Chemical Network Research Articles

Health Benefits of Antioxidant Bioactive Compounds in Ginger (Zingiber officinale) Leaves by Network Pharmacology Analysis Combined with Experimental Validation.

Network pharmacology is an ideal tool to explore the effects of therapeutic components derived from plants on human metabolic diseases that are linked to inflammation. This study investigated the antioxidant effects of ginger leaves (GLs) and predicted targets for antioxidant activity. Quantitative and free radical scavenging analyses were performed to detect the main bioactive compounds of GLs and evaluate their antioxidant activities. Chemical diversity and network pharmacology approaches were used to predict key antioxidant components of GLs and their molecular targets. Nine major bioactive compounds of GLs were quantified using an internal standard method, and the antioxidant activity was evaluated using the DPPH and ABTS free radical scavenging methods. We first built the compound-gene-pathways and protein-protein interaction networks of GLs-related antioxidant targets and then conducted gene ontology and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment analyses. Molecular docking results show that astragalin, a compound isolated from GLs, had the highest level of connectivity in the compound-target network and was involved in inflammation-related biosynthesis by directly impacting cytokine gene expression and PTGS2 inhibition markers. These findings not only suggest that the compounds isolated from GLs can be developed as potential antioxidants, but also demonstrate the applicability of network pharmacology to assess the potential of foods for disease treatment.

Computing edge version of metric dimension of certain chemical networks

In the modern digital sphere, graph theory is a significant field of research that has a great deal of significance. It finds widespread application in computer science, robotic directions, and chemistry. Additionally, graph theory is used in robot network localization, computer network problems and the formation of various chemical structures for networks. Moreover, it finds uses in exploring diffusion mechanisms and scheduling aircraft as well. The present research project examines and concentrates on the edge version of metric dimension of the Concealed Non-Kekuléan Benzenoid Hydrocarbon, Polythiophene, Backbone DNA network and Bakelite networks. All the mentioned networks have constant edge metric dimension except Bakelite network, as demonstrated by the results. If we talk about the applications of these networks, Polythiophene are used to treat prion disorders. It is also capable of detecting metal ions. The concept of Bakelite, which finds applications in the jewelry, electrical, cookware, sports, and clock industries, had an impact on the invention of modern polymers. The functions of DNA include information encoding, replication, mutation, and recombination gene expression.

Non-targeted mass spectrometry and feature-based molecular networking for determination of branched fatty acid esters of hydroxy fatty acids in milk.

Branched fatty acid esters of hydroxy fatty acids (FAHFAs) represent trace lipids with significant natural biological functions. While exogenous FAHFAs have been extensively studied, research on FAHFAs in milk remains limited, constraining our grasp of their nutritional roles. This study introduces a non-targeted mass spectrometry approach combined with chemical networking of spectral fragmentation patterns to uncover FAHFAs. Through meticulous sample handling and comparisons of various data acquisition and processing modes, we validate the method's superiority, identifying twice as many FAHFAs compared to alternative techniques. This validated method was then applied to different milk samples, revealing 45 chemical signals associated with known and potential FAHFAs, alongside findings of 66 ceramide/hexosylceramide (Cer/HexCer), 48 phosphatidyl ethanolamine/lyso phosphatidyl ethanolamine (PE/LPE), 21 phosphatidylcholine/lysophosphatidylcholine (PC/LPC), 16 phosphatidylinositol (PI), 7 phosphatidylserine (PS), and 11 sphingomyelin (SM) compounds. This study expands our understanding of the FAHFA family in milk and provides a fast and convenient method for identifying FAHFAs.

Molecular synthesis strategies for binary MO2 (M = V, Sn, Ti, Zr, Hf) high-entropy oxides as superior catalysts for enhanced oxygen evolution

Entropy stabilized multi-cation oxides are of significant interest in heterogeneous catalysis due to higher number of active sites, when compared to simple binary oxides, which reduces the overpotential for catalytic transformations. To control the enthalpy of mixing and suppress potential phase segregation, the use of molecular metal alkoxides for chemical cross-linking of different metal centers shows promise in creating homogenous high entropy chemical preceramic networks. Herein, we report on a high-entropy MO2 phase (M = V, Sn, Ti, Zr, Hf), synthesized by cross-linking partially hydrolyzed metal iso-propoxides [M(OH)n(OiPr)4-n]x. The amorphous xerogels calcined at 700 °C, 900 °C and 1250 °C revealed the formation of a single crystalline phase [(V, Sn, Ti, Zr, Hf)O2] that was verified by powder X-ray diffraction analysis, whereas the chemical composition was investigated by X-ray photoelectron spectroscopy and X-ray absorption spectroscopy. The HEO exhibited superior catalytic activity in oxygen evolution reaction with an overpotential of 312.7 mV and an electrochemically active surface area of 0.625 F cm−2. Furthermore, the chronopotentiometry measurements confirmed a high stability (> 90 h) for the HEO samples.

