Decomposition Of Mixture Research Articles

Time series forecasting of multiphase microstructure evolution using deep learning

Microstructure evolution, which plays a critical role in determining materials properties, is commonly simulated by the high-fidelity but computationally expensive phase-field method. To address this, we approximate microstructure evolution as a time series forecasting problem within the domain of deep learning. Our approach involves implementing a cost-effective surrogate model that accurately predicts the spatiotemporal evolution of microstructures, taking an example of spinodal decomposition in binary and ternary mixtures. Our surrogate model combines a convolutional autoencoder to reduce the dimensional representation of these microstructures with convolutional recurrent neural networks to forecast their temporal evolution. We use different variants of recurrent neural networks to compare their efficacy in developing surrogate models for phase-field predictions. On average, our deep learning framework demonstrates excellent accuracy and speedup relative to the “ground truth” phase-field simulations. We use quantitative measures to demonstrate how surrogate model predictions can effectively replace the phase-field timesteps without compromising accuracy in predicting the long-term evolution trajectory. Additionally, by emulating a transfer learning approach, our framework performs satisfactorily in predicting new microstructures resulting from alloy composition and physics unknown to the model. Therefore, our approach offers a useful data-driven alternative and accelerator to the materials microstructure simulation workflow.

Early time wetting kinetics in surface-directed spinodal decomposition for off-critical quenches: A molecular dynamics study.

We present results from the molecular dynamics simulation of surface-directed spinodal decomposition in binary fluid mixtures (A + B) with off-critical compositions. The aim is to elucidate the role of composition ratio in the early time wetting kinetics under the influence of long-range surface potential. In our simulations, the attractive part of surface potential varies as V(z) = -ϵa/zn, with ϵa being the surface-potential strength. The surface prefers the "A" species to form the wetting layer. Its thickness [R1(t)] for the majority wetting (number of A-type particles [NA] > number of B-type particles [NB]) grows as a power-law with an exponent of 1/(n + 2). This is consistent with the early time kinetics in the form of potential-dependent growth present in the Puri-Binder model. However, for minority wetting (NA < NB), the growth exponent in R1(t) is less than 1/(n + 2). Furthermore, on decreasing the field strength ϵa, we recover 1/(n + 2) for a minority wetting case. We provide phenomenological arguments to explain the early time wetting kinetics for both cases.

Evaluation of the reactivity of co-combustion of wheat straw and waste rubber thermolysis char

The co-combustion of wheat straw (WS) and waste rubber thermolysis char (WRTC) was examined with the perspective of utilizing WRTC as a high-calorific value fuel addition to agricultural biomass. The study investigated the effect of different proportions of WS-WRTC blends (10/15/25/49 wt% WRTC) and a maximum of 4 wt% binders, including potato starch and calcium oxide, on the reactivity of co-combustion and the distribution of products during the process. Thermogravimetric analysis with Fourier Transform Infrared Spectroscopy (TGA-FTIR) analyses was employed at a temperature range of 25–1000 °C with a heating rate of 10 °C/min. The kinetics of co-combustion were analyzed using the Fraser-Suzuki (FS) deconvolution method and a model-based kinetic modeling. The results indicate that a 10% addition of WRTC char reduces the intensity and rate of combustion and prolongs the combustion time. It was observed that the optimal addition to the blend is 25% WRCT, and it resulting in a reduction in activation energy and combustion indexes. However, it remains with no significant impact on process stability and efficiency. Kinetics of the decomposition of the WRTC25 mixture modeled by the deconvolution method indicates a 5-step reaction course, which is a combination of the characteristic combustion stages of both fuels. The main denoted functional group observed in the FTIR absorption spectra were C=O, C-O, O-H, and C-H related to the release of CO2, CO, H2O, CH4, aromatic compounds, and hydrocarbons.

