Complex Chemical Mechanisms Research Articles

Osteoarthritis year in review 2024: Biomechanics

ObjectiveWe aimed to systematically review and summarize the literature of the past year on OA and biomechanics, to highlight gaps and challenges, and to present some promising approaches and developments. Methods.A systematic literature search was conducted using Pubmed and the Web of Science Core Collection. We included original articles and systematic reviews on OA and biomechanics in human subjects published between April 2023 and April 2024. Results.Of the 155 studies that met the inclusion criteria, 9 were systematic reviews and 146 were original (mostly cross-sectional) studies that included a total of 6488 patients and 1921 controls with a mean age of 57.5 and 44.7 years, respectively. Promising advances have been made in medical imaging of affected soft tissue structures, the relationship between soft tissue properties and biomechanical changes in OA, new technologies to facilitate easier assessment of ambulatory biomechanics, and personalized physics-based models that also include complex chemical and mechanobiological mechanisms, all of which are relevant to gaining mechanistic insights into the pathophysiology of OA. ConclusionsThere is still an unmet need for larger longitudinal data sets that combine clinical, radiological, and biomechanical outcomes to characterize the biomechanical fingerprint that underlies the trajectory of functional decline and biomechanical phenotypes of OA. In addition, criteria and guidelines for control groups, as well as methods and standards for model verification allowing for comparisons between studies are needed.

Numerical and computational analysis on a dissipative dynamical system: Slow invariant manifold for complex chemical mechanism

This article presents the notion about Slow Invariant Manifold (SIM) and their fundamental role in model reduction techniques (MRTs) for challenges encountered in mechanical engineering within dissipative systems of chemical kinetics. Focusing on the reaction routes of complex mechanisms, we construct and compare primary approximations of the SIM through MRTs, including the Spectral Quasi Equilibrium Manifold (SQEM) and Intrinsic Low Dimensional Manifold (ILDM). These methods effectively transform high-dimensional complex problems into lower dimensions, solving them without compromising crucial information about the complex systems modified for homogeneous reactive systems. Employing the sensitivity analysis by using the MATLAB's toolbox, we present the numerical findings in a tabular format obtained through MRTs. This study provides the understanding about the accessible exploration of numerical solutions, improving insights of the complex variation within the system.

Decoding molecular mechanisms: brain aging and Alzheimer's disease.

The complex morphological, anatomical, physiological, and chemical mechanisms within the aging brain have been the hot topic of research for centuries. The aging process alters the brain structure that affects functions and cognitions, but the worsening of such processes contributes to the pathogenesis of neurodegenerative disorders, such as Alzheimer's disease. Beyond these observable, mild morphological shifts, significant functional modifications in neurotransmission and neuronal activity critically influence the aging brain. Understanding these changes is important for maintaining cognitive health, especially given the increasing prevalence of age-related conditions that affect cognition. This review aims to explore the age-induced changes in brain plasticity and molecular processes, differentiating normal aging from the pathogenesis of Alzheimer's disease, thereby providing insights into predicting the risk of dementia, particularly Alzheimer's disease.

Unlocking Cellular Control: The Promise of PROTACs in Disease Intervention

The discovery of proteolysis-targeting chimeras (PROTACs)[6] is among the most exciting and promising avenues in cancer therapy. [14] These fascinating compounds signify a paradigm shift from traditional approaches to medication development, offering a new idea that leverages the complexities of biological mechanisms to accomplish highly focused degradation of particular proteins implicated in pathological processes.[16] This novel strategy has the potential to address a number of drawbacks with conventional therapy techniques, such as the development of drug resistance and unexpected adverse effects resulting from interactions that are not intended. [14] The fundamental attraction of PROTACs is their distinct mode of action, which is based on controlling the cell's own machinery for protein degradation.[11] This orchestrated degradation translates to a substantial reduction in the levels of disease-driving proteins, often leading to the disruption of critical pathways involved in cancer growth and progression.[9] The in-depth principles underlying PROTAC technology are thoroughly explored in this review study, which also provides insight into the complex chemical mechanisms that enable these chimeric molecules to specifically degrade certain proteins while leaving others intact. Showcasing the potential of PROTACs as a revolutionary force in targeted cancer therapy, and focusing on its application in prostate and breast cancer especially, the article draws from a comprehensive compilation of preclinical and clinical studies, advancements, and breakthroughs in the field. [10] The methods used to create and refine PROTACs for various cancer types will be examined throughout the review, along with the subtleties of the ligand and linker choices that are crucial to their effectiveness and selectivity.[6] The difficulties and possibilities of transferring this ground-breaking technology from the lab to clinical practice will also be thoroughly examined, with an emphasis on issues like bioavailability, administration strategies, and potential resistance mechanisms.[9] Through the integration of perspectives from various studies, the objective is to present a thorough but succinct review of the state of ongoing PROTAC research, emphasizing both, noteworthy advancements and the important issues that still need to be resolved. In the end, our investigation into PROTACs aims to shed light on how they can change the face of cancer therapy by providing a preview of a day when targeted protein degradation of disease-causing proteins would lead the way in novel therapeutic approaches.[9]

