Time Dependence Research Articles

ОЦІНКА ОБЧИСЛЮВАЛЬНОЇ СКЛАДНОСТІ ГЕНЕТИЧНОГО АЛГОРИТМУ

The article is devoted to the estimation of computational complexity of a genetic algorithm as one of the key tools for solving optimisation problems. The theoretical aspects of computational complexity of algorithms and the interrelation of elements of a genetic algorithm are considered. The main types of computational complexity of algorithms are described: time, simple and asymptotic. Five basic rules for calculating the asymptotic complexity are given. A mathematical apparatus for estimating the asymptotic complexity of a genetic algorithm is presented, which takes into account the costs of forming the initial population and performing evolution. Evolution occurs through iterations, during which generations of individuals are subjected to certain operations in order to find an optimal solution (crossing, mutation, chromosome decoding, etc.). GA, as a global search algorithm, is considered to find the optimal path without getting stuck in local minima. To assess the computational complexity of GA, we consider solving the traveling salesman problem (TSP) for 28 cities of Ukraine using a modified TSPLIB library and the DEAP platform created in the Python programming language. A block diagram of the GA is presented, the main elements of which are the tournament selection operator, the ordered crossover operator, and the inversion mutation operator. The influence of the population size and the number of generations on the asymptotic complexity of the genetic algorithm in solving the TSP problem is studied. The study considered changing the size of the GA population from 50 to 500 with a step of 50, while for each such value four sets of the number of generations were modelled: from 50 to 200 with a step of 50. Based on the obtained results, we show a linear dependence of the GA execution time on the size of the considered input data. It is shown that the smallest time complexity of the presented GA for the given TSP problem is 0.33848 seconds with a population size of 50 and a similar number of generations, while the largest value is 3.752734 seconds with a population size of 500 and a number of generations of 200. The obtained results can be used to optimise the performance of a GA in the TSP problem.

Predicting the rutting behaviour of asphalt concrete in the modified wheel tracking test using DEM and a cohesive viscoelastic–elastoplastic-damage contact model

AbstractThis study used an advanced modelling approach capable of capturing the complex behaviour of asphalt concrete to model the modified wheel tracking test using a recent advanced experimental test set-up in accordance with ASTM D8292-20. The modelling approach uses the discrete element method (DEM) to naturally produce the heterogeneous internal structure and governs the behaviour of asphalt concrete at the grain level by an interparticle contact model. The contact model used is capable of characterising the rate and time dependency, viscoelastic-damage, and plastic-damage behaviour of asphalt concrete utilising the coupling of an elastoplastic-damage law with a viscoelastic-damage law. Unlike the conventional wheel tracking tests run in a fixed boundary condition (fully confined), the modified wheel tracking test considers the effect of boundary conditions on the rutting behaviour of asphalt mixes. Through comparisons and verifications with laboratory data of the rutting test at different boundary conditions (fully confined and unconfined), the modelling approach shows its capability of capturing the rutting behaviour of asphalt concrete in the modified wheel tracking test. Micromechanics analysis shows that the third (tertiary) stage of rutting behaviour is due to the weakening of the internal structure of the asphalt samples with contact bond breaks over time, which is found in the unconfined test. Meanwhile, the tertiary stage hardly occurs in the fully confined test once densification leads to contact of the aggregate–aggregate skeleton, forming a rigid structure to resist the load with lateral support from the fixed boundary condition. Finally, a parametric study was also conducted to provide further insight into the current testing set-up, including the effect of the sample size and boundary condition on the rutting behaviour of asphalt concrete.

Image‐based inertial impact test for viscoelastic constitutive identification: A digital replica for error quantification

AbstractPolymers find widespread use in applications where they are subjected to impact loading. Therefore, understanding the time dependence of their mechanical response is critical to the design of structures subjected to these high strain rate environments. However, characterising these materials on microsecond time scales has proven challenging. Traditional experimental techniques rely on satisfying a number of limiting assumptions and typically do not provide direct measurements of the material parameters. Here, we propose a novel implementation of the image‐based inertial impact (IBII) test to extract viscoelastic constitutive parameters on these microsecond time scales using the stress gauge implementation of the virtual fields method. We validate the experiment using a digital replica approach in which the constitutive parameters are first extracted on a finite element model of an IBII test on a viscoelastic material. The finite element data are then used to synthetically deform computer‐generated grid images, which are then polluted with grey‐level noise to simulate the images that would be captured in a real‐life experiment. These images are processed identically to a physical experiment, and the identification is repeated using the full‐field displacements extracted from the computer‐generated images to determine the ideal processing parameters. Parameter identification was found to strongly depend on the processing parameters used to extract the kinematic fields from full‐field images, emphasising the need for computational validation before attempting a physical experiment to extract constitutive parameters. The IBII experimental method was found to be capable of simultaneously identifying the bulk modulus and the shear modulus along with their associated time constant.

