Initiation and Carbene Induced Radical Chain Reactions in CH2F2 Pyrolysis.

High temperature dissociations of organic molecules typically involve a competition between radical and molecular processes. In this work, we use a modeling, experiment, theory (MET) framework to characterize the high temperature thermal dissociation of CH2F2, a flammable hydrofluorocarbon (HFC) that finds widespread use as a refrigerant. Initiation in CH2F2 proceeds via a molecular elimination channel; CH2F2→CHF+HF. Here we show that the subsequent self-reactions of the singlet carbene, CHF, are fast multichannel processes and a facile source of radicals that initiate rapid chain propagation reactions. These have a marked influence on the decomposition kinetics of CH2F2. The inclusion of these reactions brings the simulations into better agreement with the present and literature experiments. Additionally, flame simulations indicate that inclusion of the CHF+CHF multichannel reaction leads to a noticeable enhancement in predictions of laminar flame speeds, a key parameter that is used to determine the flammability of a refrigerant.

Kinetic analysis reveals that independent nucleation events determine the progression of polyglutamine aggregation in C. elegans.

Protein aggregation is associated with a wide range of degenerative human diseases with devastating consequences, as exemplified by Alzheimer's, Parkinson's, and Huntington's diseases. In vitro kinetic studies have provided a mechanistic understanding of the aggregation process at the molecular level. However, it has so far remained largely unclear to what extent the biophysical principles of amyloid formation learned in vitro translate to the complex environment of living organisms. Here, we take advantage of the unique properties of a Caenorhabditis elegans model expressing a fluorescently tagged polyglutamine (polyQ) protein, which aggregates into discrete micrometer-sized inclusions that can be directly visualized in real time. We provide a quantitative analysis of protein aggregation in this system and show that the data are described by a molecular model where stochastic nucleation occurs independently in each cell, followed by rapid aggregate growth. Global fitting of the image-based aggregation kinetics reveals a nucleation rate corresponding to 0.01 h-1 per cell at 1 mM intracellular protein concentration, and shows that the intrinsic molecular stochasticity of nucleation accounts for a significant fraction of the observed animal-to-animal variation. Our results highlight how independent, stochastic nucleation events in individual cells control the overall progression of polyQ aggregation in a living animal. The key finding that the biophysical principles associated with protein aggregation in small volumes remain the governing factors, even in the complex environment of a living organism, will be critical for the interpretation of in vivo data from a wide range of protein aggregation diseases.

Hamiltonian Computational Chemistry: Geometrical Structures in Chemical Dynamics and Kinetics.

The common geometrical (symplectic) structures of classical mechanics, quantum mechanics, and classical thermodynamics are unveiled with three pictures. These cardinal theories, mainly at the non-relativistic approximation, are the cornerstones for studying chemical dynamics and chemical kinetics. Working in extended phase spaces, we show that the physical states of integrable dynamical systems are depicted by Lagrangian submanifolds embedded in phase space. Observable quantities are calculated by properly transforming the extended phase space onto a reduced space, and trajectories are integrated by solving Hamilton's equations of motion. After defining a Riemannian metric, we can also estimate the length between two states. Local constants of motion are investigated by integrating Jacobi fields and solving the variational linear equations. Diagonalizing the symplectic fundamental matrix, eigenvalues equal to one reveal the number of constants of motion. For conservative systems, geometrical quantum mechanics has proved that solving the Schrödinger equation in extended Hilbert space, which incorporates the quantum phase, is equivalent to solving Hamilton's equations in the projective Hilbert space. In classical thermodynamics, we take entropy and energy as canonical variables to construct the extended phase space and to represent the Lagrangian submanifold. Hamilton's and variational equations are written and solved in the same fashion as in classical mechanics. Solvers based on high-order finite differences for numerically solving Hamilton's, variational, and Schrödinger equations are described. Employing the Hénon-Heiles two-dimensional nonlinear model, representative results for time-dependent, quantum, and dissipative macroscopic systems are shown to illustrate concepts and methods. High-order finite-difference algorithms, despite their accuracy in low-dimensional systems, require substantial computer resources when they are applied to systems with many degrees of freedom, such as polyatomic molecules. We discuss recent research progress in employing Hamiltonian neural networks for solving Hamilton's equations. It turns out that Hamiltonian geometry, shared with all physical theories, yields the necessary and sufficient conditions for the mutual assistance of humans and machines in deep-learning processes.