Model simplification and sensitivity analysis of chemical species for oxidation behavior in multi route complex chemical mechanism

This study employs a Model Reduction Technique (MRT) to simplify the four-step catalytic carbon monoxide (CO) oxidation reaction. The C-matrix method identifies key elements, key/non key components, and key reactions, while the Intrinsic Low-Dimensional Manifold (ILDM) pinpoints a Slow-Invariant Manifold (SIM) important for understanding key species behavior. Sensitivity analysis can be considered for measuring the efficiency of the chemical species in detailed mechanism. This systematic approach contributes to optimizing and controlling complex reactions offering broad application potential. In addition to the mathematical proof, the validation of the given chemical model is rectified. The comparison between the slow invariant manifold of both reaction routes is reported and the computational based results performed in this study are obtained through MATLAB.

Competing Reaction Mechanisms of Peptide Bond Formation in Water Revealed by Deep Potential Molecular Dynamics and Path Sampling.

The formation of an amide bond is an essential step in the synthesis of materials and drugs, and in the assembly of amino acids to form peptides. The mechanism of this reaction has been studied extensively, in particular to understand how it can be catalyzed, but a representation capable of explaining all the experimental data is still lacking. Numerical simulation should provide the necessary molecular description, but the solvent involvement poses a number of challenges. Here, we combine the efficiency and accuracy of neural network potential-based reactive molecular dynamics with the extensive and unbiased exploration of reaction pathways provided by transition path sampling. Using microsecond-scale simulations at the density functional theory level, we show that this method reveals the presence of two competing distinct mechanisms for peptide bond formation between alanine esters in aqueous solution. We describe how both reaction pathways, via a general base catalysis mechanism and via direct cleavage of the tetrahedral intermediate respectively, change with pH. This result contrasts with the conventional mechanism involving a single pathway in which only the barrier heights are affected by pH. We show that this new proposal involving two competing mechanisms is consistent with the experimental data, and we discuss the implications for peptide bond formation under prebiotic conditions and in the ribosome. Our work shows that integrating deep potential molecular dynamics with path sampling provides a powerful approach for exploring complex chemical mechanisms.

Real-time tracking of curing process of an epoxy adhesive by X-ray photon correlation spectroscopy

Introduction: Cross-linkable polymers are in widespread use in a variety of industries because of their thermomechanical toughness, chemical resistance, and adhesive strength. But traditional methods to characterize these materials are insufficient for fully capturing the complex chemical and physical mechanisms of the crosslinking reaction. In this study, in situ X-ray photon correlation spectroscopy (XPCS) was used to investigate the crosslinking kinetics of a two-component epoxy resin adhesive.Materials and methods: With XPCS, we tracked the temporally resolved dynamics of silica filler particles, which served as probes of the internal dynamics of the thermoset network and allowed us to study the crosslinking process. The epoxy was cured isothermally at 40 °C and 80 °C to study the effects of curing temperature on the epoxy’s crosslinking reaction. XPCS results were compared to dielectric analysis (DEA) results, to demonstrate the similarities between a traditional technique and XPCS, and highlight the additional information gained with XPCS.Results and discussion: The epoxy resin was found to be highly sensitive to temperature. The epoxy samples exhibited different relaxation processes depending on isothermal cure temperature, indicating a complex relationship between applied temperature and the development of stress/relaxation conditions associated with formation of the thermoset network. Heating to the isothermal temperature setpoint at the start of curing promoted gelation, but the vitrification process was not completed during the isothermal curing stage. Instead, cooling the sample to room temperature facilitated the final vitrification process. This paper contextualizes this epoxy’s results within the broader field of thermoset study via XPCS, and advocates for XPCS as a fundamental technique for the study of complex polymers.