Plastic deformation of dry fine-grained olivine aggregates under high pressures

Abstract This study investigates the effect of pressure on diffusion creep of dry San Carlos and synthetic (prepared by sol-gel method) olivine. We prepared dry (water content < 9 ppm wt) fine-grained (< 1 μm grain-size) olivine and deformed the samples (both San Carlos and solgel olivine in the same assembly) in the same sample assembly under high-pressure (P = 2.9–8.8 GPa) and modest temperatures (T = 980–1250 K) at a fixed strain-rate. Evolution of strength was studied using the radial X-ray diffraction from various diffraction planes. We found that San Carlos and sol-gel olivine show similar rheological behaviour (when normalized to the same grain-size). Stress estimated by the radial X-ray diffraction increases with time and initially shows similar values for all diffraction planes. In many cases, stress values start to depend on the diffraction planes in the later stage and time dependence becomes minor. The micro-structural observations show that grain-size increases during an experiment. The results are interpreted using a theory of radial X-ray diffraction and the theoretical models of diffusion and dislocation creep. We conclude that the initial stage of deformation is by diffusion creep, but deformation in the later stage is by dislocation creep. For dislocation creep, our results are in reasonable agreement with previous low temperature dislocation creep results after a correction of temperature effect. For diffusion creep, we obtain an activation volume of 7.0 ± 2.4 cm3/mol that is substantially smaller than the values reported on dislocation creep but agrees well with the results on grain-growth. By comparing the present results on dry olivine with the previous results on wet (water-saturated) olivine, we found that water enhances diffusion creep but only modestly in comparison to dislocation creep. The difference in the pressure and water content dependence between diffusion and dislocation creep has an important influence on the dominant deformation mechanisms of olivine in the upper mantle.

Chain dynamics of alternating polymers P(C n EG4)

Alternating or sequence defined polymers attract the attention of an increasing number of researchers recently. Due to their different blocks, they are very customizable and material properties can be tuned. In this publication, we present dynamical studies with focus on polymer dynamics, investigated by rheology and fast field cycling (FFC) NMR. The molecular weight dependencies of the relaxation time and zero-shear viscosity could hint to entanglement effects; however, the spectral shape of the viscoelastic data resembles the polymer dynamics of unentangled melts. Taking both techniques into account, i.e. rheology and FFC NMR leads to the conclusion that the relaxation spectra of the alternating polymer are similar to those of linear polymers.

Incorporating the Third Law of Geography with Spatial Attention Module–Convolutional Neural Network–Transformer for Fine-Grained Non-Stationary Air Quality Predictive Learning

Accurate air quality prediction is paramount in safeguarding public health and addressing air pollution control. However, previous studies often ignore the geographic similarity among different monitoring stations and face challenges in dynamically capturing different spatial–temporal relationships between stations. To address this, an air quality predictive learning approach incorporating the Third Law of Geography with SAM–CNN–Transformer is proposed. Firstly, the Third Law of Geography is incorporated to fully consider the geographical similarity among stations via a variogram and spatial clustering. Subsequently, a spatial–temporal attention convolutional network that combines the spatial attention module (SAM) with the convolutional neural network (CNN) and Transformer is designed. The SAM is employed to extract spatial–temporal features from the input data. The CNN is utilized to capture local information and relationships among each input feature. The Transformer is applied to capture time dependencies across long-distance time series. Finally, Shapley’s analysis is employed to interpret the model factors. Numerous experiments with two typical air pollutants (PM2.5, PM10) in Haikou City show that the proposed approach has better comprehensive performance than baseline models. The proposed approach offers an effective and practical methodology for fine-grained non-stationary air quality predictive learning.

