Analytical Force Research Articles

Anthocyanins stabilization of Hibiscus sabdariffa extract with sepiolite: Analytical and reactive force fields approaches

Anthocyanins are natural pigments responsible for color in fruits and flowers. They are unstable to diverse ambient conditions like pH, light, or the presence of ions. Thus, in industry, several techniques are employed to protect and use anthocyanins. Likewise, ancient Mayas used clay and indigo extract to prepare a pigment known as Maya Blue, resistant to time and chemicals. For this reason, this work aims to characterize the hibiscus extract and sepiolite bio-hybrids created according to Maya methodology and observe the stability of anthocyanins, which are color-responsible in flowers and fruits. Spectral analysis shows that anthocyanins and color were more stable than controls. Powder bio-hybrids show less color change under UV radiation and allochroic behavior in acid or alkaline atmospheres. At the same time, spectroscopy analyses and X-ray diffraction show no chemical interaction between sepiolite and hibiscus compounds. Our molecular dynamics with Reactive Force Fields (ReaxFF) studies reveal a similar behavior. Thus, our method protected the color of anthocyanins, in the temperature range from 15 to 35 °C. The interaction of hibiscus compounds with clay is due to van der Waals forces.

The Enhancement of Machine Learning-Based Engine Models Through the Integration of Analytical Functions

The integration of analytical functions into machine learning-based engine models represents a significant advancement in predictive performance and operational efficiency. This paper focuses on the development of hybrid approaches to model engine combustion and temperature indices and on the synergistic effects of combining traditional analytical methods with modern machine learning techniques (such as artificial neural networks) to enhance the accuracy and robustness of such models. The main innovative contribution of this paper is the integration of analytical functions to improve the extrapolation capabilities of the data-driven models. In this work, it is demonstrated that the integrated models achieve superior predictive accuracy and generalization performance across dynamic engine operating conditions, with respect to purely neural network-based models. Furthermore, the analytical corrective functions force the output of the complete model to follow a physical trend and to assume consistent values also outside the domain of values assumed by the input features during the training procedure of the neural networks. This study highlights the potential of this integrative approach based on the implementation of the effects superposition principle. Such an approach also allows us to solve one of the intrinsic issues of data-driven modeling, without increasing the complexity of the training data’s collection and pre-processing.

On the boundedness of solutions of a forced discontinuous oscillator

We study the global boundedness of the solutions of a non-smooth forced oscillator with a periodic and real analytic forcing. We show that the impact map associated with this discontinuous equation becomes a real analytic and exact symplectic map when written in suitable canonical coordinates. By an accurate study of the behaviour of the map for large amplitudes and by employing a parametrization KAM theorem, we show that the periodic solutions of the unperturbed oscillator persist as two-dimensional tori under conditions that depend on the Diophantine conditions of the frequency, but are independent on both the amplitude of the orbit and of the specific value of the frequency. This allows the construction of a sequence of nested invariant tori of increasing amplitude that confine the solutions within them, ensuring their boundedness. The same construction may be useful to address such persistence problem for a larger class of non-smooth forced oscillators.

Fundamental Invariant Neural Network (FI-NN) Potential Energy Surface for the OH + CH3OH Reaction with Analytical Forces.

The hydrogen abstraction reaction of OH + CH3OH plays a great role in combustion and atmospheric and interstellar chemistry and has been extensively studied theoretically and experimentally. Theoretically, the numerical gradients with respect to the Cartesian coordinates of atoms in molecular simulations on our recent potential energy surface (PES) for the title reaction trained using the permutationally invariant polynomial neural network (PIP-NN) approach hinder the extensive calculation because of the unaffordable computation cost. To address this issue, we in this work report a new full-dimensional accurate analytical PES for the title reaction using the fundamental invariant neural network (FI-NN) approach based on 140,192 points of the quality UCCSD(T)-F12a/AVTZ. Besides, the spin-orbit (SO) corrections of OH in the entrance channel were determined at the level of complete active space self-consistent field with the AVTZ basis set. As a compromise between computational cost and efficiency, the Δ-machine learning approach was employed to construct the SO-corrected PES. Based on this new FI-NN PES with analytical forces, thermal rate coefficients and various dynamic properties, including the integral cross sections, the differential cross sections, and the product energy partitioning, were determined by running a total of 5.5 million trajectories. The use of analytical gradients of the FI-NN PES accelerated simulations and about 99% of computation cost was saved, compared to that for the PIP-NN PES with numerical gradients. Such a significant acceleration is achieved mainly by replacing PIPs with FIs.