Enhancement of Prebiotic Peptide Formation in Cyclic Environments.

The dynamic behaviors of prebiotic reaction networks may be critically important to understanding how larger biopolymers could emerge, despite being unfavorable to form in water. We focus on understanding the dynamics of simple systems, prior to the emergence of replication mechanisms, and what role they may have played in biopolymer formation. We specifically consider the dynamics in cyclic environments using both model and experimental data. Cyclic environmental conditions prevent a system from reaching thermodynamic equilibrium, improving the chance of observing interesting kinetic behaviors. We used an approximate kinetic model to simulate the dynamics of trimetaphosphate (TP)-activated peptide formation from glycine in cyclic wet-dry conditions. The model predicts that environmental cycling allows trimer and tetramer peptides to sustain concentrations above the predicted fixed points of the model due to overshoot, a dynamic phenomenon. Our experiments demonstrate that oscillatory environments can shift product distributions in favor of longer peptides. However, experimental validation of certain behaviors in the kinetic model is challenging, considering that open systems with cyclic environmental conditions break many of the common assumptions in classical chemical kinetics. Overall, our results suggest that the dynamics of simple peptide reaction networks in cyclic environments may have been important for the formation of longer polymers on the early Earth. Similar phenomena may have also contributed to the emergence of reaction networks with product distributions determined not by thermodynamics, but rather by kinetics.

Direct kinetic measurements and theoretical predictions of an isoprene-derived Criegee intermediate.

Isoprene has the highest emission into Earth's atmosphere of any nonmethane hydrocarbon. Atmospheric processing of alkenes, including isoprene, via ozonolysis leads to the formation of zwitterionic reactive intermediates, known as Criegee intermediates (CIs). Direct studies have revealed that reactions involving simple CIs can significantly impact the tropospheric oxidizing capacity, enhance particulate formation, and degrade local air quality. Methyl vinyl ketone oxide (MVK-oxide) is a four-carbon, asymmetric, resonance-stabilized CI, produced with 21 to 23% yield from isoprene ozonolysis, yet its reactivity has not been directly studied. We present direct kinetic measurements of MVK-oxide reactions with key atmospheric species using absorption spectroscopy. Direct UV-Vis absorption spectra from two independent flow cell experiments overlap with the molecular beam UV-Vis-depletion spectra reported recently [M. F. Vansco, B. Marchetti, M. I. Lester, J. Chem. Phys. 149, 44309 (2018)] but suggest different conformer distributions under jet-cooled and thermal conditions. Comparison of the experimental lifetime herein with theory indicates only the syn-conformers are observed; anti-conformers are calculated to be removed much more rapidly via unimolecular decay. We observe experimentally and predict theoretically fast reaction of syn-MVK-oxide with SO2 and formic acid, similar to smaller alkyl-substituted CIs, and by contrast, slow removal in the presence of water. We determine products through complementary multiplexed photoionization mass spectrometry, observing SO3 and identifying organic hydroperoxide formation from reaction with SO2 and formic acid, respectively. The tropospheric implications of these reactions are evaluated using a global chemistry and transport model.

Thinking in Terms of Change over Time: Opportunities and Challenges of Using System Dynamics Models.

Understanding the world around us is a growing necessity for the whole public, as citizens are required to make informed decisions in their everyday lives about complex issues. Systems thinking (ST) is a promising approach for developing solutions to various problems that society faces and has been acknowledged as a crosscutting concept that should be integrated across educational science disciplines. However, studies show that engaging students in ST is challenging, especially concerning aspects like change over time and feedback. Using computational system models and a system dynamics approach can support students in overcoming these challenges when making sense of complex phenomena. In this paper, we describe an empirical study that examines how 10th grade students engage in aspects of ST through computational system modeling as part of a Next Generation Science Standards-aligned project-based learning unit on chemical kinetics. We show students' increased capacity to explain the underlying mechanism of the phenomenon in terms of change over time that goes beyond linear causal relationships. However, student models and their accompanying explanations were limited in scope as students did not address feedback mechanisms as part of their modeling and explanations. In addition, we describe specific challenges students encountered when evaluating and revising models. In particular, we show epistemological barriers to fruitful use of real-world data for model revision. Our findings provide insights into the opportunities of a system dynamics approach and the challenges that remain in supporting students to make sense of complex phenomena and nonlinear mechanisms.