Detection of recurrent fluorescence from anthracene using an electrostatic ion beam trap

The recurrent fluorescence of anthracene cations (C14H10+) was investigated using an electrostatic ion beam trap. The photon emission from the excited anthracene cations confined in the trap was observed in the order of milliseconds. The photon yields as a function of the wavelength in the range of 450–860 nm were measured by optical filters and a cooled photomultiplier, which revealed that the observed photon emission is attributed to the D2 → D0 transition of C14H10+. However, the time range of the photon emission is considerably longer than that of the spontaneous emission induced by the D2 → D0 transition (<1 μs). The number of photons emitted by the recurrent fluorescence of C14H10+ as a function of time was calculated using the previously reported theoretical decay rates of C14H10+ by recurrent fluorescence and thermal dissociation. The calculation results justified the time dependence of the measured number of photons over the extended time range. These results demonstrate that we successfully detected recurrent fluorescence photons emitted from excited anthracene cations.

Sequential Markov chain Monte Carlo for Lagrangian data assimilation with applications to unknown data locations

AbstractWe consider a class of high‐dimensional spatial filtering problems, where the spatial locations of observations are unknown and driven by the partially observed hidden signal. This problem is exceptionally challenging, as not only is it high‐dimensional, but the model for the signal yields longer‐range time dependences through the observation locations. Motivated by this model, we revisit a lesser‐known and provably convergent computational methodology from Berzuini et al. (1997, Journal of the American Statistical Association, 92, 1403–1412); Centanniand Minozzo (2006, Journal of the American Statistical Association, 101, 1582–1597); Martin et al. (2013, Annals of the Institute of Statistical Mathematics, 65, 413–437) that uses sequential Markov Chain Monte Carlo (MCMC) chains. We extend this methodology for data filtering problems with unknown observation locations. We benchmark our algorithms on linear Gaussian state‐space models against competing ensemble methods and demonstrate a significant improvement in both execution speed and accuracy. Finally, we implement a realistic case study on a high‐dimensional rotating shallow‐water model (of about – dimensions) with real and synthetic data. The data are provided by the National Oceanic and Atmospheric Administration (NOAA) and contain observations from ocean drifters in a domain of the Atlantic Ocean restricted to the longitude and latitude intervals , , respectively.

Loaded layer-cake model for cosmic ray interaction around exploding super-giant stars making black holes

The experiment AMS on the International Space-Station has produced accurate cosmic ray spectra for many chemical elements, both primaries like He, C, O, Fe, other cosmic ray (CR) primaries like Ne, Mg, and Si, secondaries like Li, Be, B, and of mixed provenance, like N, Na, and Al. The AMS spectra demonstrate that interaction is seriously diminishing fluxes up to a rigidity of about 100 GV, and so the existing models for CR interaction have to be re-examined. Based on earlier well-established ideas a model is proposed here that focusses on the cosmic ray interaction first in the wind shock shell of super giant stars, when the supernova driven shock races through, and second in the OB-Superbubble surrounding the SN: These stars include both red super-giant stars and blue super-giant stars; both produce black holes in their explosion, and drive winds and jets with electric currents. Variability of these winds or jets gives rise to temporary electric fields, as has recently been demonstrated, and discharge (so lightning) acceleration gives steep spectra, with synchrotron losses to p−5 in momentum p; these spectra are typically observed in both Galactic and some extragalactic radio filaments. Analogous hadron spectra p−4 excite a flat spectrum of magnetic irregularities in the bubble zone, which in turn yields a steep dependence of residence time versus energy, with power −5/3. This spectrum is indicated by the AMS data and appears to be required to explain the CR spectra below 100 GV. The emphasis in this paper is to work out the interaction of the freshly accelerated cosmic ray particles. In the model presented here the interaction is derived as a function of time, and then integrated, or developed to long times. The model gives a rigidity dependence of the secondary/primary ratio of slope −1/3 as well as the strong reduction of the primary fluxes below a rigidity of about 100 GV, relative to a power-law injection spectrum, with slope +2. The two key aspects based on blue super-giant stars and a magnetic irregularity spectrum in the bubble zone given by lightning are (i) a much larger column of interaction, allowed because of heavy element enrichment of the interaction zone, and (ii) even He, C, and O may have a small secondary contribution, as the difference to the Fe spectrum suggests; this small secondary component is visible in the 3He/4He ratio. The model may also explain the spectrum of CR anti-protons, the gamma-ray spectra of the Galaxy and the high energy neutrino spectrum of our Galaxy, including also red super-giant stars as sources. ISM-SNe, i.e. SN Ia and neutron-star SNe, contribute to CR protons and CR He nuclei.