Development of enhanced force models to analyze the nonlinear hysteresis response of miniaturized pneumatic artificial muscles

Miniaturized pneumatic artificial muscles (MPAMs), designed to replicate natural muscle actuation, offer unique attributes such as a high power-to-weight ratio, flexibility, easy integration, and compactness, making them favourable for many applications. The present paper aims at the development of an accurate semi-analytical force model considering the effect of the bladder material and friction terms to predict the nonlinear force-deformation response of MPAMs during contraction and expansion cycles. Existing force models for MPAMs exhibit limitations to accurately capturing the force-deformation behaviours due to several simplification factors. This study enhances these models by integrating correction terms to accurately address the nonlinearity and frictional effects exhibited by MPAMs. An analysis of the hysteresis loops resulting from the cyclic loading and unloading of MPAMs under specific pressures is undertaken to compare different methodologies in order to determine the most accurate correction terms. To investigate the nonlinear behaviour of MPAMs, the stress-strain relationship of the bladder material and results from force-deformation experimental tests on the entire actuator are considered and for the effect of friction term, theoretical and empirical approaches are investigated. Results suggest that the theoretical force model based on analytical and empirical friction forces, respectively, slightly overestimates and underestimates the force experienced by MPAMs during contraction while slightly underestimate and overestimates during expansion, respectively. A comparative analysis between MPAMs featuring Ecoflex-50 silicone and Ecoflex30 + PDMS mixture as bladder materials has also been conducted to further investigate the effect of bladder materials on their force and contraction outputs under inlet pressures ranging from 0 kPa to 300 kPa. It is shown that the MPAM feauting Ecoflex-50 bladder, exhibits lower dead-band pressure and an overall reduced blocked force in comparison to MPAM with bladder made of Ecoflex30 + PDMS while exhibiting a substantially enhanced free contraction capacity.

Force model of ultrasonic empowered minimum quantity lubrication grinding CFRP

As a weight-reducing material in aerospace applications, carbon fiber reinforced polymer (CFRP) requires precision grinding to ensure the accuracy and assembly of mating positioning surfaces. However, achieving low-damage machining of CFRPs remains challenging. Flood cooling reduces the mechanical properties, while dry grinding deteriorates the surface integrity, making minimum quantity lubrication (MQL) a viable alternative. However, nanolubricants struggle to penetrate the grinding area due to gas barrier obstruction. Consequently, a new CFRP grinding approach using multiangle 2D ultrasonic vibration-empowered nanolubricant MQL was proposed. An analytical force model is crucial for optimizing machining strategies and adjusting parameters. The aim is to reveal grinding mechanics and develop a force model under different ultrasonic and lubrication-cooling conditions. First, a uniform random fiber distribution model and a grinding wheel model were established, and the mechanical properties of the interface were predicted. Then, the intermittent grinding behavior and dynamic stiffnesses of the multiangle 2D ultrasonic vibration-assisted grinding (UVAG) system were investigated based on the geometric kinematics of the grains' trajectory. Furthermore, based on the deformation deflection curve, bending fracture force models for 45°, 90°, and 135° fibers were established with different contact and boundary conditions. The material removal mechanisms of shear and tension-compression buckling in 0° CFRP grinding were revealed, and an elliptical contact normal force model was established. The interlayer shear in 45° CFRPs was analyzed. The longitudinal compressive matrix cracking and fiber shear fracture in the 90° CFRPs were elucidated. Fiber microbuckling and fiber bundle removal of 135° CFRPs were investigated. Finally, grinding force models with different fiber orientation angles (FOAs) were interconnected and experimentally assessed. Experimental assessments demonstrated that the grinding force model has acceptable estimation error and effectively captures the mechanics of CFRPs with different FOAs.