Reaction path identification and validation from molecular dynamics simulations of hydrocarbon pyrolysis

AbstractCreation of complex chemical mechanisms for hydrocarbon pyrolysis and combustion is challenging due to the large number of species and reactions involved. Reactive molecular dynamics (RMD) enables the simulation of thousands of reactions and the discovery of previously unknown components of the reaction network. However, due to the inherent imprecision of reactive force fields, it is necessary to verify RMD‐obtained reaction paths using more accurate methods such as Density Functional Theory (DFT). We demonstrate a method for identification and confirmation of reaction pathways from RMD that supplement an established mechanism, using the example of benzene formation from n‐heptane and iso‐octane pyrolysis. We establish a validation workflow to extract reaction geometries from RMD and optimize transition states using the Nudged‐Elastic‐Band method on semi‐empirical and quantum mechanical levels of theory. Our findings demonstrate that the widely recognized ReaxFF parameterization, CHO2016, can identify known pathways from a established soot formation mechanism while also indicating new ones. We also show that CHO2016 underestimates hydrogen migration barriers by up to as compared to DFT and can lower activation barriers significantly for spin‐forbidden reactions. This highlights the necessity for validation or potentially even reparametrization of CHO2016.

Computing the solution trajectories, steady state approximation, and sensitivity analysis in complex chemical reaction mechanism

Ordinary differential equations are often employed in chemical kinetics mathematical modeling. The theoretical results for a model of a multi-step chemical kinetic process are presented in this article. This model seeks to describe both the complicated kinetics of complex chemical processes and the steady state behavior of chemical species. Reduction techniques are used to divide fast and slow processes according to their time scales, which simplifies the model. As a result, the quick processes are removed, making the slow processes the main emphasis of a reduced-dimensional model. The paper concentrates on a two-step reversible reaction mechanism for model reduction, which reduces the complexity of the entire reaction process. The phase flow of solution trajectories close to equilibrium points is also given special consideration in the analysis as it offers a clear and pertinent depiction of the behavior of the system. The physical properties of the observed data are further shown via MATLAB simulations. Sensitivity analysis computes parameters, revealing their impact on species behavior, visually presenting the parameter impact.

MEXPLORER 1.0.0 – a mechanism explorer for analysis and visualization of chemical reaction pathways based on graph theory

Abstract. The open-source software MEXPLORER 1.0.0 is presented here. The program can be used to analyze, reduce, and visualize complex chemical reaction mechanisms. The mathematics behind the tool is based on graph theory: chemical species are represented as vertices, and each reaction is described as a set of edges. MEXPLORER is a community tool published under the GNU General Public License.

Brief Review of Recent Achievements in the Flamelet Manifold Selection and Probability Density Distribution for Flamelet Manifold Variables

Abstract The flamelet model is a commonly used tool for turbulent combustion simulations in the engineering field due to its computational efficiency and compatibility with complex chemical reaction mechanisms. Despite being widely used for decades, the flamelet model still faces challenges when applied to complex flame configurations, such as partially premixed flames, inhomogeneous inlets, supersonic combustion, or multiphase combustion. The principal challenges are posed by the uncertainty of the presumed shapes for probability density functions (PDFs) of the flamelet tabulation variables and the coupled process of turbulent diffusion and chemical reaction in turbulent combustion. Recent progress is reviewed from the viewpoint of the reaction manifold, with connections made to other combustion models, as well as the determination of joint (or conditional) PDFs for flamelet manifold parameters (e.g., progress variable, scalar dissipation rates, etc.). Promising improvements have been outlined in computational efficiency and the accuracy of predicted variable fields in simulating complex combustion systems (such as turbulent inhomogeneous combustion, combustion with multi-regime modes, and two-phase combustion). Advances in computational resources, direct numerical simulation data, artificial intelligence, stochastic simulation methods, and other dimension-reduction combustion models will contribute to the development of more accurate and efficient flamelet-like models for engineering applications.