Analysis of the behavior of a gas-generating system under runaway conditions in a closed vessel

In the event of a runaway reaction, emergency relief systems (ERS) act as the last line of defense against the vessel explosion. To date, ERS design for scenarios involving the runaway of chemical mixtures that generate gaseous reaction products remains very challenging as it requires the prediction of the gas generation rate under runaway conditions at large scale. Current methodologies for the assessment of the gas generation rate are based on pseudo-adiabatic calorimetric experiments at laboratory scale in which the temperature and pressure profiles resulting from the runaway are used to assess the gas generation rate using the ideal gas law. The gas generation rate needs to be further corrected for the thermal inertia (φ-factor), of the calorimetric cell used (φ>1) to predict large scale behavior (φ≈1). The work described in this paper focuses on evaluating the capability of this approach to accurately assess the gas generation rate. It uses a dynamic simulator tool based on rigorous thermodynamics to calculate the temperature, pressure, and the composition of each component in a closed vessel containing a gas generating reactive mixture (decomposition of 20 % w/w di-tert butyl peroxide in toluene) under runaway conditions over a range of initial vessel fill levels. The results were analyzed to develop a better understanding of the phenomena that govern the thermal behavior of the mixture, the overall pressurization of the vessel, rate of generation of the gaseous products and its distribution in the liquid and vapor phase of the vessel as the runaway reaction occurs. The simulated pressure, temperature and phase composition data rigorously calculated by the simulator were used to assess and highlight the limitations of using the temperature and pressure in a closed vessel and the ideal gas law to evaluate the gas generation rate. The simulations were also used to evaluate the validity of φ-factor correction methods currently available in the literature.

Thermal stability of the DPT reaction system synthesized by acetic anhydride method

The adiabatic decomposition of a reaction mixture prepared in DPT using the acetic anhydride method was analyzed using an Accelerating Rate Calorimeter (ARC) to determine the thermal decomposition characteristics and kinetic parameters of the reaction solution at different reaction times. The adiabatic decomposition kinetics were modeled using TSS software, and the corresponding kinetic parameters were derived. Subsequently, a numerical simulation was employed to predict the critical ambient temperature of the reaction liquid in the reactor in case of cooling failure. The results indicated that the CT value was lowest at a feeding time of 10 minutes, highest at a holding time of 15 minutes, and closest to the cooling temperature of the reactor at a feeding time of 30 minutes.

Using a novel multiple-source indicator to investigate the effect of scale format on careless and insufficient effort responding in a large-scale survey experiment

Common indicator-based approaches to identifying careless and insufficient effort responding (C/IER) in survey data scan response vectors or timing data for aberrances, such as patterns signaling straight lining, multivariate outliers, or signals that respondents rushed through the administered items. Each of these approaches is susceptible to unique types of misidentifications. We developed a C/IER indicator that requires agreement on C/IER identification from multiple behavioral sources, thereby alleviating the effect of each source’s standalone C/IER misidentifications and increasing the robustness of C/IER identification. To this end, we combined a response-pattern-based multiple-hurdle approach with a recently developed screen-time-based mixture decomposition approach. In an application of the proposed multiple-source indicator to PISA 2022 field trial data we (a) showcase how the indicator hedges against (presumed) C/IER overidentification of its constituting components, (b) replicate associations with commonly reported external correlates of C/IER, namely agreement with self-reported effort and C/IER position effects, and (c) employ the indicator to study the effects of changes of scale characteristics on C/IER occurrence. To this end, we leverage a large-scale survey experiment implemented in the PISA 2022 field trial and investigate the effects of using frequency instead of agreement scales as well as approximate instead of abstract frequency scale labels. We conclude that neither scale format manipulation has the potential to curb C/IER occurrence.