PRODIGE - Planet-forming disks in Taurus with NOEMA. II. Modeling the CO (2-1) isotopologue emission of the Class II T Tauri disks in Taurus

To understand how planets form in protoplanetary disks, it is necessary to characterize their gas and dust distribution and masses. This requires a combination of high-resolution dust continuum and molecular line interferometric observations, coupled with advanced theoretical models of protoplanetary disk physics, chemical composition, and radiative transfer. We aim to constrain the gas density and temperature distributions as well as gas masses in several T Tauri protoplanetary disks located in Taurus. We use the 12CO, 13CO, and C18O (2-1) isotopologue emission observed at $0.9 with the IRAM NOrthern Extended Millimeter Array (NOEMA) as part of the MPG-IRAM Observatory Program PRODIGE (PROtostars and DIsks: Global Evolution \, PIs: P. Caselli Th. Henning). Our sample consists of Class II disks with no evidence of strong radial substructures. We use these data to constrain the thermal and chemical structure of these disks through theoretical models for gas emission. To fit the combined optically thick and thin CO line data in Fourier space, we developed the DiskCheF code, which includes the parameterized disk physical structure, machine-learning (ML) accelerated chemistry, and the RADMC-3D line radiative transfer module. A key novelty of DiskCheF is the fast and feasible ML-based chemistry trained on the extended grid of the disk physical-chemical models precomputed with the ANDES2 code. This ML approach allows complex chemical kinetics models to be included in a time-consuming disk fitting without the need to run a chemical code. We present a novel approach to incorporate chemistry into disk modeling without the need to explicitly calculate a chemical network every time. Using this new disk modeling tool, we successfully fit the 12CO, 13CO, and C$ $O (2-1) data from the CI, CY, DL, DM, DN, and IQ Tau disks. The combination of optically thin and optically thick CO lines allows us to simultaneously constrain the disk temperature and mass distribution, and derive the CO-based gas masses. The best-fit disk gas masses range between 0.005 and $0.04 M_ sun $. These values are in reasonable agreement with the disk dust masses rescaled by a factor of 100 as well as with other indirect gas measurements via, for example, modeling of the wavelength dependence of the dust continuum emission radii, and HD and CO isotopologue emission.

Strong and tough conductive hydrogels via an amphiphilic macromolecular crosslinker for flexible strain sensor

The strength and toughness of hydrogel are usually achieved through the synergy of chemical cross-linking and physical cross-linking from multi-component contributions. However, there are few reports on hydrogels that rely on a single component to obtain two cross-linked structures. Herein, we successfully synthesized an amphiphilic macromolecular crosslinker (polyoxyethylene 20 oleyl ether acrylate (O20AC)), and introduced it into a system composed of acrylamide (AAm), sodium chloride (NaCl) and water to prepare the P(AAm-O20AC)/NaCl hydrogel with high strength, outstanding toughness, and excellent good conductivity. Notably, the O20AC possess both crosslinker properties, which not only provide hydrogels with a stable chemical cross-linking network, but also its long hydrophobic chain act as physical crosslinking points to give the hydrogel a new pathway of energy dissipation.Depending on the O20AC crosslinker, the P(AAm-O20AC)/NaCl hydrogels exhibit good strength (tensile stress 714.46 kPa), toughness (elongation at break 3005 %), and fatigue resistance (overlap of hysteresis curves in repeated loading–unloading cycles). Besides, the existence of sodium ions and chloride ions also provides the hydrogel with highly sensitive deformation-dependent conductivity (GF is 5.8 in the 400 %-500 % strain range). Collectively, the strategy of preparing chemical and physical double-crosslinked hydrogels by amphiphilic O20AC will provide a bran-new insights for the development of hydrogel sensing materials.

New Estimates of Nitrogen Fixation on Early Earth.

