Random Chain Research Articles

Preparation and study of properties of sorbents based on the fibrous waste of “Bombyx mori” natural silk

The work presents information on obtaining hydrolyzed fibroin powder with high sorption properties under the influence of ultra-high frequency rays from the fibrous waste of the silk industry in an acidic environment, as well as information on its composition, structure, and sorption properties. During the research, it was found that the formation of hydrolyzed fibroin powder is caused by the hydrolysis of alpha- and random chains of fibroin. In differential scanning calorimetry analysis, it was shown that hydrolyzed fibroin is thermally structurally stable up to 210 °C and capable of sorption of gold and silver ions in large quantities. Using the samples obtained by treating the hydrolyzed fibroin powders in different medium solutions, cottonseed oil was whitened, and the peroxide content of the oil was reduced from 12 mmol/kg to 2.2 mmol/kg. By lowering it to this point, it is possible to increase the oil's shelf life.

The influence of functional groups on the pyrolysis of per- and polyfluoroalkyl substances

Thermal treatment is a widely used remediation strategy for PFAS-contaminated materials such as soil, spent sorbents, and domestic waste. To better understand the effectiveness and environmental impact of thermal treatments for PFAS-contaminated materials, a fundamental understanding of PFAS thermal degradation mechanisms is required. This work aims to study the pyrolysis of six representative PFAS compounds, all of which have an eight-carbon length but with different functional groups. To assess the thermal stability and pyrolysis products of these six PFAS compounds, evolved gas analysis (EGA) was performed using thermogravimetric analysis/differential scanning calorimetry (TGA/DSC) coupled with an infrared spectrometer (IR) and a mass spectrometer (MS), as well as pyrolysis-GCMS (Pyr-GCMS). The EGA data demonstrates that compounds with lower estimated vapor pressures were generally found to be more thermally labile, and the presence of an ionic bond necessitates higher temperatures for pyrolysis. Pyrolysis at 900 °C yielded a variety of fluorinated organic compounds at significant signals. Tetrafluoroethene constituted the majority of the Pyr-GCMS signal for all the compounds. Moreover, a significant fraction of detected products was unable to be identified, underscoring a need for better tools to help with the identification of unknowns. Pyrolysis can occur through random chain scission, scission of the functional group, scission of the terminal CF3 group, and HF elimination. Some compounds may undergo more complete beta-scission to produce smaller pyrolysis products compared to others. The presence of acidic protons within the functional group can help facilitate HF elimination, whereas the salt form of a PFAS compound is less likely to undergo HF elimination. Termination of radical intermediates can either be recombination with a CF3 radical or hydrogen abstraction (H-abstraction). Observed hydrogen-substituted products indicate that functional groups with higher hydrocarbon character may lead to more H-abstraction terminated products. Overall findings show that the functional group of a PFAS may decrease or increase its thermal stability and lead to different profiles of pyrolysis products.

The evolution of two‐stage production and order equilibrium in a random yield supply chain with demand information updating

AbstractThe mismatch between production, order, and demand seriously affects supply chain performance. However, most research focus on the mismatch between the retailer's order and customer's demand, which ignores the influence of the supplier's random yield on supply chain members' decision‐making. This paper investigates a two‐stage optimization problem within a two‐echelon supply chain, featuring a supplier with random yield and a retailer updating demand information in real‐time. Faced with a long production lead time, the retailer can either place advance orders at the production season's onset (first‐stage advance order) or opt for instant orders at the beginning of the sales season (second‐stage instant order). To ensure timely order fulfillment, the supplier initially employs a cost‐effective regular production mode with random yield during the production season. If yields are insufficient during sales, a pricier emergency production mode with guaranteed output becomes available. Utilizing a dynamic programming approach, we formulate the two‐stage optimization problem to derive optimal production and order decisions. Our analysis uncovers how realized random yield and stochastic market signals influence emergency production and instant order quantities in the second stage. We compare expected profits in scenarios with perfect and imperfect market signals, probing the members' preferences regarding order strategies. An intriguing finding emerges: as instant wholesale prices rising, the supplier's preferred order strategy diverges from the retailer. By strategic adjustments to the instant wholesale price, we demonstrate the potential for unanimous agreement on preferred order strategies among supply chain members — a quality enhancing the chain's flexibility and performance. Moreover, we extend the model to hybrid order strategies and identify conditions for unanimous preference among the three strategies. To bolster our theoretical findings, we provide numerical examples, lending practical support to our study.