Thermal Decomposition of Pure Waste and Binary Mixtures

In this study, the thermal decomposition of pure organic residues - coffee grounds, sugarcane bagasse and orange juice residues - and their binary mixtures with coffee grounds were analyzed. The results of thermogravimetric analyses at 10 °C/min under an oxidizing atmosphere showed that sugarcane bagasse and coffee grounds had individually higher reaction rates in the devolatilization zone, while orange juice residues burned more quickly in the combustion zone. There were significant mass interactions between the coffee grounds and orange juice residues in the binary mixtures, which led to higher reaction rates in the combustion of the mixture than in the pure residues. In the mixture of coffee grounds and sugarcane bagasse, there were no significant mass interactions. These encouraging findings can optimize the generation of bioenergy from these wastes and their mixtures, therefore, one must understand the different interactions during thermal decomposition to then develop sustainable strategies for utilizing biomass.

Experimental investigation on the combustion characteristics of electrolyte jets containing flame retardant tris (2-chloroethyl) phosphate

Abstract It is critical to well understand the combustion characteristics of the electrolytes inside lithium-ion batteries for safety concerns, particularly the electrolyte jet flames after thermal runaway. An electrolyte jet fire setup is developed in this study to investigate the combustion characteristics of electrolyte jets with the flame-retardant additive tris (2-chloroethyl) phosphate (TCEP) under high-temperature circumstances. Jet and ignition delay times and flammability are defined to characterize the flame-retardant effects. The fundamental parameters of self-extinguishing time and propagation rate are also measured for a comprehensive comparison. The experimental results show that the propagation of electrolyte flame at ambient temperature can be entirely stopped with 40 wt % of TCEP additives and 50 wt % can make the electrolyte nonflammable. Owing to the high boiling temperature and vaporization enthalpy of TCEP, more heat is required for the decomposition of electrolytes and TCEP mixtures, resulting in lower decomposition reaction rates and heat release rates. Thus, both the jet delay times and the ignition delay times significantly increase with the TCEP additives. Moreover, analyses on the spectrum of electrolyte jet flame reveal that the suppressing effects of TCEP on the combustion of electrolyte jets are operated by scavenging the OH radical and heat release.

Near-critical dark opalescence in out-of-equilibrium SF6

The first-order phase transition between the liquid and gaseous phases ends at a critical point. Critical opalescence occurs at this singularity. Discovered in 1822, it is known to be driven by diverging fluctuations in the density. During the past two decades, boundaries between the gas-like and liquid-like regimes have been theoretically proposed and experimentally explored. Here, we show that fast cooling of near-critical sulfur hexafluoride (SF6), in presence of Earth’s gravity, favors dark opalescence, where visible photons are not merely scattered, but also absorbed. When the isochore fluid is quenched across the critical point, its optical transmittance drops by more than three orders of magnitude in the whole visible range, a feature which does not occur during slow cooling. We show that transmittance shows a dip at 2eV near the critical point, and the system can host excitons with binding energies ranging from 0.5 to 4 eV. The spinodal decomposition of the liquid-gas mixture, by inducing a periodical modulation of the fluid density, can provide a scenario to explain the emergence of this platform for coupling between light and matter. The possible formation of excitons and polaritons points to the irruption of quantum effects in a quintessentially classical context.

Importance Sampling BRDF Derivatives

We propose a set of techniques to efficiently importance sample the derivatives of a wide range of Bidirectional Reflectance Distribution Function (BRDF) models. In differentiable rendering, BRDFs are replaced by their differential BRDF counterparts, which are real-valued and can have negative values. This leads to a new source of variance arising from their change in sign. Real-valued functions cannot be perfectly importance sampled by a positive-valued PDF, and the direct application of BRDF sampling leads to high variance. Previous attempts at antithetic sampling only addressed the derivative with the roughness parameter of isotropic microfacet BRDFs. Our work generalizes BRDF derivative sampling to anisotropic microfacet models, mixture BRDFs, Oren-Nayar, Hanrahan-Krueger, among other analytic BRDFs. Our method first decomposes the real-valued differential BRDF into a sum of single-signed functions, eliminating variance from a change in sign. Next, we importance sample each of the resulting single-signed functions separately. The first decomposition, positivization, partitions the real-valued function based on its sign, and is effective at variance reduction when applicable. However, it requires analytic knowledge of the roots of the differential BRDF, and for it to be analytically integrable too. Our key insight is that the single-signed functions can have overlapping support, which significantly broadens the ways we can decompose a real-valued function. Our product and mixture decompositions exploit this property, and they allow us to support several BRDF derivatives that positivization could not handle. For a wide variety of BRDF derivatives, our method significantly reduces the variance (up to 58× in some cases) at equal computation cost and enables better recovery of spatially varying textures through gradient-descent-based inverse rendering.

