Complex Polymerization Process Research Articles

Artificial Polymerizations in Living Organisms for Biomedical Applications

AbstractWithin living organisms, numerous nanomachines are constantly involved in complex polymerization processes, generating a diverse array of biomacromolecules for maintaining biological activities. Transporting artificial polymerizations from lab settings into biological contexts has expanded opportunities for understanding and managing biological events, creating novel cellular compartments, and introducing new functionalities. This review summarizes the recent advancements in artificial polymerizations, including those responding to external stimuli, internal environmental factors, and those that polymerize spontaneously. More importantly, the cutting‐edge biomedical application scenarios of artificial polymerization, notably in safeguarding cells, modulating biological events, improving diagnostic performance, and facilitating therapeutic efficacy are highlighted. Finally, this review outlines the key challenges and technological obstacles that remain for polymerizations in biological organisms, as well as offers insights into potential directions for advancing their practical applications and clinical trials.

Artificial Polymerizations in Living Organisms for Biomedical Applications.

Within living organisms, numerous nanomachines are constantly involved in complex polymerization processes, generating a diverse array of biomacromolecules for maintaining biological activities. Transporting artificial polymerizations from lab settings into biological contexts has expanded opportunities for understanding and managing biological events, creating novel cellular compartments, and introducing new functionalities. This review summarizes the recent advancements in artificial polymerizations, including those responding to external stimuli, internal environmental factors, and those that polymerize spontaneously. More importantly, the cutting-edge biomedical application scenarios of artificial polymerization, notably in safeguarding cells, modulating biological events, improving diagnostic performance, and facilitating therapeutic efficacy are highlighted. Finally, this review outlines the key challenges and technological obstacles that remain for polymerizations in biological organisms, as well as offers insights into potential directions for advancing their practical applications and clinical trials.

Preparation of amino-functionalized microcrystalline cellulose from Arundo Donax L. and its effect on the curing behavior of bisphenol A–based benzoxazine

In this paper, microcrystalline cellulose (MCC) was extracted from Arundo donax L. (AD), using acid hydrolysis method, and successfully functionalized with amine groups. The structure, morphology and crystallinity of the unmodified and modified MCCs were investigated. Subsequently, the amino-functionalized MCC (MCCA) at different ratios was employed as a reinforcement of bisphenol A aniline based benzoxazine (BA-a), and the curing kinetics of the as-prepared composites were studied by two isoconversional integral methods. The kinetic triplet was determined. The MCCA-based composites showed a single curing stage relative to the complex polymerization process of pure BA-a. Interestingly, the average activation energy and decimal logarithm of pre-exponential factor of the cure process decreased respectively from 89 kJ/mol and 6.95 for BA-a to 80 kJ/mol and 6.05 with the incorporation of 15 % (wt.) MCCA. The studied systems followed an Avrami-Erofeev reaction mechanism, which reflects the autocatalytic polymerization behavior of BA-a.

Multicomponent 3d Transition-Metal Nanoparticles as Catalysts Free of Pd, Pt, or Rh for Automotive Three-Way Catalytic Converters

Multicomponent 3d transition metal nanoparticles supported on Al2O3 were prepared using a complex polymerization process and a post H2-reduction treatment at 900 °C. Catalysts in a binary system we...

Modeling and process optimization of a full-scale emulsion polymerization reactor

One of the most common but promising processes for the production of paints and coatings is the free radical emulsion polymerization reaction involving different types of monomers. As it is also demonstrated by statistics concerning accidents in chemical industries, polymerizations are one of the most frequent causes of thermal runaway; therefore such syntheses require a very high level of control of all the operating variables, especially at full plant scale where safety problems are of paramount importance.The main aims of this work were the modeling and the industrial scale optimization of a complex polymerization process. Since four different monomers are involved in different proportions in such a process, the pseudo-homopolymer approach together with available literature correlations for the estimation of the main constitutive parameters of the system were used to solve the mathematical model and simulate the dynamics of the synthesis at the full plant scale.The proposed model and all the constitutive parameters estimates were validated through the comparison with both laboratory and full-scale experimental data, demonstrating a good agreement. Finally, the results of the optimization procedure were validated at laboratory scale.

Cooperative polymerization of one-patch colloids.

We numerically investigate cooperative polymerization in an off-lattice model based on a pairwise additive potential using particles with a single attractive patch that covers 30% of the colloid surface. Upon cooling, these particles self-assemble into small clusters which, below a density-dependent temperature, spontaneously reorganize into long straight tubes. We evaluate the partition functions of clusters of all sizes to provide an accurate description of the chemical reaction constants governing this process. Our calculations show that, for intermediate sizes, the partition functions retain contributions from two different structures, differing in both energy and entropy. We illustrate the microscopic mechanism behind the complex polymerization process in this system and provide a detailed evaluation of its thermodynamics.