Random Quantum Ising Model with Three-Spin Couplings.

We apply a real-space block renormalization group approach to study the critical properties of the random transverse-field Ising spin chain with multispin interactions. First, we recover the known properties of the traditional model with two-spin interactions by applying the renormalization approach for the arbitrary size of the block. For the model with three-spin couplings, we calculate the critical point and demonstrate that the phase transition is controlled by an infinite disorder fixed point. We have determined the typical correlation-length critical exponent, which seems to be different from that of the random transverse Ising chain with nearest-neighbor couplings. Thus, this model represents a new infinite disorder universality class.

Random telegraph processes with nonlocal memory.

We study two-state (dichotomous, telegraph) random ergodic continuous-time processes with dynamics depending on their past. We take into account the history of the process in an explicit form by introducing integral nonlocal memory term into conditional probability function. We start from an expression for the conditional transition probability function describing additive multistep binary random chain and show that the telegraph processes can be considered as continuous-time interpolations of discrete-time dichotomous random sequences. An equationinvolving the memory function and the two-point correlation function of the telegraph process is analytically obtained. This integral equationdefines the correlation properties of the processes with given memory functions. It also serves as a tool for solving the inverse problem, namely for generation of a telegraph process with a prescribed pair correlation function. We obtain analytically the correlation functions of the telegraph processes with two exactly solvable examples of memory functions and support these results by numerical simulations of the corresponding telegraph processes.

Critical behavior of dirty free parafermionic chains

Abstract A family of $\mathbb Z_n$-symmetric non-Hermitian models of Baxter was shown by Fendley to be exactly solvable via a parafermionic generalization of the Clifford algebra. We study these models with spatially random couplings, and obtain several exact results on thermodynamic singularities as the distributions of couplings are varied. We find that these singularities, independent of $n$, are identical to those in the random transverse-field Ising chain; correspondingly the models host infinite-randomness critical points. Similarities in structure to exact methods for random Ising models, a strong-disorder renormalization group, and generalizations to other models with free spectra, are discussed.

Reactivity Control of Oxidative CL-20@PVDF Composite Microspheres by Using Carbon Nanomaterials as Catalysts.

2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane (CL-20) is one of the high-energy oxidants, but has limited application due to its high sensitivity. In this work, polyvinylidene fluoride (PVDF) was used as a co-oxidizer, which is expected to increase the safety of CL-20. One kind of novel graphene-based carbohydrazide complex (GCCo and GCNi) was employed to modify the properties of dual-oxidant CL-20@PVDF composites by the spray drying method and compared with traditional nanocarbon materials (CNTs and GO). The properties of these composites were investigated using the TGA/DSC technique and impact test. The results show that GCCo and GCNi could increase the activation energy (Ea) of CL-20@PVDF composites, and change the physical model of CL-20@PVDF, which followed the random chain scission model and then the first-order reaction model. In addition, these nanocarbon materials could reduce the impact sensitivity of CL-20@PVDF by their unique structure. Besides that, a dual-oxidant CL-20@PVDF system was used to improve the combustion property of Boron. GCCo and GCNi with the synergetic effect could increase the flame temperature and control the burn rate of CL-20@PVDF@B compared with CNTs and GO. The energetic nanocarbon catalyst-modified oxidant provides a facile method for stabilizing high-energy but sensitive materials to broaden their application.