Sequential Treatment by Ozonation and Biodegradation of Pulp and Paper Industry Wastewater to Eliminate Organic Contaminants.

In this research, the decomposition of toxic organics from pulp and paper mill effluent by the sequential application of ozonation and biodegradation was studied. Ozonation, as a pre-treatment, was executed to transform the initial pollutants into less toxic compounds (such as organic acids of low molecular weights). Biodegradation was executed during three days with acclimated microorganisms that were able to complete the decomposition of the initial organic mixture (raw wastewater) and to achieve a higher degree of mineralization (85-90%). Experiments were performed under three different conditions: (a) only ozonation of the initial contaminants, (b) only biodegradation of residual water without previous treatment by ozone and (c) ozonation followed by biodegradation performed by acclimated microorganisms. In the case of 72 h of biodegradation, the mineralization efficiency reached 85% and 89% after 30 and 60 min of ozonation, respectively. The no significant difference in this parameter coincided with the calculated generalized microorganisms' consortia specific growing rate μmax that was reduced from 2.08 × 10-3 h-1 to 6.05 × 10-4 h-1 when the ozonation time was longer. The identification of the organics composition by gas chromatography with mass detector (GC-MS) before and after treatments confirmed that the proposed combined process served as a more efficient alternative to secondary and tertiary treatments (mineralization degree between 60 and 80% in average) of the paper industry wastewater.

Moss removal facilitates decomposition and net nitrogen loss of monospecific and mixed-species litter in a boreal peatland

AbstractLitter decomposition plays an important role in biogeochemical cycling in boreal peatlands, where mosses, especially Sphagnum species, are a determinant. In recent decades, these peatlands have experienced a decline in moss cover due to abrupt climate warming and atmospheric nitrogen (N) deposition. To reveal the effect of the reduction in moss cover on litter decomposition, we adopted a field living moss removal experiment (with the senesced tissues remaining) in a Sphagnum-dominated boreal peatland, and investigated litter mass loss and net N loss of three deciduous woody species decomposing in monocultures and mixtures over 3 years. Based on the observed and predicted mass loss and net N loss of litter mixtures, we divided litter mixing effects into additive (no significant difference), synergistic (observed value greater than predicted value), and antagonistic (observed value lower than predicted value) effects. Across 3 years of decomposition, moss removal increased litter mass loss and net N loss, irrespective of single- or mixed-species compositions. Moss removal generally changed litter mixing effects on mass loss from antagonistic to additive effects in the initial 2 years, but from synergistic to additive effects after 3 years of decomposition. Regarding net N loss of litter mixtures, moss removal often resulted in a shift from additive to synergistic effects or from antagonistic to additive effects after 2 and 3 years of decomposition. Our observations suggest that the declines in living moss cover can accelerate litter decomposition and nutrient release, and highlight that living moss loss makes litter mixture decomposition predictable by reducing non-additive effects in boreal peatlands. Given the widespread occurrence of reduced moss cover in boreal peatlands, the mechanisms explaining living moss controls on litter decomposition and N cycling should receive significant attention in further studies.

A Novel Enhancement Approach Following MVMD and NMF Separation of Complex Snoring Signals.