A stability indicating method development and validation of a rapid and sensitive RP-HPLC method for Nintedanib and its application in quantification of nanostructured lipid carriers.

Nintedanib (NTB) is a multiple tyrosine kinase inhibitor, been investigated for many disease conditions like idiopathic pulmonary fibrosis (IPF), systemic sclerosis interstitial lung disease (SSc-ILD) and non-small cell lung cancer (NSCLC). NTB is available as oral capsule formulation, but its ability to detect degradants formed through oxidative, photolytic and hydrolytic processes makes it difficult to quantify. In the current work, a novel reversed-phase high-performance liquid chromatography (RP-HPLC) method was developed and validated. The developed method is simple, precise, reproducible, stable and accurate. The inherent stability of NTB was evaluated using the proposed analytical method approach and force degradation studies were carried out. NTB was separated chromatographically on the Shimadzu C 18 column as stationary phase (250 ×4.6 mm, 5 µm) using an isocratic elution method with 0.1% v/v triethyl amine (TEA) in HPLC grade water and acetonitrile (ACN) in the ratio 35:65% v/v. The mobile phase was pumped at a constant flow rate of 1.0 ml/min, and the eluent was detected at 390 nm wavelength. NTB was eluted at 6.77±0.00 min of retention time (t R) with a correlation coefficient of 0.999, the developed method was linear in the concentration range of 0.5 µg/ml to 4.5 µg/ml. The recovery rate was found to be in the range of 99.391±0.468% for 1.5 µg/ml concentration. Six replicate standards were determined to have an % RSD of 0.04. The formulation excipients didn't interfere with the determination of NTB, demonstrating the specificity of the developed method. The proposed approach of the analytical method developed can be used to quantify the amount of NTB present in bulk drugs and pharmaceutical formulations.

The catastrophic failure of the Jagersfontein tailings dam: An industrial disaster 150 years in the making

This study provides a sociohistorical account of decisions made over a 150-year period about the Jagersfontein mine in South Africa's Free State and the catastrophic failure of its tailings dam that occurred in the early morning of September 11, 2022. The failure resulted in the sudden and uncontrolled release of more than 6 million m3 of liquid sludge, smothering nearly 200 nearby houses and approximately 1600 ha of agricultural and grazing land. Two people died, and one remains missing. Our account of this disaster shows the analytic force of examining the history of the mine's development, the issues decision-makers faced at various points in time, the choices made and the cumulative influence of these conditions on human vulnerability. We present a disaster timeline over four historical periods, linking sources and literature to each significant point in the timeline. It is one of the only available tailings dam failure timelines linked to the social context through historical analysis.

Measuring the viscoelastic relaxation function of cells with a time-dependent interpretation of the Hertz-Sneddon indentation model

The Hertz-Sneddon elastic indentation model is widely adopted in the biomechanical investigation of living cells and other soft materials using atomic force microscopy despite the explicit viscoelastic nature of these materials. In this work, we demonstrate that an exact analytical viscoelastic force model for power-law materials, can be interpreted as a time-dependent Hertz-Sneddon-like model. Characterizing fibroblasts (L929) and osteoblasts (OFCOLII) demonstrates the model's accuracy. Our results show that the difference between Young's modulus EY obtained by fitting force curves with the Hertz-Sneddon model and the effective Young's modulus derived from the viscoelastic force model is less than 3%, even when cells are probed at large forces where nonlinear deformation effects become significant. We also propose a measurement protocol that involves probing samples at different indentation speeds and forces, enabling the construction of the average viscoelastic relaxation function of samples by conveniently fitting the force curves with the Hertz-Sneddon model.

FORM: Anthropology as design

AbstractThis essay argues for design as an expression of analytic rigour and ethical commitment. It explores what it might mean to ‘write’ in collaboration with the entities and forces from Country, ancestors, oceans, soil, honeyeaters, sound, wild bores, frogs, fire, ash, sand, trees, and echoes, to colour, code, bitrates, cameras, computers, and archives. Every design decision in ‘Epistemic attunements’ was conceptually‐motivated and informed by an anthropologically‐attuned sensibilty. It was never simply a case of making things look ‘beautiful’. The analytic force of the design propelled our intermedial arguments forward, but also sideways, down through layers, into shifts of sensation and abrupt moments of pause for specific critical effect.