The Limiting Behaviors of the Gutman and Schultz Indices in Random 2k-Sided Chains

The study of complex networks with topological indices has flourished in recent years. The aim of this paper is to study the limiting behaviors of Gutman and Schultz indices in random polygonal chains, whose graph-theoretic mathematical properties and their future applications have attracted the interest of scientists. By applying the concepts of symmetry and asymptotics as well as the knowledge of probability theory, we obtain explicit analytic expressions for the Gutman and Schultz indices of n random 2k-vertex chains and prove that they converge to a normal distribution, which contributes to a deeper understanding of the structural features of random polygonal chains and plays a crucial role in the study of the limiting behavior of topological indices and their applications.

A supersymmetric quantum perspective on the explicit large deviations for reversible Markov jump processes, with applications to pure and random spin chains

The large deviations at various levels that are explicit for Markov jump processes satisfying detailed balance are revisited in terms of the supersymmetric quantum Hamiltonian H that can be obtained from the Markov generator via a similarity transformation. We first focus on the large deviations at level 2 for the empirical density p^(C) of the configurations C seen during a trajectory over the large time window [0,T] , and rewrite the explicit Donsker–Varadhan rate function as the matrix element I[2][p^(.)]=⟨p^|H|p^⟩ involving the square-root ket |p^⟩ . (The analog formula is also discussed for reversible diffusion processes as a comparison.) We then consider the explicit rate functions at higher levels, in particular for the joint probability of the empirical density p^(C) and the empirical local activities a^(C,C′) characterizing the density of jumps between two configurations (C,C′) . Finally, the explicit rate function for the joint probability of the empirical density p^(C) and of the empirical total activity A^ that represents the total density of jumps of a long trajectory is written in terms of the two matrix elements ⟨p^|H|p^⟩ and ⟨p^|Hoff|p^⟩ , where Hoff represents the off-diagonal part of the supersymmetric Hamiltonian H. This general framework is then applied to pure or random spin chains with single-spin-flip or two-spin-flip transition rates, where the supersymmetric Hamiltonian H corresponds to quantum spin chains with local interactions involving Pauli matrices of two or three neighboring sites. It is then useful to introduce the quantum density matrix ρ^=|p^⟩⟨p^| associated with the empirical density p^(.) in order to rewrite the various rate functions in terms of reduced density matrices involving only two or three neighboring sites.

Evaluating disease surveillance strategies for early outbreak detection in contact networks with varying community structure

Disease surveillance systems allow public health agencies to respond to emerging diseases before they become widespread. Developing such systems requires identifying optimal ways to monitor in the context of an epidemic outbreak; this problem is known as sensor selection. Contact networks represent the dynamics of interaction in a population and are used to model how a disease spreads in a population and to explore strategies of sensor selection. We evaluated five sensor selection strategies on their ability to provide an early warning of a COVID-like outbreak in synthetic contact networks encapsulated in four network scenarios. Three of these scenarios assessed different aspects of community structure. The fourth scenario employed a contact network representing the population and interactions of 6.8 million people in New York City, constructed from an agent-based simulation using census and transportation data. This scenario exemplifies how sensor selection strategies may perform in a real-world, urban context. Our findings suggest that the choice of the optimal strategy depends heavily on the community structure of the network. Strategies that select highly connected nodes or maximize network coverage are the optimal surveillance strategy for outbreak detection in many network community structures. However, a naive implementation of these strategies may fail to provide an early warning at all—including in the New York City scenario. Moreover, these methods are impractical for real-world use as they require knowledge of the underlying contact network. Instead, a selection strategy that starts with a set of random nodes and then performs a random walk through a chain of neighbors reliably provides early warnings without requiring prior knowledge of the network. We find this method, called “random chain”, to be the most pragmatic for implementation in a real-world disease surveillance context.