Snoring is a prominent characteristic of sleep-disordered breathing, and its detection is critical for determining the severity of the upper airway obstruction and improving daily quality of life. Home snoring analysis is a highly invasive method, but it becomes challenging when a sleeping partner also snores, leading to distorted evaluations in such environments. In this article, we tackle the problem of complex snore signal separation of multiple snorers. This article introduces two audio-based methods that efficiently extract an individual's snoring signal, allowing for the analysis of sleep-breathing disorders in a normal sleeping environment without isolating individuals. In the first method, Principal Component Analysis (PCA) identifies the source components from the finite number of modes generated by the decomposition of the snoring mixture using Multivariate Variational Mode Decomposition (MVMD). The second method applies Blind Source Separation (BSS) based on Non-Negative Matrix Factorization (NMF) to separate the single-channel snoring mixture. Furthermore, the decomposed signals are tuned using the iterative enhancement algorithm to adequately match the source snoring signals. These methods were evaluated by simulating various real-time snoring recordings of 7 subjects (2 men, 2 women, and 3 children). The correlation coefficient between the source and its separated signal was computed to assess the separation results, exhibiting good performance of the methods used. The enhancement approach also demonstrated its efficiency by increasing the correlation over to 80% in both methods. The experimental results show that the proposed algorithms are effective and practical for separating mixed snoring signals.

Detritivores accelerate litter mixture decomposition effect via greater consumption of high quality litter.

To explore the mixing effect of litter decomposition and the role of detritivores, we conducted a laboratory-based microcosm experiment to study the influence of detritivores on litter mixture decomposition by using two litter species with contrasting quality, i.e., Cinnamomum camphora and Michelia × alba, and a detritivore (isopoda). After 100 days incubation, the decomposition rate of litter mixture was 52.1%, slower than that of M. alba (62.6%) and significantly faster than that of C. camphora (33.6%). The addition of isopods significantly increased litter decomposition rate, with C. camphora, M. alba, and the mixture increased by 14.4%, 20.1% and 22.1%, respectively. There was no significant mixing effect without isopods. Adding isopods significantly promoted the mixing effect of litter decomposition, with a value of the litter mixture decomposition effect of 8.6%. The detritivores increased litter decomposition rate and mixing effect through increasing consumption of litter with better quality.

Decomposition of litter mixtures induces non-additive effects on soil priming across a riparian land use gradient

While litter traits have been used for decades to predict decomposition rates through the Leaf Economics Spectrum (LES) acquisitive to conservative trait lens, litter trait and litter mixture effects on soil carbon (C) priming effects (PE) is less known. To assess whether the LES can predict soil C PE during the decomposition of litter mixtures from complex ecosystems, a 99-day incubation experiment was conducted. The experiment involved soil and leaf litter from three Canadian riparian land uses (grasslands, deciduous agroforests, and coniferous agroforests), incubated as single plant species, and as plant species mixtures in their natural proportions. Natural abundance δ13C-isotopic analysis was used to assess the proportion of CO2 derived from leaf litter versus soil C mineralization at initial (0–21 days, stage I) and later (22–99 days, stage II) stages of decomposition. Single species positions on the LES mirrored litter-C mineralization dynamics, yet PE emissions remained harder to predict with LES traits. Nonetheless, higher PE emissions relative to litter-C mineralization were observed with litter from LES species expressing conservative traits compared to LES species expressing acquisitive traits. Leaf litter mixtures from both agroforests, expressing high leaf litter trait functional dissimilarity, resulted in synergistic, non-additive effects on soil PE. These findings contribute to our understanding of the effects of leaf litter functional traits and trait mixing on soil PE, highlighting that mixed-species litter decomposition can lead to non-additive effects on soil C dynamics in forested riparian ecosystems.

Some features of trichlorobiphenyls photolysis

The photolytic decomposition of the Trikhlorbifenil mixture, 2,4,5- and 2,4,6-trichlorobiphenyls under an influence of ultraviolet radiation in presence of tungsten oxide as a catalyst in a methanol medium was studied. A higher rate of photolysis of individual congeners of polychlorbiphenyls was established compared to the congeners of the Trikhlorbifenil mixture. The influence of structural features of polychlorarenes on a depth of photodecomposition and a routes of formation of photodestruction products was considered.