Including Neutrino-driven Convection in the Force Explosion Condition to Predict Explodability of Multidimensional Core-collapse Supernovae (FEC+)

Most massive stars end their lives with core collapse. However, it is not clear which explode as a core-collapse supernova (CCSN), leaving behind a neutron star, and which collapse to a black hole, aborting the explosion. One path to predict explodability without expensive multidimensional simulations is to develop analytic explosion conditions. These analytic conditions also provide a deeper understanding of the explosion mechanism and they provide some insight into why some simulations explode and some do not. The analytic force explosion condition (FEC) reproduces the explosion conditions of spherically symmetric CCSN simulations. In this follow-up manuscript, we include the dominant multidimensional effect that aids explosion—neutrino-driven convection—in the FEC. This generalized critical condition (FEC+) is suitable for multidimensional simulations and has potential to accurately predict explosion conditions of two- and three-dimensional CCSN simulations. We show that adding neutrino-driven convection reduces the critical condition by ∼30%, which is consistent with previous multidimensional simulations.

Analytic and Data-Driven Force Prediction for Vacuum-Based Granular Grippers

As manufacturing and assembly processes continue to require more adaptable systems for automated handling, innovative solutions for universal gripping are emerging. These grasping systems can enable the handling of wide varieties of shapes, with gripping forces varying with grasped geometries. For the efficient usage of handling systems, precise offline and online prediction models for resulting grasping forces for different objects are necessary. In previous research, a flexible vacuum-based granular gripper was developed, for which no option for predicting gripping forces is currently available. Various gripping force prediction methodologies within the current state of the art are examined and evaluated. For an assessment of grasping forces of previously untested objects for the examined gripper with limited data and low computational effort, two methodologies are proposed. An analytical, 2D-geometry-derived gripper-specific metric for geometries is compared to a methodology based on similarities of objects to a small existing dataset. The applicability and prediction quality for different object types is analyzed through validation experiments. Gripping force estimations are possible with both methodologies, with individual weaknesses towards geometric features such as air permeabilities. With further development, robust predictions of gripping forces could be achieved for a wide range of unknown object geometries with limited experimental effort.

Force modeling of vertical surface grinding considering wheel-workpiece contact geometry

Vertical surface grinding (VSG) is an effective method that can adapt to multiple machining tasks, including stock removal machining to get high productivity and finish machining to obtain high-quality surfaces. Force prediction can reveal some mechanisms in the VSG process, conducing to the improved machining quality and efficiency. This paper aims to develop a novel analytical force model considering the actual wheel-workpiece contact geometry (WWCG). In the modeling process, the cup grinding wheel was divided into many micro-cutting layers along the wheel axis, and the expressions of geometrical-kinematic parameters were derived to characterize the material removal at different positions of the primary grinding zone (PGZ). A single-grain grinding force model was proposed based on chip formation and plastic pile-up mechanisms from an energy perspective. Based on the analysis of multiple-grain interactions at each micro-cutting layer, equations of the local and total grinding forces in the VSG process were then derived by synthesizing the single-grain forces. Findings show that the developed model could accurately predict the magnitudes of total grinding forces and provide the distributions of local grinding forces. The WWCG variation could significantly affect the grinding forces by altering the material removal rate of different micro-cutting layers. This work provides a new method for predicting grinding forces and theoretical guidance for optimizing machining parameters and tailoring the edge profiles of cup grinding wheels to adapt to specific machining tasks.

Fast and accurate nonadiabatic molecular dynamics enabled through variational interpolation of correlated electron wavefunctions.