Fixed nitrogen species generated by the early Earth's atmosphere are thought to be critical to the emergence of life and the sustenance of early metabolisms. A previous study estimated nitrogen fixation in the Hadean Earth's N2/CO2-dominated atmosphere; however, that previous study only considered a limited chemical network that produces NOx species (i.e., no HCN formation) via the thermochemical dissociation of N2 and CO2 in lightning flashes, followed by photochemistry. Here, we present an updated model of nitrogen fixation on Hadean Earth. We use the Chemical Equilibrium with Applications (CEA) thermochemical model to estimate lightning-induced NO and HCN formation and an updated version of KINETICS, the 1-D Caltech/JPL photochemical model, to assess the photochemical production of fixed nitrogen species that rain out into the Earth's early ocean. Our updated photochemical model contains hydrocarbon and nitrile chemistry, and we use a Geant4 simulation platform to consider nitrogen fixation stimulated by solar energetic particle deposition throughout the atmosphere. We study the impact of a novel reaction pathway for generating HCN via HCN2, inspired by the experimental results which suggest that reactions with CH radicals (from CH4 photolysis) may facilitate the incorporation of N into the molecular structure of aerosols. When the HCN2 reactions are added, we find that the HCN rainout rate rises by a factor of five in our 1-bar case and is about the same in our 2- and 12-bar cases. Finally, we estimate the equilibrium concentration of fixed nitrogen species under a kinetic steady state in the Hadean ocean, considering loss by hydrothermal vent circulation, photoreduction, and hydrolysis. These results inform our understanding of environments that may have been relevant to the formation of life on Earth, as well as processes that could lead to the emergence of life elsewhere in the universe.

Long-term source apportionment of PM2.5 across the contiguous United States (2000-2019) using a multilinear engine model

Identifying PM2.5 sources is crucial for effective air quality management and public health. This research used the Multilinear Engine (ME-2) model to analyze PM2.5 from 515 EPA Chemical Speciation Network (CSN) and Interagency Monitoring of Protected Visual Environments (IMPROVE) sites across the U.S. from 2000 to 2019. The U.S. was divided into nine regions for detailed analysis. A total of seven source types (tracers) were resolved across the country: (1) Soil/Dust (Si, Al, Ca and Fe); (2) Vehicle emissions (EC, OC, Cu and Zn); (3) Biomass/wood burning (K); (4) Heavy oil/coal combustion (Ni, V, Cl and As); (5) Secondary sulfate (SO42-); (6) Secondary nitrate (NO3-) and (7) Sea salt (Mg, Na, Cl and SO42-). Furthermore, we extracted and calculated secondary organic aerosols (SOA) based on the secondary sulfate and nitrate factors. Notably, significant reductions in secondary sulfate, nitrate, and heavy oil/coal combustion emissions reflect recent cuts in fossil-fueled power sector emissions. A decline in SOA suggests effective mitigation of their formation conditions or precursors. Despite these improvements, vehicle emissions and biomass burning show no significant decrease, highlighting the need for focused control on these persistent pollution sources for future air quality management.

Unlocking the functional potential of Nitraria schoberi extracts using chemical fingerprinting, biological efficiencies, in silico and network pharmacological approaches