Enhancing the foamability of poly(L-lactide-co-ε-caprolactone) through formation of stereocomplex crystal by introducing of poly(D-lactic acid)

The bio-elastomer poly (L-lactide-co-ε-caprolactone) (PLCL) with linear random molecular chain structure exhibits low viscosity and melt strength, resulting in poor foamability, cell merging, or collapse during the foaming process. Consequently, PLCL foams have large cell sizes and low cell densities, limiting their use as biodegradable elastomeric foams. To enhance the foamability of PLCL, poly (D-lactic acid) (PDLA) was incorporated into the PLCL matrix. Stereocomplex crystals (Sc-crystals) formed between the L-LA segments of PLCL and the molecular segments of PDLA. The effects of varying PDLA content on the basic properties and foaming behavior of the blends were investigated. Results indicate that optimal Sc-crystal formation occurs in the PLCL/PDLA-10 blend. The presence of Sc-crystals enhances melt strength and facilitates cell nucleation, reducing cell size from 351.2 to 11.8 μm and increasing cell density from 1.4 × 104 to 1.7 × 108 cells/cm³. Consequently, the mechanical properties of the foam are significantly improved. The Sc-crystal formation between PLCL and PDLA provides an effective method to enhance the processability of linear chain copolymers containing L-lactide.

E-waste prediction and optimal route selection using adaptive deep Markov random field and block chain

PurposeAn efficient e-waste management system is developed, aided by deep learning techniques. Here, a smart bin system using Internet of things (IoT) sensors is generated. The sensors detect the level of waste in the dustbin. The data collected by the IoT sensor is stored in the blockchain. Here, an adaptive deep Markov random field (ADMRF) method is implemented to determine the weight of the wastes. The performance of the ADMRF is boosted by optimizing its parameters with the help of the improved corona virus herd immunity optimization algorithm (ICVHIOA). Here, the main objective of the developed ADMRF-based waste weight prediction is to minimize the root mean square error (RMSE) and mean absolute error (MAE) rate at the time of testing. If the weight of the bins is more than 80%, then an alert message will be sent to the waste collector directly. Optimal route selection is carried out using the developed ICVHIOA for efficient collection of wastes from the smart bin. Here, the main objectives of the optimal route selection are to reduce the distance and time to minimize the operational cost and the environmental impacts. The collected waste is then considered for recycling. The performance of the implemented IoT and blockchain-based smart dustbin is evaluated by comparing it with other existing smart dustbins for e-waste management.Design/methodology/approachThe developed e-waste management system is used to collect the waste and to avoid certain diseases caused by the dumped waste. Disposal and recycling of the e-waste is necessary to decrease pollution and to manufacture new products from the waste.FindingsThe RMSE of the implemented framework was 33.65% better than convolutional neural network (CNN), 27.12% increased than recurrent neural network (RNN), 22.27% advanced than Resnet and 9.99% superior to long short-term memory (LSTM).Originality/valueThe proposed E-waste management system has given an enhanced performance rate in weight prediction and also in optimal route selection when compared with other conventional methods.

Enhancing starch functionality through synergistic modification via sequential treatments with cold plasma and electron beam irradiation

The impact of dual sequential modifications using radio-frequency (RF) plasma and electron beam irradiation (EBI) on starch properties was investigated and compared with single treatments within an irradiation dose range of 5–20 kGy. Regardless of sequence, dual treatments synergistically affected starch properties, increasing acidity, solubility, and paste clarity, while decreasing rheological features with increasing irradiation dose. The molecular weight distribution was also synergistically influenced. Amylopectin distribution broadened particularly below 10 kGy. Amylose narrowed its distribution across all irradiation doses. This was due to dominating EBI-induced degradation and molecular rearrangements from RF plasma. With the highest average radiation-chemical yield (G) and degradation rate constant (k) of (2.12 ± 0.14) × 10−6 mol·J−1 and (3.43 ± 0.23) × 10−4 kGy−1, respectively, upon RF plasma pre-treatment, amylose underwent random chain scission. In comparison to single treatments, dual modification caused minor alterations in spectral characteristics and crystal short-range order structure, along with increased granule aggregation and surface irregularities. The synergistic effect was dose-dependent, significant up to 10 kGy, irrespective of treatment sequence. The highest synergistic ratio was observed when RF plasma preceded irradiation, demonstrating the superior efficiency of plasma pre-treatment in combination with EBI. This synergy has the potential to lower costs and extend starch's technological uses by enhancing radiation sensitivity and reducing the irradiation dose.