Modeling and Simulation of Curing and Damage in Thermosetting Adhesives

The curing of thermosetting adhesives is a complex polymerization process that involves the transition of a viscous liquid into a viscoelastic solid. This phase transition is frequently accompanied by a volume shrinkage of the material, which may induce mechanical strains and stresses. These, in turn, can lead to a reduced performance or even failure of the adhesive joint. The present contribution introduces a continuum mechanical model that is suited to describe the emergence of stresses and the corresponding initiation of material degradation in adhesive layers, both during the process of curing and, of course, during subsequent loading. The model is implemented into a finite element code and some numerical examples demonstrate the interaction of curing shrinkage, stress evolution, and damage.

Simple and exact Monte Carlo algorithm for modelling of complex polymerization processes

A new, exact Monte Carlo algorithm for the simulation of living/controlled polymerization processes is proposed. In the algorithm macromolecules are represented by doubly linked lists, which are natural models of macromolecules. It allows modelling of elementary reactions by forming/breaking of links in the doubly linked list. As a result the algorithm has a very simple structure and high efficiency. All details of the modeled system: kinetics, molecular weight distributions, microstructure of macromolecules, etc., are accessible at any moment of simulation. Practical aspects of implementation of the new algorithm were emphasized: data structures, flowcharts, and source codes (Pascal).

Neural Networks and Genetic Algorithms Used for Modeling and Optimization of the Siloxane‐Siloxane Copolymers Synthesis

This paper presents the use of neural networks and genetic algorithms as tools for modeling and optimization applied to a complex polymerization process–synthesis of statistical dimethyl‐methylvinylsiloxane copolymers. A feed forward neural network models the dependence between the conversion of monomers and copolymer composition (output variables) and working conditions (temperature, reaction time, amount of catalyst and initial composition of monomers–input variables). The training and validation data sets are gathered by ring‐opening copolymerization of the octamethylcyclotetrasiloxane (D4) with 1,3,5,7‐tetravinyl‐1,3,5,7‐tetramethylcyclotetrasiloxane (D4 V), with a cation exchange (styrene‐divinylbenzene copolymer containing sulfonic groups) as a catalyst, in the absence of solvent. This model is included into an optimization procedure based on a scalar objective function and solved with a simple genetic algorithm. The genetic algorithm computes the optimal values for the control variables and for the weight coefficients attached to the individual objectives. An inverse neural network modeling, that is the identification of reaction conditions leading to a desired value for copolymer composition, is presented as particular variant of optimization. The genetic algorithm and neural networks prove to be good and accessible tools for solving an optimization problem performed with a multi‐objective scalar function and provide important information for the experimental practice.

Halting a cellular production line: responses to ribosomal pausing during translation

Cellular protein synthesis is a complex polymerization process carried out by multiple ribosomes translating individual mRNAs. The process must be responsive to rapidly changing conditions in the cell that could cause ribosomal pausing and queuing. In some circumstances, pausing of a bacterial ribosome can trigger translational abandonment via the process of trans-translation, mediated by tmRNA (transfer-messenger RNA) and endonucleases. Together, these factors release the ribosome from the mRNA and target the incomplete polypeptide for destruction. In eukaryotes, ribosomal pausing can initiate an analogous process carried out by the Dom34p and Hbs1p proteins, which trigger endonucleolytic attack of the mRNA, a process termed mRNA no-go decay. However, ribosomal pausing can also be employed for regulatory purposes, and controlled translational delays are used to help co-translational folding of the nascent polypeptide on the ribosome, as well as a tactic to delay translation of a protein while its encoding mRNA is being localized within the cell. However, other responses to pausing trigger ribosomal frameshift events. Recent discoveries are thus revealing a wide variety of mechanisms used to respond to translational pausing and thus regulate the flow of ribosomal traffic on the mRNA population.

Spontaneous in Vitro Formation of Supramolecular β-Amyloid Structures, “βamy Balls”, by β-Amyloid 1−40 Peptide

