Sequential Addition Method Research Articles

Generating microstructures of long fiber reinforced composites by the fused sequential addition and migration method

Generating microstructures of long fiber reinforced composites by the fused sequential addition and migration method

Automatic micro-scale modelling and evaluation of effective properties of highly porous ceramic matrix materials using the scaled boundary finite element method

This paper presents an approach to the numerical estimation of effective properties of highly porous materials based on the scaled boundary finite element method (SBFEM). The latter can be formulated on quadtree meshes with hanging nodes and thus facilitates the efficient mesh generation and analysis of a large number of randomly created samples. To generate the corresponding Representative Volume Elements (RVEs), an improved Random Sequential Addition (RSA) method with overlap is used. Moreover, an alternative image-based digital matrix method for generating random assemblies of circular particles of the same size is proposed. In addition, a modified smoothing algorithm to treat the staircase boundaries present in a quadtree mesh is developed in order to accurately and efficiently capture the sinter-neck features of overlapping circular particles. The proposed image-based mesh generation and analysis procedure is used to evaluate the relationship between microstructural parameters and effective Young’s modulus considering a large number of samples. In this context, a new dimensionless morphological parameter Nfc is proposed. The latter represents the morphology and connectivity of voids and is shown to be a useful indicator with respect to the effective elastic properties of highly porous ceramic matrix material.

Numerical study of soil-rock mixtures with 3D numerical manifold method: Generation of random aggregate structure and discretized model

As the main material composition of many large landslides in China and other countries, the soil-rock mixtures (SRMs) have attracted many researchers to investigate their mechanical and hydraulic characteristics in the past several decades. Although in-door and in-situ tests still play the dominant role in obtaining the material parameters of SRMs, numerical methods are seen as a better choice regarding economy and practicality. As a competitive numerical method, the numerical manifold method (NMM), which can solve continuous and discontinuous problems in a unified framework, is used in the present work to investigate the hydraulic characters of SRMs. Before investigating the hydraulic characters of SRMs with NMM, the aggregate structural model and discretized model of SRMs should be built. To effectively build the aggregate structure model of SRMs with high rock content, a simplified random sequential addition method, which is based on E(A, B) concept and background mesh, is firstly developed. Then, the recently proposed parallel preprocessing strategy of NMM is used to generate the discretized model of SRMs. Finally, an illustrative example regarding the determination of effective permeability coefficient of SRMs is solved with 3DNMM. The proposed numerical model in investigating SRMs deserves further investigation.

Reduced Order Machine Learning Models for Accurate Prediction of CO2 Capture in Physical Solvents.

CO2 sorption in physical solvents is one of the promising approaches for carbon capture from highly concentrated CO2 streams at high pressures. Identifying an efficient solvent and evaluating its solubility data at different operating conditions are highly essential for effective capture, which generally involves expensive and time-consuming experimental procedures. This work presents a machine learning based ultrafast alternative for accurate prediction of CO2 solubility in physical solvents using their physical, thermodynamic, and structural properties data. First, a database is established with which several linear, nonlinear, and ensemble models were trained through a systematic cross-validation and grid search method and found that kernel ridge regression (KRR) is the optimum model. Second, the descriptors are ranked based on their complete decomposition contributions derived using principal component analysis. Further, optimum key descriptors (KDs) are evaluated through an iterative sequential addition method with the objective of maximizing the prediction accuracy of the reduced order KRR (r-KRR) model. Finally, the study resulted in the r-KRR model with nine KDs exhibiting the highest prediction accuracy with a minimum root-mean-square error (0.0023), mean absolute error (0.0016), and maximum R2 (0.999). Also, the validity of the database created and ML models developed is ensured through detailed statistical analysis.

Multistep sequence-controlled supramolecular polymerization by the combination of multiple self-assembly motifs

The precise sequence control of polymer chain is an important research topic of polymer chemistry. Although some methods such as iterative synthesis and supramolecular polymerization have been developed to fabricate sequence-controllable polymer, it is still a great challenge to consecutively prepare multiple supramolecular polymers with different sequence structures. In this work, through the reasonable utilization of assembly motifs, we integrated multiple host-guest recognitions and metal coordination interactions to prepare different sequence-controlled supramolecular polymers by a multistep assembly strategy. This research provides inspiration for the design and preparation of supramolecular polymers with different sequence structures.

A sequential addition and migration method for generating microstructures of short fibers with prescribed length distribution

We describe an algorithm for generating fiber-filled volume elements for use in computational homogenization schemes. The algorithm permits to prescribe both a length distribution and a fiber-orientation tensor of second order, and composites with industrial filler fraction can be generated. Typically, for short-fiber composites, data on the fiber-length distribution and on the volume-weighted fiber-orientation tensor of second order is available. We consider a model where the fiber orientation and the fiber length distributions are independent, i.e., uncoupled. We discuss the use of closure approximations for this case and report on identifying the describing parameters of the frequently used Weibull distribution for modeling the fiber-length distribution. We discuss how to integrate these procedures in the Sequential Addition and Migration algorithm, developed for fibers of equal length, and work out algorithmic modifications accounting for possibly rather long fibers. We investigate the capabilities of the introduced methodology for industrial short-fiber composites, demonstrating the rather low dispersion of the effective elastic moduli for the generated unit cells.

