tool to better understand cylindrical cells at the atomic level, e.g. during the intercalation process [3]. Another strong field of neutrons is the monitoring of the wetting process during the electrolyte filling of prismatic hard case cells on the macroscopic length scale using the neutron imaging technique [4].

Batch process monitoring based on sequential phase division multiway sparse weighted neighborhood preserving embedding

Batch processes are often characterized by multiphase and different batch durations, which vary from phase to phase presenting multiple local neighborhood features. In this paper, a sequential phase division-multiway sparse weighted neighborhood preserving embedding method is proposed for monitoring batch processes more sensitively. First, batches with uneven durations are synchronized, and the phases are automatically determined in chronological order. Secondly, the nearest neighbors are computed at each phase and the optimal sparse representation (SR) is obtained based on the nearest neighbors. This improves the robustness of the algorithm to noise and outliers, and solves the problem of computational difficulties associated with global SR based. Thirdly, the distance values of the neighbor elements are considered to fully extract the neighbor structure when the optimal SR is calculated. Finally, after dimension reduction, T 2 and squared prediction error statistics are established in feature space and residual space respectively for fault detection. The effectiveness of the method is verified by a multiphase numerical simulation example and the penicillin fermentation process.

Bound Debris Expulsion from Neutron Star Merger Remnants

Many studies have found that neutron star mergers leave a fraction of the stars’ mass in bound orbits surrounding the resulting massive neutron star or black hole. This mass is a site of r-process nucleosynthesis and can generate a wind that contributes to a kilonova. However, comparatively little is known about the dynamics determining its mass or initial structure. Here we begin to investigate these questions, starting with the origin of the disk mass. Using tracer particle as well as discretized fluid data from numerical simulations, we identify where in the neutron stars the debris came from, the paths it takes in order to escape from the neutron stars’ interiors, and the times and locations at which its orbital properties diverge from those of neighboring fluid elements that end up remaining in the merged neutron star.

X-ray Diffraction, Spectroscopy, Optical Properties, NPA, NBO, FMO, and Hirshfeld Surface Analyses of Two Newly Synthesized Piperidinium Ionic Liquids

The present study elaborates on the synthesis, crystal structure, and computational studies of two new ionic liquids. In the crystal structure, [C5H12N][C21H14ClN2O2S] (4a), the anions form chains along the a-axis direction through C—H···π(ring) interactions. These are connected into layers that run approximately parallel to the ac plane by a variety of hydrogen bonds. In the compound structure, [C5H12N][C18H15N2O2S] (4b), the two ions are primarily associated by an N—H···N hydrogen bond. In the crystal structure, layers parallel to the bc plane are formed by pairs of C—H···O and N—H···S hydrogen bonds and by C—H···π(ring) interactions. A theoretical study reveals that 4a has lower energy than 4b and is more stable. The NBO and DOS studies further confine the liquids’ structural reactivity and electronic properties. The quantum theory of atoms in a molecule (QTAIM) analysis reveals the important non-covalent interactions among the fragments and charge transfer. The global reactivity descriptors indicate their molecular reactivity relationship with the presence of functional groups. The remarkable polarizability (αo) and hyperpolarizability (βo) values indicate their optical and nonlinear optical (NLO) properties. Furthermore, the analysis performed by CrystalExplorer shows the intermolecular interactions and reactive sites between cations and anions in ionic liquids. The 2D fingerprint plots and Hirshfeld surfaces indicate the major interactions of crystals with neighboring elements in crystal packing. For both compounds, the H···H interactions are significantly higher than the other element interactions.