We build on the concept of eigenvector continuation to develop an efficient multi-state method for the rigorous and smooth interpolation of a small training set of many-body wavefunctions through chemical space at mean-field cost. The inferred states are represented as variationally optimal linear combinations of the training states transferred between the many-body bases of different nuclear geometries. We show that analytic multi-state forces and nonadiabatic couplings from the model enable application to nonadiabatic molecular dynamics, developing an active learning scheme to ensure a compact and systematically improvable training set. This culminates in application to the nonadiabatic molecular dynamics of a photoexcited 28-atom hydrogen chain, with surprising complexity in the resulting nuclear motion. With just 22 DMRG calculations of training states from the low-energy correlated electronic structure at different geometries, we infer the multi-state energies, forces and nonadiabatic coupling vectors at 12 000 geometries with provable convergence to high accuracy along an ensemble of molecular trajectories, which would not be feasible with a brute force approach. This opens up a route to bridge the timescales between accurate single-point correlated electronic structure methods and timescales of relevance for photo-induced molecular dynamics.

Boron coordination and three‐membered ring formation in sodium borate glasses: a machine‐learning molecular dynamics study

AbstractClassical molecular dynamics (CMD) simulations that employ analytical force fields have been commonly utilized to investigate mechanical, chemical, and thermal properties of oxide glasses owing to their superior computational efficiency. Conversely, simple functional forms limit the accuracy in modeling complicated glass structures, specifically, in alkaline borate glasses, which exhibit boron coordination numbers that vary nonlinearly with changes in glass composition and temperature. Machine‐learning potentials (MLPs), which are trained using datasets on energy and force evaluated via the density functional theory (DFT), are garnering significant attention as a novel simulation technology for enhancing the accuracy in modeling materials. Therefore, this study applied a universal MLP, PreFerred Potential (PFP) (trade‐name: Matlantis), to model sodium borate glasses, and its accuracy was verified in reproducing boron coordination and ring structures by comparing its results to available experimental data. We found that PFP can quantitatively reproduce the boron coordination change with glass composition without any empirical correction, while the boron coordination in the melts at high temperatures is overestimated, even though the qualitative variation was better estimated than CMD simulations. Furthermore, the MLP could generate many 3‐rings, unlike the analytical force‐field. Accordingly, we demonstrated superior accuracy of the MLP in modeling alkaline borate glasses, while discussing the challenges faced in reproducing the elaborated microstructures in borate glasses.

Influence of the abrasive shape-position characteristic on the grinding thermo-mechanical coupling

Grinding is a generally utilized method, removing excess materials through effective abrasives. The grinding abrasives with multiple shape-position characteristics play a dominant role in determining the thermo-mechanical coupling, which may influence the surface quality directly. To investigate this correlated influence mechanism, this paper focuses on the abrasive shape and position characteristic on the grinding thermo-mechanical process with the analytic single abrasive interaction force model by considering the abrasive shape and its distribution information. It can be found that the mapped dynamic grinding temperature is actually discretized on the workpiece surface, which is on account of the diversity of the abrasive shape and its distribution. Moreover, higher spherical and conical abrasive particles, as well as lower pyramid shaped abrasive particle ratios, can generate greater specific grinding energy with a discretized temperature distribution, when compared with a higher proportion of pyramid shaped abrasive. The study can be utilized to provide valuable theoretical foundation for engineering practice by preparing structural wheel and its grinding property.

Excited State Properties of Point Defects in Semiconductors and Insulators Investigated with Time-Dependent Density Functional Theory.

We present a formulation of spin-conserving and spin-flip hybrid time-dependent density functional theory (TDDFT), including the calculation of analytical forces, which allows for efficient calculations of excited state properties of solid-state systems with hundreds to thousands of atoms. We discuss an implementation on both GPU- and CPU-based architectures along with several acceleration techniques. We then apply our formulation to the study of several point defects in semiconductors and insulators, specifically the negatively charged nitrogen-vacancy and neutral silicon-vacancy centers in diamond, the neutral divacancy center in 4H silicon carbide, and the neutral oxygen-vacancy center in magnesium oxide. Our results highlight the importance of taking into account structural relaxations in excited states in order to interpret and predict optical absorption and emission mechanisms in spin defects.

Understanding Ionic Diffusion Mechanisms in Li2S Coatings for Solid-State Batteries: Development of a Tailored Reactive Force Field for Multiscale Simulations.