Analysis of Ozonation Processes Using Coupled Modeling of Fluid Dynamics, Mass Transfer, and Chemical Reaction Kinetics.

The efficacy of oxidation of recalcitrant organic contaminants in municipal and industrial wastewaters by ozonation is influenced by chemical reaction kinetics and hydrodynamics within a reactor. A 3D computational fluid dynamics (CFD) model incorporating both multiphase flow and reaction kinetics describing ozone decay, hydroxyl radical (•OH) generation, and organic oxidation was developed to simulate the performance of continuous flow ozonation reactors. Formate was selected as the target organic in this study due to its well-understood oxidation pathway. Simulation results revealed that the dissolved ozone concentration in the reactor is controlled by rates of O3(g) interphase transfer and ozone self-decay. Simulations of the effect of various operating conditions showed that the reaction stoichiometric constraints between formate and ozone were reached; however, complete utilization of gas phase ozone was hard to achieve due to the low ozone interphase mass transfer rate. Increasing the O3(g) concentration leads to an increase in the formate removal rate by ∼5% due to an enhancement in the rate of O3(g) interphase mass transfer. The CFD model adequately describes the mass transfer occurring in the two-phase flow system and confirms that O3(g) interphase mass transfer is the rate-limiting step in contaminant degradation. The model can be used to optimize the ozone reactor design for improved contaminant degradation and ozonation efficiency.

Long-term behaviours of Autocatalytic Sets.

Autocatalytic Sets are reaction networks theorised as networks at the basis of life. Their main feature is the ability of spontaneously emerging and self-reproducing. The Reflexively and Food-generated theory provides a formal definition of Autocatalytic Sets in terms of graphs with peculiar topological properties. This formalisation has been proved to be a powerful tool for the study of the chemical networks underlying life, and it was able to identify autocatalytic structures in real metabolic networks. However, the dynamical behaviour of such networks has not been yet complitely clarified. In this work, I present a first attempt to connect the topology of an Autocatalytic Set with its dynamics. For this purpose, I represent Autocatalytic Sets in terms of Chemical Reaction Networks, and I use the Chemical Reaction Network theory to detect motifs in the networks'structure, that allow to determine the long-term behaviour of the system.

Representation of Multiphase OH Oxidation of Amorphous Organic Aerosol for Tropospheric Conditions.

Organic aerosol (OA) is ubiquitous in the atmosphere and, during transport, can experience chemical transformation with consequences for air quality and climate. Prediction of the chemical evolution of OA depends on its reactivity with atmospheric oxidants such as the OH radical. OA particles undergo amorphous phase transitions from liquid to solid (glassy) states in response to temperature changes, which, in turn, will impact its reactivity toward OH oxidation. To improve the predictability of OA reactivity toward OH oxidation, the reactive uptake coefficients (γ) of OH radicals reacting with triacontane and squalane serving as amorphous OA surrogates were measured at temperatures from 213-293 K. γ increases strongest with temperature when the organic species is in the liquid phase, compared to when being in the semisolid or solid phase. The resistor model is applied, accounting for the amorphous phase state changes using the organic species' glass transition temperature and fragility, to evaluate the physicochemical parameters of the temperature dependent OH uptake process. This allows for the derivation of a semiempirical formula, applicable to models, to predict the degree of oxidation and chemical lifetime of the condensed-phase organic species for typical tropospheric temperature and humidity when OA particle viscosity is known.

SAR by kinetics for drug discovery in protein misfolding diseases.