The objective of this investigation was to assess the cytotoxic, antioxidant, and enzyme inhibitory properties of the aerial portions of Nitraria schoberi (family Nitrariaceae). Extracts were prepared by sequential maceration in hexane, dichloromethane (DCM), ethyl acetate and methanol (MeOH) and water extract was prepared by infusion. DCM and MeOH extracts accumulated the highest amount of metabolites where the former was characterized by the presence of octadecyl ferulate (16.96 mg/100 mg), methoxyluteolin (8.48 mg/100 mg) and isorhamnetin (5.35 mg/100 mg) and the alkaloids perlolyrine (5.03 mg/100 mg), (+)-5a-hydroxylemannine (2.43 mg/100 mg) and matrine (2.40 mg/100 mg). Results of antioxidant activity showed that the MeOH extract exerted the highest anti-2,2-diphenyl-1-picrylhydrazyl (DPPH) (28.13 mg Trolox equivalent (TE)/g) and Fe+++ (54.38 mg TE/g) reducing capacity. All extracts exerted significant acetylcholinesterase (3.71–4.37 mg galantamine equivalent (GALAE)/g) and tyrosinase (86.40–109.63 mg kojic acid equivalent (KAE)/g) inhibition activity. The best α-amylase and α-glucosidase inhibition was exhibited by the DCM (0.81 mmol ACAE/g) and MeOH (1.34 mmol ACAE/g) extracts respectively. The highest cytotoxic effect was exerted against the breast adenocarcinoma (MDA-MB-231) (IC50: 26.71 μg/mL) and cervix adenocarcinoma (HELA) (IC50: 29.10 μg/mL) cell lines by the MeOH and DCM extracts respectively. In addition, we performed molecular docking analysis to understand the interaction between chemical components and tested enzymes and cellular targets for cancer. Chrysoeriol-3-O-rutinoside bound strongly to AChE. Furthermore, a good interaction between vascular endothelial growth factor receptor 2 (VEGFR2) and isovitexin-2″-O-arabinoside was observed. We employed network pharmacology to understand the role of N. schoberi's compounds in neurological disorder like Alzheimer and multiple sclerosis. Therefore, the results of this study indicate that N. schoberi may serve as a viable starting point for the future creation of phytopharmaceuticals that target particular diseases associated with oxidative stress, such as diabetes and certain neurological disorders.

Quasi-equilibrium chemical evolution in starless cores

ABSTRACT The chemistry of H2O, CO, and other small molecular species in an isolated pre-stellar core, L1544, has been assessed in the context of a comprehensive gas-grain chemical model, coupled to an empirically constrained physical/dynamical model. Our main findings are (i) that the chemical network remains in near equilibrium as the core evolves towards star formation and the molecular abundances change in response to the evolving physical conditions. The gas-phase abundances at any time can be calculated accurately with equilibrium chemistry, and the concept of chemical clocks is meaningless in molecular clouds with similar conditions and dynamical time-scales, and (ii) A comparison of the results of complex and simple chemical networks indicates that the abundances of the dominant oxygen and carbon species, H2O, CO, C, and C+ are reasonably approximated by simple networks. In chemical equilibrium, the time-dependent differential terms vanish, and a simple network reduces to a few algebraic equations. This allows rapid calculation of the abundances most responsible for spectral line radiative cooling in molecular clouds with long dynamical time-scales. The dust ice mantles are highly structured and the ice layers retain a memory of the gas-phase abundances at the time of their deposition. A complex (gas-phase and gas-grain) chemical structure therefore exists, with cosmic-ray induced processes dominating in the inner regions. The inferred H2O abundance profiles for L1544 require that the outer parts of the core and also any medium exterior to the core are essentially transparent to the interstellar radiation field.

Automated Chemical Reaction Network Generation and Its Application to Exoplanet Atmospheres

With the advent of JWST and the spectroscopic characterization of exoplanet atmospheres in unprecedented detail, there is a demand for more complete pictures of chemical and photochemical reactions and their impacts on atmospheric composition. Traditionally, building reaction networks for (exo)planetary atmospheres involves manually tracking relevant species and reactions, a time-consuming and error-prone process. This approach’s applicability is also often limited to specific conditions, making it less versatile for different planetary types (i.e., photochemical networks for Jupiters may not be directly applicable to water-rich exoplanets). We introduce an automated approach using a computer-aided chemical reaction network generator, combined with a 1D photochemical kinetic-transport model, offering significant advantages. This approach automatically selects reaction rates through a rate-based iterative algorithm and multiple refinement steps, enhancing model reliability. Also, this approach allows for the efficient simulation of diverse chemical environments, from hydrogen to water, carbon dioxide, and nitrogen-dominated atmospheres. Using WASP-39b and WASP-80b as examples, we demonstrate our approach’s effectiveness, showing good agreement with recent JWST data. Our WASP-39b model aligns with prior studies and JWST observations, capturing photochemically produced sulfur dioxide. The WASP-80b model reveals an atmosphere influenced by deep-interior thermochemistry and vertical mixing, consistent with JWST NIRCam observations. Furthermore, our model identifies a novel initial step for the N2–NH3–HCN pathway that enhances the efficiency of the conversion in high-temperature/high-pressure environments. This automated chemical network generation offers a novel, efficient, and precise framework for studying exoplanetary atmospheres, marking a significant advancement over traditional modeling techniques.