Modelling of thermal plasma-assisted carbon tetrafluoride abatement

This study focuses on the numerical modelling of the decomposition process of carbon tetrafluoride (CF4, Freon R-14), the most stable perfluorinated gas with a significant global warming potential, as experimentally carried out using a thermal argon/steam plasma in a plasma-chemical reactor. The numerical modelling approach incorporates advanced computational fluid dynamics (CFD) techniques and the kinetics of a complex chemical reaction mechanism. A two-dimensional axisymmetric model, incorporating the conservation of mass, momentum, energy and chemical balances, has been developed to simulate the species distribution under the assumption of steady flow, including turbulence effects. In order to compare the species distributions and destruction efficiencies, two chemical reaction mechanism models of different complexity were implemented: a simplified model A with 196 reactions and a model B with 258 reactions. The simulations were performed for plasma generated by steam and argon stabilized DC plasma torch under three operating conditions for arc currents of 120, 150 and 200 A. The destruction and removal efficiency (DRE) of CF4 decomposition at a high feed rate was computed by both models, with an error of less than 6% compared to the experimental data. It can be concluded that both models accurately predict the residual CF4 concentration trends depending on the feed per input torch power. While model A overestimates process efficiency, model B, with a more balanced reaction mechanism, more closely aligns with experimental results. Model B provides an accurate prediction of the DRE and captures trends as a function of input parameters. Importantly and innovatively, the model presented in this study, unlike previous models, allows for the first time the inclusion of both high concentrations and a sufficiently large reaction mechanism for CF4 abatement. This model presents a novel opportunity to perform trend simulations of operating characteristics for the CF4 abatement problem at a quantitative level, allowing experimental design to be evaluated and optimised.

On the interaction between a detonation wave and an inert gas plug: A numerical investigation

Employing inert gases to attenuate and obstruct the propagation of detonation waves has proven to be an effective strategy for mitigating potential damage in the realm of industrial safety, which involves complex physical and chemical mechanisms. This study utilizes an in-house solver built on the OpenFOAM platform to examine the interaction between a detonation wave and an inert gas plug of various lengths. The results reveal that as the length of the inert gas plug increases, various detonation states emerge downstream of the gas plug, and an exponential relationship is observed between the detonation re-initiation distance and the gas plug's length. In the process of detonation re-initiation, the non-isentropic process within the viscous boundary layer plays a crucial role in initiating the flames at the upper and lower channel walls. Later, the collision between flames initiates the detonation wave. Additionally, a localized detonation can also be triggered through the interaction between the compression wave and the wall. Notably, the impingements of the detonation wave and the transmitted shock wave induce the mixing and downstream motion of the gas plug. In the presence of the detonation re-initiation, the motion patterns of the left and right interfaces of the gas plug can be categorized into two distinct stages, which are mainly because of the impingement of backpropagation expansion waves and the hindrance of the high pressure generated by the detonation re-initiation, respectively. Also, as the length of the inert gas plug increases, the velocity difference between the two stages gradually decreases.

Optimize Granular Media Filtration

Effective filtration depends on a combination of complex physical and chemical mechanisms. It's important for operators to know how granular media filters work, the basics of how they are designed, and strategies for successful operation.

Purification of artemisinin precursor amorphadiene from microbial metabolites containing isomeride farnesene: Solid-Pd chemocatalysis of farnesene to squalene with high boiling point

Due to complex chemical mechanism of sesquiterpene synthases, amorpha-4, 11-diene (AD) from microbial fermentation contained isomeride β-farnesene (FA) with similar boiling point. For purifying AD, FA was converted to biofuel-valued squalene (SQ) with high boiling point using solid Pd catalysts. (NHT)OS-Pd catalysts resulted in FA conversion of >99.9% and SQ selectivity of >99.0% at 80 °C for 5 h. Moreover, this catalyst exhibited great stability and recycling performance. When (NHT)OS-Pd was applied to AD mixture, almost FA was converted to SQ and AD remained in the pre-reaction state. As SQ had a higher boiling point, AD was easily distilled out.