To develop effective therapeutic strategies for protein misfolding diseases, a promising route is to identify compounds that inhibit the formation of protein oligomers. To achieve this goal, we report a structure-activity relationship (SAR) approach based on chemical kinetics to estimate quantitatively how small molecules modify the reactive flux toward oligomers. We use this estimate to derive chemical rules in the case of the amyloid beta peptide (Aß), which we then exploit to optimize starting compounds to curtail Aß oligomer formation. We demonstrate this approach by converting an inactive rhodanine compound into an effective inhibitor of Aß oligomer formation by generating chemical derivatives in a systematic manner. These results provide an initial demonstration of the potential of drug discovery strategies based on targeting directly the production of protein oligomers.

A Stability-Indicating Ultra Performance Liquid Chromato-Graphic (UPLC) Method for the Determination of a Mycophenolic Acid-Curcumin Conjugate and Its Applications to Chemical Kinetic Studies.

A simple, precise, and accurate reversed-phase ultra-performance liquid chromatographic (UPLC) method was developed and validated for the determination of a mycophenolic acid-curcumin (MPA-CUR) conjugate in buffer solutions. Chromatographic separation was performed on a C18 column (2.1 × 50 mm id, 1.7 µm) with a gradient elution system of water and acetonitrile, each containing 0.1% formic acid, at a flow rate of 0.6 mL/min. The column temperature was controlled at 33 °C. The compounds were detected simultaneously at the maximum wavelengths of mycophenolic acid (MPA), 254 nm, and curcumin (CUR), or MPA-CUR, at 420 nm. The developed method was validated according to the ICH Q2(R1) guidelines. The linear calibration curves of the assay ranged from 0.10 to 25 µg/mL (r2 ≥ 0.995, 1/x2 weighting factor), with a limit of detection and a limit of quantitation of 0.04 and 0.10 µg/mL, respectively. The accuracy and precision of the developed method were 98.4-101.6%, with %CV < 2.53%. The main impurities from the specificity test were found to be MPA and CUR. Other validation parameters, including robustness and solution stability, were acceptable under the validation criteria. Forced degradation studies were conducted under hydrolytic (acidic and alkaline), oxidative, thermal, and photolytic stress conditions. MPA-CUR was well separated from MPA, CUR, and other unknown degradation products. The validated method was successfully applied in chemical kinetic studies of MPA-CUR in different buffer solutions.

Review on the Use of Artificial Intelligence to Predict Fire Performance of Construction Materials and Their Flame Retardancy.

The evaluation and interpretation of the behavior of construction materials under fire conditions have been complicated. Over the last few years, artificial intelligence (AI) has emerged as a reliable method to tackle this engineering problem. This review summarizes existing studies that applied AI to predict the fire performance of different construction materials (e.g., concrete, steel, timber, and composites). The prediction of the flame retardancy of some structural components such as beams, columns, slabs, and connections by utilizing AI-based models is also discussed. The end of this review offers insights on the advantages, existing challenges, and recommendations for the development of AI techniques used to evaluate the fire performance of construction materials and their flame retardancy. This review offers a comprehensive overview to researchers in the fields of fire engineering and material science, and it encourages them to explore and consider the use of AI in future research projects.

Modeling and simulations of CoViD-19 molecular mechanism induced by cytokines storm during SARS-CoV2 infection.

It is highly desired to explore the interventions of COVID-19 for early treatment strategies. Such interventions are still under consideration. A model is benchmarked research and comprises target cells, virus infected cells, immune cells, pro-inflammatory cytokines, and, anti-inflammatory cytokine. The interaction of the drug with the inflammatory sub-system is analyzed with the aid of kinetic modeling. The impact of drug therapy on the immune cells is modelled and the computational framework is verified with the aid of numerical simulations. The work includes a significant hypothesis that quantifies the complex dynamics of the infection, by relating it to the effect of the inflammatory syndrome generated by IL-6. In this paper we use the cancer immunoediting process: a dynamic process initiated by cancer cells in response to immune surveillance of the immune system that it can be conceptualized by an alternating movement that balances immune protection with immune evasion. The mechanisms of resistance to immunotherapy seem to broadly overlap with those used by cancers as they undergo immunoediting to evade detection by the immune system. In this process the immune system can both constrain and promote tumour development, which proceeds through three phases termed: (i) Elimination, (ii) Equilibrium, and, (iii) Escape [1]. We can also apply these concepts to viral infection, which, although it is not exactly "immunoediting", has many points in common and helps to understand how it expands into an "untreated" host and can help in understanding the SARS-CoV2 virus infection and treatment model.