Chemical fault diagnosis network based on single domain generalization

Recent developments in fault diagnosis have leveraged domain generalization to address the issue of domain shift. Most existing methods focus on learning domain-invariant representations from multiple source domains. However, collecting valuable fault samples from varying operational conditions is challenging, and it is common for available data to originate from a single operational condition. Thus, this paper introduces a Multi-scale generative and adversarial Metric networks (MGAMN) for Chemical Process Fault Diagnosis. To enhance model generalization, a domain generation module was developed to create augmented domains with significant distributional differences from the source domain. The diagnostic task module then extracts domain-invariant features from both the source and augmented domains. A multi-scale generation strategy is established, utilizing multi-scale deep separable convolutions (Dsc) to ensure that the generated samples contain rich state information. Additionally, an adversarial training and metric learning strategy is designed to learn generalized features capable of resisting unknown domain shifts. Extensive diagnostic experiments on the non-isothermal continuous stirred tank reactor (CSTR) and the Tennessee Eastman Process (TEP) chemical datasets validate the effectiveness of the proposed method. Moreover, ablation studies confirm the effectiveness of the proposed modular strategy, demonstrating significant potential for practical applications.

Temporal and fertilizer-dependent dynamics of soil bacterial communities in buckwheat fields under long-term management

This study integrated bacterial community and soil chemicals to characterize the soil ecosystem in an open upland field managed by six controlled fertilizer programs using the minimum amount of pesticides. Amplicon sequencing the 16S rRNA gene revealed that inorganic nitrogen fertilizer and compost altered the diversity and structure of the soil bacterial community throughout buckwheat (Fagopyrum esculentum Moench ‘Hitachiakisoba’) cultivation. The bacterial community comprised three clusters that contained bacteria that are prevalent in soils fertilized with nitrogen (cluster 1, 340 taxa), without nitrogen and compost (cluster 2, 234 taxa), and with compost-fertilized (cluster 3, 296 taxa). Cluster 2 contained more taxa in Actinobacteriota and less in Acidobacteriota, and cluster 3 contained more taxa in Gemmatimonadota compared with the other clusters. The most frequent taxa in cluster 1 were within the Chloroflexi phylum. The bacterial community structure correlated with soil chemical properties including pH, total organic carbon, SO42−, soluble Ca2+. A co-occurrence network of bacterial taxa and chemicals identified key bacterial groups comprising the center of a community network that determined topology and dynamics of the network. Temporal dynamics of the bacterial community structure indicated that Burkholderiales were associated with buckwheat ripening, indicating plant-bacteria interaction in the ecosystem.

Parsec-scale cosmic-ray ionisation rate in Orion

Cosmic rays are a key component of the interstellar medium because they regulate the dynamics and chemical processes in the densest and coldest regions of molecular clouds. Still, the cosmic-ray ionisation rate of H$_2$ (crir ) is one of the most debated parameters characterising molecular clouds because of the uncertainties in the adopted chemical networks and analysis techniques. This work aims to homogeneously estimate the crir at parsec scales towards the Orion Molecular Clouds OMC-2 and OMC-3. We explore the change in crir across a whole star-forming region by probing a range of column densities taht has never been explored before. The significant increase in statistics obtained by studying an entire region allows us to place stronger constraints on the range of crir values and exploit its connection with the physical properties of the interstellar medium. The most recent crir estimates are based on o$-$H$_2$D$^+$, which is a direct product of the interaction between cosmic rays and H$_2$ in cold clouds. Since observations of o$-$H$_2$D$^+$ are challenging, we proxy its abundance through CO depletion by employing C18O (2$-$1) observations towards OMC-2 and OMC-3, taking advantage of the existing correlation between the two parameters. Using additional observations of HCO$^+$ (1$-$0) and DCO$^+$ (3$-$2), we determine the deuteration fraction, and we finally derive the map of crir in these two regions. The C18O depletion correlates with both the total column density of H$_2$ and the N$_2$H$^+$ emission across OMC-2 and OMC-3. The obtained depletion factors and deuteration fractions are consistent with previous values obtained in low- and high-mass star-forming regions. These two parameters additionally show a positive correlation in the coldest fields of our maps. We derive cosmic-ray ionisation rates of $ $ s$^ $. These values agree well with previous estimates based on o$-$H$_2$D$^+$ observations. The crir also shows a functional dependence on the column density of H$_2$ across a full order of magnitude ($ $ cm$^ $). The estimated values of crir decrease overall for increasing $N( H_2 $), as predicted by theoretical models. The results delivered by our approach are comparable with theoretical predictions and previous independent studies. This confirms the robustness of the analytical framework and promotes CO depletion as a viable proxy of o$-$H$_2$D$^+$. We also explore the main limitations of the method by varying the physical size of the gas crossed by the cosmic rays (i.e. the path length). By employing a path length obtained from low-resolution observations, we recover values of the crir that are well below any existing theoretical and observational prediction. This discrepancy highlights the need for interferometric observations in order to reliably constrain the crir at parsec scales as well.