Magnetic order in holmium and erbium formate: Parent frameworks for a potential random spin chain paramagnet

This work uses a combination of neutron diffraction and bulk property measurements to establish the low temperature magnetic states in the dense metal-organic frameworks Ho(HCO2)3 and Er(HCO2)3; whose structures feature chains of face-sharing LnO9 polyhedra packed into a triangular lattice. Below 0.7 K Ho(HCO2)3 is found to adopt a state in which the magnetic moment on its ferromagnetic chains vary from neighbouring chains but the sum around each triangle is constant. Er(HCO2)3 is found to be the first lanthanide formate to adopt an ordered magnetic state with antiferromagnetic coupling within its chains near 50 mK. The potential to combine the ferromagnetic and antiferromagnetic coupling within chains associated with Ho and Er cations, respectively, in the same compound has also been explored via the series Ho1-xErx(HCO2)3. Ho0.5Er0.5(HCO2)3 remains paramagnetic to 0.4 K, suggesting it may be a starting point to search for a random spin chain paramagnet.

Exploring expected values of topological indices of random cyclodecane chains for chemical insights

Chemical graph theory has made a significant contribution to understand the chemical compound properties in the modern era of chemical science. At present, calculation of the topological indices is one of most important area of research in the field of chemical graph theory. Cyclodecane is a cyclic hydrocarbon with the chemical formula C10H20\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$C_{10}H_{20}$$\\end{document}. It consists of a ring of ten carbon atoms bonded together in a cyclical structure. Cyclodecane chains can be part of larger molecules or polymers, where multiple cyclodecane rings are connected together. These molecules can have various applications in chemistry, materials science, and pharmaceuticals. This article aims to determine expected values of some connectivity based topological indices of random cyclodecane chains, containing saturated hydrocarbons with at least two rings. It also compares these descriptors using explicit formulae, numerical tables and present graphical profiles of these comparisons.

On the Re-Creation of Protoribosome Analogues in the Lab.

The evolution of the translation system is a fundamental issue in the quest for the origin of life. A feasible evolutionary scenario necessitates the autonomous emergence of a protoribosome capable of catalyzing the synthesis of the initial peptides. The peptidyl transferase center (PTC) region in the modern ribosomal large subunit is believed to retain a vestige of such a prebiotic non-coded protoribosome, which would have self-assembled from random RNA chains, catalyzed peptide bond formation between arbitrary amino acids, and produced short peptides. Recently, three research groups experimentally demonstrated that several distinct dimeric constructs of protoribosome analogues, derived predicated on the approximate 2-fold rotational symmetry inherent in the PTC region, possess the ability to spontaneously fold, dimerize, and catalyze the formation of peptide bonds and of short peptides. These dimers are examined, aiming at retrieving information concerned with the characteristics of a prebiotic protoribosome. The analysis suggests preconditions for the laboratory re-creation of credible protoribosome analogues, including the preference of a heterodimer protoribosome, contradicting the common belief in the precedence of homodimers. Additionally, it derives a dynamic process which possibly played a role in the spontaneous production of the first bio-catalyzed peptides in the prebiotic world.

Infinite disorder renormalization fixed point for the continuum random field Ising chain

Infinite disorder renormalization fixed point for the continuum random field Ising chain

Analyzing the expected values of neighborhood degree-based topological indices in random cyclooctane chains.