In order to investigate Li2S as a potential protective coating for lithium anode batteries using superionic electrolytes, we need to describe reactions and transport for systems at scales of >10,000 atoms for time scales beyond nanoseconds, which is most impractical for quantum mechanics (QM) calculations. To overcome this issue, here, we first report the development of the reactive analytical force field (ReaxFF) based on density functional theory (DFT) calculations on model systems at the PBE0/TZVP and M062X/TZVP levels. Then, we carry out reactive molecular dynamics simulations (RMD) for up to 20 ns to investigate the diffusion mechanisms in bulk Li2S as a function of vacancy density, determining the activation barrier for diffusion and conductivity. We show that RMD predictions for diffusion and conductivity are comparable to experiments, while results on model systems are consistent with and validated by short (10-100 ps) ab initio molecular dynamics (AIMD). This new ReaxFF for Li2S systems enables practical RMD on spatial scales of 10-100 nm (10,000 to 10 million atoms) for the time scales of 20 ns required to investigate predictively the interfaces between electrodes and electrolytes, electrodes and coatings, and coatings and electrolytes during the charging and discharging processes.

Fantastical excited state optimized structures and where to find them.

The quantum chemistry community has developed analytic forces for approximate electronic excited states to enable walking on excited state potential energy surfaces (PES). One can thereby computationally characterize excited state minima and saddle points. Always implicit in using this machinery is the fact that an excited state PES only exists within the realm of the Born-Oppenheimer approximation, where the nuclear and electronic degrees of freedom separate. This work demonstrates through abinitio calculations and simple nonadiabatic dynamics that some excited state minimum structures are fantastical: they appear to exist as stable configurations only as a consequence of the PES construct, rather than being physically observable. Each fantastical structure exhibits an unphysically high predicted harmonic frequency and associated force constant. This fact can serve as a valuable diagnostic of when an optimized excited state structure is non-observable. The origin of this phenomenon can be attributed to the coupling between different electronic states. As PESs approach one another, the upper surface can form a minimum that is very close to a near-touching point. The force constant, evaluated at this minimum, relates to the strength of the electronic coupling rather than to any characteristic excited state vibration. Nonadiabatic dynamics results using a Landau-Zener model illustrate that fantastical excited state structures have extremely short lifetimes on the order of a few femtoseconds. Their appearance in a calculation signals the presence of a nearby conical intersection through which the system will rapidly cross to a lower surface.

A stability indicating method development and validation of a rapid and sensitive RP-HPLC method for Nintedanib and its application in quantification of nanostructured lipid carriers

Background: Nintedanib (NTB) is a multiple tyrosine kinase inhibitor, been investigated for many disease conditions like idiopathic pulmonary fibrosis (IPF), systemic sclerosis interstitial lung disease (SSc-ILD) and non-small cell lung cancer (NSCLC). NTB is available as oral capsule formulation, but its ability to detect degradants formed through oxidative, photolytic and hydrolytic processes makes it difficult to quantify. In the current work, a novel reversed-phase high-performance liquid chromatography (RP-HPLC) method was developed and validated. Methods: The developed method is simple, precise, reproducible, stable and accurate. The inherent stability of NTB was evaluated using the proposed analytical method approach and force degradation studies were carried out. NTB was separated chromatographically on the Shimadzu C18 column as stationary phase (250 ×4.6 mm, 5 µm) using an isocratic elution method with 0.1% v/v triethyl amine (TEA) in HPLC grade water and acetonitrile (ACN) in the ratio 35:65% v/v. The mobile phase was pumped at a constant flow rate of 1.0 ml/min, and the eluent was detected at 390 nm wavelength. Results: NTB was eluted at 6.77±0.00 min of retention time (tR) with a correlation coefficient of 0.999, the developed method was linear in the concentration range of 0.5 µg/ml to 4.5 µg/ml. The recovery rate was found to be in the range of 99.391±0.468% for 1.5 µg/ml concentration. Six replicate standards were determined to have an % RSD of 0.04. Conclusion: The formulation excipients didn't interfere with the determination of NTB, demonstrating the specificity of the developed method. The proposed approach of the analytical method developed can be used to quantify the amount of NTB present in bulk drugs and pharmaceutical formulations.