A novel dynamic distance coding identification method for oil–gas gathering and transportation process

Deep learning (DL) has become a mainstream method for fault identification in petrochemical processes. However, the high noise and nonlinear coupling of complex data samples have led to different degrees of low accuracy and robustness problems in the method. Meanwhile, the fault cause is difficult to capture due to the complex chemical process operation mechanism. To address this challenge, a novel dynamic distance coding method incorporating DL is proposed to identify anomalies in real time. First, the collected normal process data are smoothed by the Savitzky–Golay filter to build a normal sample set. Then, dynamic coding based on the distance metric is introduced to compute the distribution of normal and real-time samples for extracting the spatial domain features. By a sliding window, dynamic coded maps are generated and analyzed for fault causes. Finally, the time-domain information is extracted by long short-term memory (LSTM) to learn the deep features of the encoded graph for fault identification. The proposed method was applied to an oil–gas gathering and transportation process, which proves its feasibility and effectiveness. Compared with the conventional LSTM, the F1 score of the method is improved by 0.193, reaching 0.986. The obtained visualization information enables explaining the causes and supplements the fault database, providing a valuable reference for workers’ feedback operations.

A chemo-mechano-biological modeling framework for cartilage evolving in health, disease, injury, and treatment

Background and ObjectiveOsteoarthritis (OA) is a pervasive and debilitating disease, wherein degeneration of cartilage features prominently. Despite extensive research, we do not yet understand the cause or progression of OA. Studies show biochemical, mechanical, and biological factors affect cartilage health. Mechanical loads influence synthesis of biochemical constituents which build and/or break down cartilage, and which in turn affect mechanical loads. OA-associated biochemical profiles activate cellular activity that disrupts homeostasis. To understand the complex interplay among mechanical stimuli, biochemical signaling, and cartilage function requires integrating vast research on experimental mechanics and mechanobiology—a task approachable only with computational models. At present, mechanical models of cartilage generally lack chemo-biological effects, and biochemical models lack coupled mechanics, let alone interactions over time. MethodsWe establish a first-of-its kind virtual cartilage: a modeling framework that considers time-dependent, chemo-mechano-biologically induced turnover of key constituents resulting from biochemical, mechanical, and/or biological activity. We include the “minimally essential” yet complex chemical and mechanobiological mechanisms. Our 3-D framework integrates a constitutive model for the mechanics of cartilage with a novel model of homeostatic adaptation by chondrocytes to pathological mechanical stimuli, and a new application of anisotropic growth (loss) to simulate degradation clinically observed as cartilage thinning. ResultsUsing a single set of representative parameters, our simulations of immobilizing and overloading successfully captured loss of cartilage quantified experimentally. Simulations of immobilizing, overloading, and injuring cartilage predicted dose-dependent recovery of cartilage when treated with suramin, a proposed therapeutic for OA. The modeling framework prompted us to add growth factors to the suramin treatment, which predicted even better recovery. ConclusionsOur flexible framework is a first step toward computational investigations of how cartilage and chondrocytes mechanically and biochemically evolve in degeneration of OA and respond to pharmacological therapies. Our framework will enable future studies to link physical activity and resulting mechanical stimuli to progression of OA and loss of cartilage function, facilitating new fundamental understanding of the complex progression of OA and elucidating new perspectives on causes, treatments, and possible preventions.

Hybrid modeling and distributed optimization control method for the iron removal process

<p style='text-indent:20px;'>Iron removal by goethite is a key procedure in zinc hydrometallurgy. Due to its complex chemical reaction mechanism, multiple reactors cascade, and high uncertainty, it is difficult to optimize and control in industrial process. In this paper, a distributed optimization control method which is based on a novel hybrid model of the iron removal process is proposed. By combining the mechanism model and a data-driven oxygen mass transfer coefficient model, a hybrid model is first established. Then, to overcome the influence of the former reactor on the latter reactor, the ratio of the status in each subsystem to the set point is taken as a new status, and a distributed optimization control problem is constructed. Considering the high dimensionality of this problem, it is necessary to reconstruct it by Virtual Motion Camouflage (VMC), so that the optimal control problem is transformed into a nonlinear constrained optimal trajectory planning problem. And a Legendre pseudo-spectral method is used to solve the problem accurately to obtain the optimal trajectory of the ion concentration. Finally, simulation results show that the proposed method can effectively reflect the industrial process, and track the fluctuation of inlet ion concentrations with a nice real-time performance.</p>