Evaluation of stress-loaded ferromagnetic structures using the magnetic recording method and extended signal analysis

This paper presents a new method of analysis of results obtained using the Magnetic Recording Method (MRM), which was applied to inspect tensile stress-loaded ferromagnetic samples. The MRM method comprised a two-step procedure. In the first step, a strictly defined magnetic pattern is recorded on the samples using an array of permanent magnets, and then the original residual magnetization is measured. Next, the samples are subjected to static stress. In the second step, the residual magnetization measurement is repeated. Comparative analysis of the measurement results allows for an accurate and unambiguous assessment of applied stress in steel components. Moreover, the magnetization was measured again after three years to investigate the effect of time on magnetization patterns. The study confirms that the method is stable and has a negligible time dependence.

Separation of kinetic rate orders in extreme ultraviolet transient grating spectroscopy

We present an extreme ultraviolet (EUV) transient grating (TG) experiment of the spinel Co3O4 compound using tuneable incident energies across the Co M2,3-edge and a 395 nm probe pulse, detecting both the first and the second diffraction orders (SDOs). While the first diffraction order shows a monotonous behavior as a function of time, with a sharp response at t = 0, followed by a weak sub-picosecond component and a nearly constant signal thereafter, the time dependence of SDO varies dramatically with the incident energy as it is tuned across the Co M-edge, with the appearance of a component at t > 1 ps that grows with increasing energy. The results are rationalized in terms of the deviations of the initial grating from sinusoidal to non-sinusoidal, namely a flattening of the grating pattern, that introduces new Fourier components. These deviations are due to higher order, three-body terms in the population relaxation kinetics. The present results highlight the use of the SDO response in EUV TG as a tool to identify higher order terms in the population kinetics.

Unsupervised anomaly detection for power batteries: A temporal convolution autoencoder framework

Abstract To prevent potential abnormalities from escalating into critical faults, a rapid and precise algorithm should be employed for detecting power battery anomalies. An unsupervised model based on temporal convolutional autoencoder (TCAE) that can quickly and accurately identify abnormal power battery data was proposed. Its encoder utilized a temporal convolutional network (TCN) structure with residuals to parallelly process data while capturing time dependencies. A novel TCN structure with an effect–cause relationship was developed for the decoder. The same-time-scale connection was established between the encoder and decoder to improve the model performance. The validity of the proposed model was confirmed using a real-world car dataset. Compared with the GRU-AE model, the proposed approach reduced the parameter count and mean square error by 19.5% and 71.9%, respectively. This study provides insights into the intelligent battery pack abnormality detection technology.

Time and power dependence of laser-induced photodamage on human sperm revealed by longitudinal rolling measurement using optical tweezers

Lasers are widely applied in assisted reproductive technologies, including sperm fixation, sperm selection and intracytoplasmic sperm injections, to reduce procedure time and improve consistency and reproducibility. However, quantitative studies on laser-induced photodamage of sperm are lacking. In this study, we demonstrated that, by using optical tweezers, the kinematic parameters of freely swimming sperm are correlated with the frequency as well as the percentage of pausing duration of longitudinal rolling of the same sperm head in the optical trap. Furthermore, by trapping individual sperm cells using 1064-nm optical tweezers, we quantitatively characterized the time-dependence of longitudinal rolling frequency and percentage of pausing duration of sperm under different laser powers. Our study revealed that, as trapping time and the laser power time increase, the longitudinal rolling frequency of the optically trapped sperm decreases with an increasing percentage of pausing duration, which characterizes the effect of laser power and duration on the photodamage of individual sperm cells. Our study provides experimental basis for the optimization of laser application in assisted reproductive technology, which may reduce the photodamage-induced biosafety risk in the future.

Affect and satisfaction time dependency: An experience sampling study.

The aim of this study was to test the time dependency between affect and satisfaction on a momentary level. Ninety-eight students participated in the study, using the experience sampling method. Affect and satisfaction scales were administered five times a day for 7 days via handheld devices, sampling the whole awake period with ratings approximately 3-4 h apart. The aim of this study was to examine the cross-correlation between affect and satisfaction at the intra-individual level and to test their temporal consistency via lagged cross-correlations. On average, satisfaction was robustly associated with positive affect (PA; mean correlation 0.50) and negative affect (NA; mean correlation -0.38). The correlation of satisfaction with affect factors showed a consistent temporal dependency. Lag (i.e., the shift of one time series with respect to another) significantly affected the magnitude of the correlation coefficients of satisfaction with PA and NA (explaining almost half of the correlation variance). A significant affect-satisfaction cross-correlation can be found when no lag is present. The introduction of a lag reduces the affect-satisfaction cross-correlation to virtually zero. Research suggests that affect and satisfaction overlap at the momentary level, and the results of this study imply that they are also time-dependent. These findings corroborate the idea that momentary satisfaction judgments are partially based on available emotional information, both in terms of intensity and temporal consistency.