Efficient and recyclable palladium enriched magnetic nanocatalyst for reduction of toxic environmental pollutants.

In this paper, highly stable, powerful, and recyclable magnetic nanoparticles tethered N-heterocyclic carbene-palladium(II) ((CH3)3-NHC-Pd@Fe3O4) as magnetic nanocatalyst was successfully synthesized from a simplistic multistep synthesis under aerobic conditions through easily available low-cost chemicals. Newly synthesized (CH3)3-NHC-Pd@Fe3O4 magnetic nanocatalyst was characterized from various analytical tools and catalytic potential of the (CH3)3-NHC-Pd@Fe3O4 magnetic nanocatalyst was studied for the catalytic reduction of toxic 4-nitrophenol (4-NP), hexavalent chromium (Cr(VI)), Methylene Blue (MB) and Methyl Orange (MO) at room temperature in aqueous media. UV-Visible spectroscopy was employed to monitor the reduction reactions. New (CH3)3-NHC-Pd@Fe3O4 magnetic nanocatalyst exhibited excellent catalytic activity for the reduction of toxic environmental pollutants. Moreover, (CH3)3-NHC-Pd@Fe3O4 magnetic nanocatalyst could be easily and rapidly separated from the reaction mixture with the help of an external magnet and recycled minimum five times in reduction of 4-NP, MB, MO and four times in Cr(VI) without significant loss of catalytic potential and remains stable even after reuse.

Rational Design of a Cocktail of Inhibitors against Aß Aggregation.

It has been reported that many molecules could inhibit the aggregation of Aß (amyloid-ß) through suppressing either primary nucleation, secondary nucleation, or elongation processes. In order to suppress multiple pathways of Aß aggregation, we screened 23 small molecules and found two types of inhibitors with different inhibiting mechanisms based on chemical kinetics analysis. Trp-glucose conjugates (AS2) could bind with fibril ends while natural products (D3 and D4) could associate with monomers. A cocktail of these two kinds of molecules achieved co-inhibition of various fibrillar species and avoid unwanted interference.

Reactions of Criegee Intermediates are Enhanced by Hydrogen-Atom Relay Through Molecular Design.

We report a type of highly efficient double hydrogen atom transfer (DHAT) reaction. The reactivities of 3-aminopropanol and 2-aminoethanol towards Criegee intermediates (syn- and anti-CH3 CHOO) were found to be much higher than those of n-propanol and propylamine. Quantum chemistry calculation has confirmed that the main mechanism of these very rapid reactions is DHAT, in which the nucleophilic attack of the NH2 group is catalyzed by the OH group which acts as a bridge of HAT. Typical gas-phase DHAT reactions are termolecular reactions involving two hydrogen bonding molecules; these reactions are typically slow due to the substantial entropy reduction of bringing three molecules together. Putting the reactive and catalytic groups in one molecule circumvents the problem of entropy reduction and allows us to observe the DHAT reactions even at low reactant concentrations. This idea can be applied to improve theoretical predictions for atmospherically relevant DHAT reactions.

An ecological framework for the analysis of prebiotic chemical reaction networks.

It is becoming widely accepted that very early in life's origin, even before the emergence of genetic encoding, reaction networks of diverse small chemicals might have manifested key properties of life, namely self-propagation and adaptive evolution. To explore this possibility, we formalize the dynamics of chemical reaction networks within the framework of chemical ecosystem ecology. To capture the idea that life-like chemical systems are maintained out of equilibrium by fluxes of energy-rich food chemicals, we model chemical ecosystems in well-mixed compartments that are subject to constant dilution by a solution with a fixed concentration of input chemicals. Modelling all chemical reactions as fully reversible, we show that seeding an autocatalytic cycle with tiny amounts of one or more of its member chemicals results in logistic growth of all member chemicals in the cycle. This finding justifies drawing an instructive analogy between an autocatalytic cycle and a biological species. We extend this finding to show that pairs of autocatalytic cycles can exhibit competitive, predator-prey, or mutualistic associations just like biological species. Furthermore, when there is stochasticity in the environment, particularly in the seeding of autocatalytic cycles, chemical ecosystems can show complex dynamics that can resemble evolution. The evolutionary character is especially clear when the network architecture results in ecological precedence, which makes a system's trajectory historically contingent on the order in which cycles are seeded. For all its simplicity, the framework developed here helps explain the onset of adaptive evolution in prebiotic chemical reaction networks, and can shed light on the origin of key biological attributes such as thermodynamic irreversibility and genetic encoding.