Cyclooctane is classified as a cycloalkane, characterized by the chemical formula C 8 H 16. It consists of a closed ring structure composed of eight carbon atoms and sixteen hydrogen atoms. A cyclooctane chain typically refers to a series of cyclooctane molecules linked together. Cyclooctane and its derivatives find various applications in chemistry, materials science, and industry. Topological indices are numerical values associated with the molecular graph of a chemical compound, predicting certain physical or chemical properties. In this study, we calculated the expected values of degree-based and neighborhood degree-based topological descriptors for random cyclooctane chains. A comparison of these topological indices' expected values is presented at the end.

The impact of ultrasonic-assisted extraction on the in vitro hypoglycemic activity of peach gum polysaccharide in relation to its conformational conversion.

Peach gum (PG) is an exudate of the peach tree (Prunus persica of the Rosaceae family), which consists primarily of polysaccharides with a large molecular weight and branching structure. Consequently, PG can only swell in water and does not dissolve easily, which severely limits its application. Current conventional extraction methods for PG polysaccharide (PGPS) are time consuming and inefficient. This study investigated the impact of ultrasonic-assisted extraction (UAE) on PGPS structure and conformation, and their relationship to hypoglycemic activity in vitro. In comparison with conventional aqueous extraction, UAE enhanced PGPS yielded from 28.07-32.83% to 80.37-84.90% (w/w) in 2 h. It drastically decreased the molecular size and conformational parameters of PGPS, including weight-average molecular weight (Mw), number-average molecular weight (Mn), z-average radius of gyration (Rg), hydrodynamic radius (Rh) and instrinsic viscosity ([η]) values. Peach gum polysaccharide conformation converted extended molecules to flexible random coil chains or compact spheres with no obvious primary structure alteration. Furthermore, UAE altered the flow behavior of PGPS solution from that of a non-Newtonian fluid to that of a Newtonian fluid. As a result, PGPS treated with UAE displayed weaker inhibitory activity than untreated PGPS, mostly because UAE weakens the binding strength of PGPS to α-glucosidase. However, this negative effect of UAE on PGPS activity was compensated by the increased solubility of polysaccharide. This enabled PGPS to achieve a wider range of doses. Ultrasonic-assisted extraction is capable of degrading PGPS efficiently while preserving its primary structure, resulting in a Newtonian fluid solution. The degraded PGPS conformations displayed a consistent correlation with their inhibitory effect on α-glucosidase activity. © 2024 Society of Chemical Industry.

Crystallization Behavior of Copolyesters Containing Sulfonates.

The polar sulfonate groups in cationic dyeable polyester (CDP) lead to complex crystallization behavior, affecting CDP production's stability. In this study, cationic dyeable polyesters (CDP) with different sulfonate group contents were prepared via one-step feeding of sodium isophthalic acid-5-sulfonate (SIPA), terephthalic acid (PTA), and ethylene glycol (EG). The non-isothermal crystallization behavior of these copolyesters was analyzed by differential scanning calorimetry (DSC). Results show that the crystallization temperature of the sample shifts to lower values with the increase in SIPA content. The relaxation behavior of the molecular chain is enhanced due to the ionic aggregation effect of sulfonate groups in CDP. Therefore, at low cooling rates (2.5 °C/min and 5 °C/min), some molecular chain segments in CDP are still too late to orderly stack into the lattice, forming metastable crystals, and melting double peaks appear on the melting curve after crystallization. When the cooling rate increases (10-20 °C/min), the limited region of sulfonate aggregation in CDP increases, resulting in more random chain segments, and a cold crystallization peak appears on the melting curve after crystallization. The non-isothermal crystallization behavior of all samples was fitted and analyzed by the Jeziorny equation, Ozawa equation, and Mo equation. The results indicate that the nucleation density and nucleation growth rate of CDP decrease with the increase in SIPA content. Meanwhile, analysis of the Kissinger equation reveals that the activation energy of non-isothermal crystallization decreases gradually with the increase in SIPA content, and the addition of SIPA makes CDP crystallization more difficult.