Tuning the separability in noncommutative space

With the help of the generalized Peres–Horodecki separability criterion (Simon’s condition) for a bipartite Gaussian state, we have studied the separability of the noncommutative (NC) space coordinate degrees of freedom. Non-symplectic nature of the transformation between the usual commutative space and NC space restricts the straightforward use of Simon’s condition in NCS. We have transformed the NCS system to an equivalent Hamiltonian in commutative space through the Bopp shift, which enables the utilization of the separability criterion. To make our study fairly general and to analyze the effect of parameters on the separability of bipartite state in NC-space, we have considered a bilinear Hamiltonian with time-dependent (TD) parameters, along with a TD external interaction, which is linear in field modes. The system is transformed (Sp(4,R)) into canonical form keeping the intrinsic symplectic structure intact. The solution of the TD-Schrödinger equation is obtained with the help of the Lewis–Riesenfeld invariant method (LRIM). Expectation values of the observables (thus the covariance matrix) are constructed from the states obtained from LRIM. It turns out that the existence of the NC parameters in the oscillator determines the separability of the states. In particular, for isotropic oscillators, the separability condition for the bipartite Gaussian states depends on specific values of NC parameters. Moreover, particular anisotropic parameter values for the oscillator may cease the separability. In other words, both the deformation parameters (θ, η) and parameter values of the oscillator (mass, frequency) are important characteristics for the separability of bipartite Gaussian states. Thus tuning the parameter values, one can destroy or recreate the separability of states. With the help of a toy model, we have demonstrated how the tuning of a TD-NC space parameter affects the separability.

In vivo disentanglement of diffusion frequency-dependence, tensor shape, and relaxation using multidimensional MRI.

Diffusion MRI with free gradient waveforms, combined with simultaneous relaxation encoding, referred to as multidimensional MRI (MD-MRI), offers microstructural specificity in complex biological tissue. This approach delivers intravoxel information about the microstructure, local chemical composition, and importantly, how these properties are coupled within heterogeneous tissue containing multiple microenvironments. Recent theoretical advances incorporated diffusion time dependency and integrated MD-MRI with concepts from oscillating gradients. This framework probes the diffusion frequency, , in addition to the diffusion tensor, , and relaxation, , , correlations. A clinical imaging protocol was then introduced, with limited brain coverage and 3 mm3 voxel size, which hinder brain segmentation and future cohort studies. In this study, we introduce an efficient, sparse in vivo MD-MRI acquisition protocol providing whole brain coverage at 2 mm3 voxel size. We demonstrate its feasibility and robustness using a well-defined phantom and repeated scans of five healthy individuals. Additionally, we test different denoising strategies to address the sparse nature of this protocol, and show that efficient MD-MRI encoding design demands a nuanced denoising approach. The MD-MRI framework provides rich information that allows resolving the diffusion frequency dependence into intravoxel components based on their distribution, enabling the creation of microstructure-specific maps in the human brain. Our results encourage the broader adoption and use of this new imaging approach for characterizing healthy and pathological tissues.

Oscillations of the amplitude of the Cooper pair gap induced by interactions between Cooper pairs in the framework of BCS theory

According to BCS theory, superconductivity arises from the weak attraction between electrons. In particular, Cooper pairs are important in determining the properties of superconductivity; therefore, the theoretical description of Cooper pairs is based on the mean-field approximation used in the BCS theory. However, the mean-field approximation cannot estimate the contribution induced by the interactions between Cooper pairs, i.e., many-body terms , accurately. In this article, therefore, using simultaneous differential equations derived from time-dependent Schrödinger equation, we show the detailed description of the Cooper pairs including such interactions, which has not been mentioned in BCS theory so far. We reveal that the time dependence oscillates when interactions between Cooper pairs are accurately considered. Based on theses calculations, the coupling of the Cooper pair gap can become zero, suggesting that the Cooper pairs can be switched on and off in real superconductors in the framework of BCS theory.