Kinetic models of HMX decomposition via chemical reaction neural network

1,3,5,7-Tetranitro-1,3,5,7-tetrazocane (HMX) is commonly used in solid propellants and explosives as energetic materials. The study of its kinetics and decomposition mechanism is of great significance to its application in the aerospace industries. This work investigates the thermal decomposition of HMX based on the combined thermogravimetric (TG) measurements and chemical reaction neural network (CRNN). Two compact kinetic models for HMX are introduced, with one consisting of four substances and a single global reaction (4–1 model) and the other consisting of four substances and four reactions (4–4 model). In the 4–1 model, the calculated activation energy is 328.44 kJ·mol−1, which agrees with the experimental value. As for the 4–4 model, the substances and reactions are assigned based on a skeleton mechanism involving reactions of N-N and C-N bond cleavage and HONO elimination. Moreover, the catalytic effects of TiO2 and Al2O3 on HMX are well simulated using the aforementioned kinetic models. The CRNN models can reproduce the peak temperature with a reduced activation energy, but the initial decomposition temperature is overestimated owing to the complex nature of catalytic impact. This work presents the application of the CRNN model to obtain a decomposition mechanism of HMX, highlighting its efficacy in accurately capturing the thermal decomposition behavior. The potential extension of CRNN to kinetic modeling of other energetic materials is anticipated in future studies.

Efficient Generation of Conformer Ensembles Using Internal Coordinates and a Generative Directional Graph Convolution Neural Network.

We present a neural-network-based high-throughput molecular conformer-generation algorithm. A chemical graph-convolutional network is trained to predict low-energy conformers in internal coordinate representation (bond lengths, bond, and torsion angles), starting from two-dimensional (2D) chemical topology. Generative neural network (NN) architecture performs denoising from torsion space, producing conformer ensembles with populations that are well correlated with torsion energy profiles. Short force-field-based energy minimization is applied to refine final conformers. All computation-intensive stages of the algorithm are GPU-optimized. The procedure (termed GINGER) is benchmarked on a commonly used test set of bioactive three-dimensional (3D) conformers from the PDB. We demonstrate highly competitive results in conformer recovery and throughput rates suitable for giga-scale compound library processing. A web server that allows interactive conformer ensemble generation by GINGER and their viewing is made freely available at https://www.molsoft.com/gingerdemo.html.

Ice chemistry modeling of active phase comets: Hale–Bopp

We present a chemical kinetics model of the solid-phase chemical evolution of a comet, beginning with a long period of cold-storage in the Oort Cloud, followed by five orbits that bring the comet close to the Sun. Current orbital parameters for Hale–Bopp are used for all orbits. The chemical model is based on an earlier treatment (Garrod 2019) that considered only the cold-storage phase, and which was based on the interstellar ice chemical kinetics model MAGICKAL. The comet is treated as 25 chemically distinct layers, whose thicknesses increase with depth; each layer has a dust component, and the chemical network includes a further 200 atomic/molecular species. The new model includes several key updates to the previous treatment: (i) Time- and depth-dependent temperature profiles based on heat transfer according to heliocentric distance; (ii) a rigorous treatment of back-diffusion (random walk) for species capable of diffusing through the thick bulk-ice layers; (iii) adoption of recent improvements in the kinetic treatment of nondiffusive chemical reaction rates in the ice and on the ice surface. Starting from an initially simple ice composition, interstellar UV photons drive a rapid chemistry in the upper micron of material, but diminished by absorption of the UV by the dust component. Galactic cosmic rays (GCRs) drive a much slower chemistry in the deeper ices over the long cold-storage period, to depths on the order of 10 m. The first solar approach drives off the upper layers of ice material via thermal desorption and/or dissociation (the model does not include a treatment for the more complex cometary outbursts), bringing closer to the surface the deeper material that previously underwent long-term processing by GCRs. Subsequent orbits are more uniform in their chemical behavior after the upper layers are lost. Loss of molecular material leads to concentration of the dust in the upper layers, with a large dust fraction extending to depths on the order of 10 cm after all orbits are complete. Substantial quantities of complex organic molecules (COMs) are formed in the upper 10 m during the cold storage phase, with some of this material released during solar approach; however, their abundances with respect to water appear too low to account for the observed gas-phase values for comet Hale–Bopp, indicating that the majority of complex molecular material observed, at least in comet Hale–Bopp, is an inheritance of primordial material.