Microscopic dynamics of charge separation at the aqueous electrochemical interface.

We have used molecular simulation and methods of importance sampling to study the thermodynamics and kinetics of ionic charge separation at a liquid water-metal interface. We have considered this process using canonical examples of two different classes of ions: a simple alkali-halide pair, Na+I-, or classical ions, and the products of water autoionization, H3O+OH-, or water ions. We find that for both ion classes, the microscopic mechanism of charge separation, including water's collective role in the process, is conserved between the bulk liquid and the electrode interface. However, the thermodynamic and kinetic details of the process differ between these two environments in a way that depends on ion type. In the case of the classical ion pairs, a higher free-energy barrier to charge separation and a smaller flux over that barrier at the interface result in a rate of dissociation that is 40 times slower relative to the bulk. For water ions, a slightly higher free-energy barrier is offset by a higher flux over the barrier from longer lived hydrogen-bonding patterns at the interface, resulting in a rate of association that is similar both at and away from the interface. We find that these differences in rates and stabilities of charge separation are due to the altered ability of water to solvate and reorganize in the vicinity of the metal interface.

Monomeric and fibrillar α-synuclein exert opposite effects on the catalytic cycle that promotes the proliferation of Aß42 aggregates.

The coaggregation of the amyloid-ß peptide (Aß) and α-synuclein is commonly observed in a range of neurodegenerative disorders, including Alzheimer's and Parkinson's diseases. The complex interplay between Aß and α-synuclein has led to seemingly contradictory results on whether α-synuclein promotes or inhibits Aß aggregation. Here, we show how these conflicts can be rationalized and resolved by demonstrating that different structural forms of α-synuclein exert different effects on Aß aggregation. Our results demonstrate that whereas monomeric α-synuclein blocks the autocatalytic proliferation of Aß42 (the 42-residue form of Aß) fibrils, fibrillar α-synuclein catalyses the heterogeneous nucleation of Aß42 aggregates. It is thus the specific balance between the concentrations of monomeric and fibrillar α-synuclein that determines the outcome of the Aß42 aggregation reaction.

Facilitated dissociation of transcription factors from single DNA binding sites.

The binding of transcription factors (TFs) to DNA controls most aspects of cellular function, making the understanding of their binding kinetics imperative. The standard description of bimolecular interactions posits that TF off rates are independent of TF concentration in solution. However, recent observations have revealed that proteins in solution can accelerate the dissociation of DNA-bound proteins. To study the molecular basis of facilitated dissociation (FD), we have used single-molecule imaging to measure dissociation kinetics of Fis, a key Escherichia coli TF and major bacterial nucleoid protein, from single dsDNA binding sites. We observe a strong FD effect characterized by an exchange rate [Formula: see text], establishing that FD of Fis occurs at the single-binding site level, and we find that the off rate saturates at large Fis concentrations in solution. Although spontaneous (i.e., competitor-free) dissociation shows a strong salt dependence, we find that FD depends only weakly on salt. These results are quantitatively explained by a model in which partially dissociated bound proteins are susceptible to invasion by competitor proteins in solution. We also report FD of NHP6A, a yeast TF with structure that differs significantly from Fis. We further perform molecular dynamics simulations, which indicate that FD can occur for molecules that interact far more weakly than those that we have studied. Taken together, our results indicate that FD is a general mechanism assisting in the local removal of TFs from their binding sites and does not necessarily require cooperativity, clustering, or binding site overlap.