Enhancing the anti-thixotropic properties of waxy maize starch modified by different α-amylases and its underlying molecular mechanism

The large thixotropy of the starch-thickened foods is often unfavorable in many applications. This study examined the contribution of the proportion of amylopectin chain length to time-dependence of starch gels. The α-amylase (AM) from Bacillus stearothermophilus and maltogenic α-amylase (MA) from Bacillus subtilis were used to trim amylopectin in different reaction patterns. HPLC, HPAEC and IBC data suggested AM attacked B-chains (DP 12–36), causing an increment in number of the chains with DP 6–12, whereas MA primarily trimmed the short B-chains (DP 12–18) and partial A-chains (DP 9–12) to generate short chains with DP 6–9. Interestingly, the recovery of AM-gels was faster than MA-gels at the same degree of hydrolysis when subjected to shear according to the linear correlation analysis. When releasing the same mass of sugar, shortening of the long internal chains played an important role in reducing time dependence of starch gel rather than the external side chains. Possible models were proposed to illustrate the differences in the mechanism of rapid-recovery caused by different side-chain distributions. The outcome provided a new perspective to regulate the thixotropy behavior of starch through enzyme strategies in the granular state.

Velocity-compensated intravoxel incoherent motion of the human calf muscle.

To determine whether intravoxel incoherent motion (IVIM) describes the blood perfusion in muscles better, assuming pseudo diffusion (Bihan Model 1) or ballistic motion (Bihan Model 2). IVIM parameters were measured in 18 healthy subjects with three different diffusion gradient time profiles (bipolar with two diffusion times and one with velocity compensation) and 17 b-values (0-600 s/mm2) at rest and after muscle activation. The diffusion coefficient, perfusion fraction, and pseudo-diffusion coefficient were estimated with a segmented fit in the gastrocnemius medialis (GM) and tibialis anterior (TA) muscles. Velocity-compensated gradients resulted in a decreased perfusion fraction (6.9% ± 1.4% vs. 4.4% ± 1.3% in the GM after activation) and pseudo-diffusion coefficient (0.069 ± 0.046 mm2/s vs. 0.014 ± 0.006 in the GM after activation) compared to the bipolar gradients with the longer diffusion encoding time. Increased diffusion coefficients, perfusion fractions, and pseudo-diffusion coefficients were observed in the GM after activation for all gradient profiles. However, the increase was significantly smaller for the velocity-compensated gradients. A diffusion time dependence was found for the pseudo-diffusion coefficient in the activated muscle. Velocity-compensated diffusion gradients significantly suppress the IVIM effect in the calf muscle, indicating that the ballistic limit is mostly reached, which is supported by the time dependence of the pseudo-diffusion coefficient.

Search for Chirality in Hydrogenated Magnesium Nanosilicates: A DFT and TD-DFT Investigation.

The formation of silicate grains in the interstellar medium (ISM), especially those containing chiral surfaces such as clinopyroxenes, is poorly understood. Moreover, silicate interactions with various forms of hydrogen-proton (H+), neutral H (HI), and molecular hydrogen (H2) are of high importance as hydrogen comprises >90% of the ISM gas budget, and these species play important roles in the formation of new molecules in space. Furthermore, silicate surfaces catalyze the formation of H2 in the interstellar medium formed on dust grain surfaces by H-H association. The technical difficulty of in situ laboratory investigations of nanosilicate nucleation using astrophysically relevant environmental conditions makes computational chemistry a useful tool for studying potential nanosilicate structures. Furthermore, chiral surfaces interacting with chiral organic molecules could serve as templates that lead to the enantiomeric excess of l-amino acids and d-polyols detected in carbonaceous meteorites. However, in order for this effect to take place, an excess of one chiral form of a mineral is required to break the symmetry. This symmetry-breaking event could have been due to the asymmetric absorption of circularly polarized light by the nanosilicate as it traverses star-forming regions. We investigate this possibility using a metastable chiral form of an enstatite dimer as an input for density functional theory (DFT) and time-dependent (TD)-DFT calculations to obtain various properties and circular dichroism spectra. All in all, twenty-six magnesium nanosilicate structures were studied using varying degrees of hydrogenation: none, with HI, with H+, and with H2. The HSE06/aug-cc-pVQZ level of theory was used for the DFT calculations. TD-DFT calculations utilized the CAM-B3LYP/cc-pVQZ and ωB97X-D3/cc-pVQZ functional and basic set pairings. Results show that (1) all twenty-six structures have absorption bands that fall within the 0.6-28.3 μm range available with the newly launched James Webb Space Telescope and (2) there is a small enantioselective effect by UV-CPL on the eight chiral enstatite dimers (predicted g-values of up to 0.007). While the observed effect is small, it opens up the possibility that it is the inorganic material that becomes enantiomerically biased by UV-CPL, driving chiral enhancements in meteoric organic molecules.