On the trade-off between ranking effectiveness and fairness

This paper addresses the problem of maximizing the effectiveness of the ranking produced by information retrieval or recommender systems and at the same time maximizing two fairnesses, that of the group and that of the individual. The context of this paper is therefore that of access to information carried out by users, who aim to satisfy their own information needs, to documents produced by authors and curators who aim to be exposed in a fair manner, i.e. without discriminating between groups nor individuals. The paper describes a general method based on the spectral decomposition of mixtures of symmetric matrices, each of which represents a variable to be maximized, and experiments conducted with the use of a test collection. The method described in this paper has explained if and how the trade-offs between effectiveness, group fairness and individual fairness manifest themselves. The experimental results show that maintaining an acceptable level of effectiveness and fairness at the same time is feasible and (b) the trade-offs exist but the order of magnitude of the variations depends on the measure of effectiveness used and therefore by what the user’s model of access to information is as well as on the fairness measures and therefore on how authors or editors should be exposed.

Synergistic effects and products yield analyses based on co-pyrolysis of poplar tree and rape stalks with polyethylene terephthalate and polypropylene

In this study, results from co-pyrolysis of poplar tree, rape stalks (biomass) and polyethylene terephthalate, polypropylene (plastics) are reported using thermogravimetric analyzer, Fourier transform infrared spectroscopy and Pyrolyzer-Gas chromatograph/Mass spectrometry. The individual samples, and their mixtures (in 1:1 mass mixture ratios) were investigated to evaluate their co-pyrolysis behavior. TGA results showed the decomposition of agroforestry residues and plastic mixtures having three main stages of moisture evaporation, volatilization, and solid decomposition. The volatilization stage was important in the process. FTIR results showed the presence of poplar tree and rape stalks promoted the pyrolysis of plastics, which also supported the results of TGA. The same absorption bands at the same wavenumbers, indicated the presence of similar chemical functional groups. Py-GC/MS results showed co-pyrolysis of agroforestry residues and PET promoted formation of aromatic and acid while with PP, it promoted hydrocarbons and alcohol at 700 °C.

Anisotropy in spinodal-like dynamics of unknown water at ice V–water interface

Experimentally demonstrating the existence of waters with local structures unlike that of common water is critical for understanding both the origin of the mysterious properties of water and liquid polymorphism in single component liquids. At the interfaces between water and ices Ih, III, and VI grown/melted under pressure, we previously discovered low- and high-density unknown waters, that are immiscible with the surrounding water. Here, we show, by in-situ optical microscopy, that an unknown water appears at the ice V–water interface via spinodal-like dynamics. The dewetting dynamics of the unknown water indicate that its characteristic velocity is ~ 90 m/s. The time evolution of the characteristic length of the spinodal-like undulation suggests that the dynamics may be described by a common model for spinodal decomposition of an immiscible liquid mixture. Spinodal-like dewetting dynamics of the unknown water transiently showed anisotropy, implying the property of a liquid crystal.

A GMDA clustering algorithm based on evidential reasoning architecture

The traditional clustering algorithm is difficult to deal with the identification and division of uncertain objects distributed in the overlapping region, and aimed at solving this problem, the Evidential Clustering based on General Mixture Decomposition Algorithm (GMDA-EC) is proposed. First, the belief classification of target cluster is carried out, and the sample category of target distribution overlapping region is extended. Then, on the basis of General Mixture Decomposition Algorithm (GMDA) clustering, the fusion model of evidence credibility and evidence relative entropy is constructed to generate the basic probability assignment of the target and achieve the belief division of the target. Finally, the performance of the algorithm is verified by the synthetic dataset and the measured dataset. The experimental results show that the algorithm can reflect the uncertainty of target clustering results more comprehensively than the traditional probabilistic partition clustering algorithm.