RETRACTED: On Vertex PI index of certain perfect graphs, lexicographic product of graphs, and some chemical networks

RETRACTED: On Vertex PI index of certain perfect graphs, lexicographic product of graphs, and some chemical networks

A novel data-driven reduced order modelling methodology for simulation of humid blowout in wet combustion applications

Computationally inexpensive reduced order models such as Chemical Reactor Networks (CRN) are encouraging tools to obtain fast numerical solutions. However, the accuracy of such models is usually compromised compared to experiments or high-fidelity numerical simulations. Therefore, continued improvement of such models is necessary to ensure reasonable accuracy and fill the need for computationally inexpensive tools. To that end, inputs from computational fluid dynamics simulations have been used to build CRNs in the last 25 years. This can be further improved by the application of data-driven techniques. The present work uses a novel data-driven analysis based on high-fidelity simulation results where the high-dimensional data is first reduced to a low-dimensional manifold and is then clustered into chemically coherent regions. These results along with the high-fidelity simulation results are used to construct a CRN for wet combustion simulations. Wet combustion is a novel clean combustion technique, where an increase in the level of steam dilution distributes the flame, with a risk for flame extinction. This phenomenon, i.e., humid blowout (HBO), was modelled using the above-mentioned CRN. The HBO predicted by the CRN was also tested for thermal power, equivalence ratio, and fuel conditions different from its design point. The error in HBO prediction was quantified by comparing the steam-dilution level at HBO obtained using CRN and that obtained experimentally. The CRN predicted the HBO with an error magnitude less than 5% when the thermal power was unchanged. The maximum error in all tested conditions was 16.65%. Furthermore, a sensitivity analysis revealed that the inclusion of hot gas recirculation through the central recirculation zone, quantified by the mass flow split between the post-flame region and the CRZ, is important in the prediction of HBO, although its accuracy is inconsequential.

High-frequency optimally windowed chirp rheometry for rapidly evolving viscoelastic materials: Application to a crosslinking thermoset

Knowledge of the evolution in the mechanical properties of a curing polymer matrix is of great importance in composite parts or structure fabrication. Conventional rheometry, based on small amplitude oscillatory shear, is limited by long interrogation times. In rapidly evolving materials, time sweeps can provide a meaningful measurement albeit at a single frequency. To overcome this constraint, we utilize a combined frequency- and amplitude-modulated chirped strain waveform in conjunction with a homemade sliding plate piezo-operated rheometer (PZR) and a dual-head commercial rotational rheometer (Anton Paar MCR 702) to probe the linear viscoelasticity of these time-evolving materials. The direct controllability of the PZR, resulting from the absence of any kind of firmware and the microsecond actuator-sensor response renders this device ideal for exploring the advantages of this technique. The high frequency capability allows us to extend the upper limits of the accessible linear viscoelastic spectrum and, most importantly, to shorten the length of the interrogating strain signal (OWCh-PZR) to subsecond scales, while retaining a high time-bandwidth product. This short duration ensures that the mutation number (NMu) is kept sufficiently low, even in fast-curing resins. The method is validated via calibration tests in both instruments, and the corresponding limitations are discussed. As a proof of concept, the technique is applied to a curing vinylester resin. The linear viscoelastic (LVE) spectrum is assessed every 20 s to monitor the rapid evolution in the time and frequency dependence of the complex modulus. Comparison of the chirp implementation, based on parameters such as duration of the experiment, sampling frequency, and frequency range, in a commercial rotational rheometer with the PZR provides further information on the applicability of this technique and its limitations. Finally, FTIR spectroscopy is utilized to gain insights into the evolution of the chemical network, and the gap dependence of the evolving material properties in these heterogeneous systems is also investigated.