TChem: A performance portable parallel software toolkit for complex kinetic mechanisms

We present TChem, a performance portable software toolkit to enable the analysis of complex kinetic mechanisms. The software provides tools for gas-phase and surface chemistry, thermodynamic properties, and implements formulations for several canonical reactor models. Analytical derivatives necessary to construct Jacobian matrices corresponding to all implemented functionalities are available through automatic differentiation. TChem uses the Kokkos framework to achieve portability across multiple heterogeneous computing platforms with a single version of the code. We implement a hierarchical parallelism framework to enable efficient chemical source term and thermodynamic property evaluations over the number of samples assigned to the local computing device. We analyze parallel efficiency results extracted from test cases for thermodynamic properties, source terms, and Jacobians evaluations on Intel Xeon CPUs and NVIDIA Volta GPUs. Program summaryProgram Title: TChemCPC Library link to program files:https://doi.org/10.17632/25prt5g35w.1Developer's repository link:https://github.com/sandialabs/TChemLicensing provisions: BSD 2-clauseProgramming language: C++Nature of problem: TChem seeks efficient solutions of models that involve complex chemical kinetic mechanisms, which are essential for various applications areas such as combustion modeling, atmospheric chemistry and catalysis. In general, a detailed chemical kinetic mechanism involves a number of species and associated elementary reactions. The system of governing equations is typically highly non-linear and stiff, involving a large range of time scales.Solution method: TChem provides source term functions and numerical/analytic Jacobians for canonical reactor models. In particular, the analytic Jacobian is computed using automatic differentiation techniques implemented in the SACADO library. For solving stiff time ODE/DAE systems, TChem uses the Tines implementation of an adaptive 2nd-order backward difference formula to obtain efficient solutions that evolve on a range of time scales. The software employs batch parallelism to exploit massively parallel modern computing devices. Target workflows correspond to reactive flow problems or uncertainty quantification studies, which require the solutions of many samples corresponding to different grid points or input conditions. The software provides performance portable implementations aiming to use modern heterogeneous computing platforms i.e., multi/many core host processors with external GPU accelerators. We achieve the performance portability using the Kokkos parallel programming model that enables translation of a single Kokkos implementation to multiple device-specific backend codes.

Cluster-based characterization of multi-dimensional tropospheric ozone variability in coastal regions: an analysis of lidar measurements and model results

Abstract. Coastal regions are susceptible to multiple complex dynamic and chemical mechanisms and emission sources that lead to frequently observed large tropospheric ozone variations. These large ozone variations occur on a mesoscale and have proven to be arduous to simulate using chemical transport models (CTMs). We present a clustering analysis of multi-dimensional measurements from ozone lidar in conjunction with both an offline GEOS-Chem chemical-transport model (CTM) simulation and the online GEOS-Chem simulation GEOS-CF, to investigate the vertical and temporal variability of coastal ozone during three recent air quality campaigns: 2017 Ozone Water-Land Environmental Transition Study (OWLETS)-1, 2018 OWLETS-2, and 2018 Long Island Sound Tropospheric Ozone Study (LISTOS). We developed and tested a clustering method that resulted in five ozone profile curtain clusters. The established five clusters all varied significantly in ozone magnitude vertically and temporally, which allowed us to characterize the coastal ozone behavior. The lidar clusters provided a simplified way to evaluate the two CTMs for their performance of diverse coastal ozone cases. An overall evaluation of the models reveals good agreement (R≈0.70) in the low-level altitude range (0 to 2000 m), with a low and unsystematic bias for GEOS-Chem and a high systemic positive bias for GEOS-CF. The mid-level (2000–4000 m) performances show a high systematic negative bias for GEOS-Chem and an overall low unsystematic bias for GEOS-CF and a generally weak agreement to the lidar observations (R=0.12 and 0.22, respectively). Evaluating cluster-by-cluster model performance reveals additional model insight that is overlooked in the overall model performance. Utilizing the full vertical and diurnal ozone distribution information specific to lidar measurements, this work provides new insights on model proficiency in complex coastal regions.