The concentration of beta-amyloid peptide (Abeta), x-42 or x-40 amino acids long, increases in brain with the progression Alzheimer's disease (AD). These peptides are deposited extracellularly as highly insoluble fibrils that form densities of amyloid plaques. Abeta fibrillization is a complex polymerization process preceded by the formation of oligomeric and prefibrillar Abeta intermediates. In some of our in vitro studies, in which the kinetics of intermediate steps of fibril formation were examined, we used concentrations of synthetic Abeta that exceed what is normally employed in fibrillization studies, 300-600 microM. At these concentrations, in a cell free system and under physiological conditions, Abeta 1-40 peptide (Abeta40) forms fibrils that spontaneously assemble into clearly defined spheres, "betaamy balls", with diameters of approximately 20-200 microm. These supramolecular structures show weak birefringence with Congo red staining and high stability with prolonged incubation times (at least 2 weeks) at 30 degrees C, freezing, and dilution in H(2)O. At 600 microM, they are detected after incubation for approximately 20 h. Abeta peptide 1-42 (Abeta42) lacks the ability to form betaamy balls but accelerates Abeta40 betaamy ball formation at low stoichiometric levels (1:20 Abeta42:Abeta40 ratio). Abeta42 levels above this (=10-50% w/w) impede Abeta40 betaamy ball formation. Using light (LM) and electron microscopy (EM), this study examines the gross morphology and ultrastructure of Abeta40 betaamy balls and their time course of formation, in the absence and presence of Abeta42, along with some stability measures. As spheres of a misfolded protein, betaamy balls resemble both AD Abeta senile plaques and neuronal inclusion bodies associated with other neurodegenerative diseases.

Integration of multilayer perceptron networks and linear dynamic models: a Hammerstein modeling approach

Recently, neural network dynamic modeling has drawn a great deal of attention not only from academia but also from industry. It has been shown that neural network can learn to mimic the behavior of a complex chemical process. However, a neural network dynamic model might be difficult to develop when training data do not contain sufficient nonlinear information about the process. In addition, owing to the capacity of a modern control system, a great deal of steady-state data is stored. Most modeling techniques to date do not take into account the fact that the transient data may not contain sufficient nonlinear information and that the nonlinear steady-state data are abundant and easily accessible. In order to fully utilize the abundant steady-state information, we present a neural network Hammerstein (NNH) modeling approach. In the proposed approach, a static neural network is integrated with a linear dynamic model in series. A constrained gradient-based algorithm is derived for training the linear dynamic model in order to retain the nonlinearity established by the static neural network. To demonstrate the proposed modeling approach, a complex polymerization process is used as an example

Integration of Multilayer Perceptron Networks and Linear Dynamic Models

Recently neural network dynamic modeling has drawn much attention not only from academia but also from industry. It has been shown that neural networks can model complex chemical processes. However a neural network dynamic model might be difficult to develop when training data does not contain sufficient nonlinear information. Owing to the capacity of modem control systems.a great deal of steady state data is stored. This paper presents a Neural Network Hammerstein (NNH) modeling approach in order to fully utilize the abundant steady state information. To a complex polymerization process is used to demonstrate the proposed modelingapproach

Initial glycosylation and acidic pH in the Golgi apparatus are required for multimerization of von Willebrand factor.

Two conditions were identified that interfered with the complex polymerization process in biosynthesis of von Willebrand factor (vWf). Treatment of human umbilical vein endothelial cells with tunicamycin inhibited N-linked glycosylation of nascent vWf and the resulting pro-vWf monomers failed to dimerize. The single subunits accumulated in the endoplasmic reticulum and were neither processed further nor secreted. In the presence of a weak base (ammonium chloride or chloroquine), interdimer disulfide bond formation was inhibited in a dose-dependent manner. This process appeared therefore to be pH sensitive and likely to be initiated in the acidic trans-Golgi apparatus (Anderson, R. G. W., and R. K. Pathak, 1985, Cell, 40: 635-643). The weak base had no obvious effect on the other processing steps, i.e. dimerization, complex carbohydrate formation and sulfation, and produced only slight inhibition of prosequence cleavage. On the other hand, the weak base interfered with the targeting of newly synthesized vWf into Weibel-Palade bodies, with all of the vWf being secreted constitutively and none stored in the Weibel-Palade bodies. In summary, initial glycosylation of the nascent vWf protein and low pH in the trans-Golgi apparatus were important conditions for the successful polymerization of human vWf. Genetic defects disrupting any one of these conditions could result in the phenotype of von Willebrand disease.

ChemInform Abstract: A NEW METHOD OF MAKING TITANIA GELS AND THEIR MICROSTRUCTURE

AbstractAerohydro‐ Gele werden durch Reaktion von H2O, mit folgenden Alkoxiden erhalten: Ti(OEt)4, Ti(OPr)4, Ti(OBu)4 und [(CH3)2CHO]4Ti.

A New Method of Making Titania Gels and Their Microstructure

The reaction of H2O2 with alkoxides results in aerohydro gels of titania. These gels appear to form by a complex polymerization process involving peroxy species. The gels are porous and appear to have a fibrillar microstructure.

Molecular weight distribution of polymers as a method of investigating complex polymerization processes. Cationic polymerization of cyclic ethers

A study was made of the effect of reactions taking place during cationic polymerization of cyclic ethers on the variation of molecular weight distribution of polymers during polymerization. It was shown that this variation may be a means for establishing the type of reaction taking place. Ratios were derived to determine rate constants of the reactions studied using experimental results.