Thermal-damage suppression of composite material by anti-lightning NCF/CIPCF film

Lightning strike protection (LSP) design is significant for carbon fiber reinforced plastic (CFRP), which is sensitive to lightning strikes. An anti-lightning film reinforced by nickel-coated carbon fibers/carbonyl iron powders conductivity framework (NCF/CIPCF) is designed for lightning protection of CFRP laminates. Representative volume element (RVE) models of NCF/CIPCF film are established according to random sequential addition (RSA) method. Material properties are obtained based on homogenization theory. This numerical method is validated by calculations of electrical resistivity and thermal conductivity. Subsequently, lightning damage analysis of CFRP laminates with different protections is conducted based on thermal-electric coupling method and temperature dependent ablation criteria, which aims to evaluate the suppression effects of lightning damage by this film. Results show that damage area and depth of CFRP laminates are effectively controlled by NCF/CIPCF film, which is slightly superior to aluminum at the same weight. NCF/CIPCF anti-lightning film is light and easy to repair and replace, which can be considered as an excellent and promising method in LSP design of aircraft.

A Novel Powerful Choline Chloride - Thiourea/Sulfuric Acid: An Efficient and Recyclable Catalyst Used for the Microwave-assisted Synthesis of Quinazolin- 4(3H)-one Derivatives Used as Antibacterial Agents in Green Media

Background: Choline chloride-thiourea/sulfuric acid is a powerful and efficient green catalyst used for one-pot synthesis of quinazoline-4 (3H)-one derivatives via a reaction between various amines, acetic anhydride, and anthranilic acid under microwave irradiation and solventfree conditions (4a-q). Microwave irradiation, which is a faster, more cost-effective, less energyintensive, and more efficient method than conventional heating, has been used to synthesize some quinazolinone derivatives. Introduction: For the past ten years, one of the major subjects in synthetic organic chemistry has been green synthesis, which has used efficient and environmentally friendly methods to synthesize biological compounds. The use of catalysts has significant advantages, including ease of preparation and separation, chemical and thermal stability, and environmental friendliness due to features such as reusability, low cost, and efficient and easy workup techniques. Therefore, the mechanism is performed by a non-toxic organic catalyst that uses the least amount of energy and chemical reactants in accordance with the principles of green chemistry and the least waste. Methods: One-pot and sequential addition methods have been used to synthesize quinazolinone derivatives. In the sequential addition method, the reaction was started by adding acetic anhydride and anthranilic acid to the reaction vessel under microwave irradiation and continued by adding choline chloride thiourea/sulfuric acid as efficient, recyclable green catalysts and the desired amine. In vitro, the well diffusion method against different pathogenic strains was used to evaluate the antimicrobial activity of quinazoline-4 (3H)-one derivatives. Pathogenic strains used were Candida albicans ATCC 10231 (yeast), Aspergillus niger ATCC 16404 (fungus), Escherichia coli ATCC 8739, Pseudomonas aeruginosa ATCC 9027 (bacteria) and ATCC 6538, and Staphylococcus aureus S. epidermidis ATCC 12228. Pyrimidine-containing compounds, in which the 3- hydroxyl, 2,5-dimethoxy, 4-bromo, 4‐methoxy, and 4‐chloro groups are attached to the phenyl ring of pyrimidine, exhibit antimicrobial properties. Results: In a short reaction time, a variety of biologically active quinazolinone derivatives were synthesized with high efficiency. According to the results, it was found that with aliphatic amines, the reaction time was shorter, and the reaction efficiency was higher. Products synthesized from aromatic amines had more antibacterial properties. Conclusion: In this work, a variety of 2-methyl-quinazoline-4 (3H)-one derivatives (4a–q) were synthesized as potent antibacterial agents under microwave irradiation and solvent-free conditions in the presence of ChCl-thiourea/H2SO4 as an efficient, eco-friendly, and recyclable catalyst.

Toughening Poly(l-lactide) Blends: Effectiveness of Sequence-Controlled Six-Arm Star-Branched Block Copolymers of Poly(l-lactide) and Poly(ε-caprolactone).

Well-defined six-arm star-branched bio-degradable block copolymers of l-lactide and ε-caprolactone were prepared using controlled ring-opening polymerization and a sequential monomer addition method using dipentaerythritol as the initiator core and organocatalysts at low temperatures in solution. Sequence of enchainment was changed by reversing the order of monomer addition giving, either, a crystalline PLA block or an amorphous PCL block as the outer segment. Well-defined six-arm poly(ε-caprolactone-b-l-lactide, 6s-PCL-b-PLA) block copolymers were obtained with a range of segment molecular weights. However, in the case of six-arm poly(l-lactide-b-ε-caprolactone, 6s-PLA-b-PCL), disruption of the block structure was observed on account of competing transesterification reactions accompanying a chain-growth reaction. Such sequence-controlled block copolymers showed interesting phase morphologies, as evidenced by differential scanning calorimetry (DSC) studies. 6s-PCL-b-PLA showed two glass-transition temperatures and two melting temperatures characteristic of the amorphous and crystalline blocks. 6s-PCL-b-PLA and 6s-PLA-b-PCL with different segment chain lengths were solution blended (10 wt %) with a commercially sourced PLA. All the blends were highly transparent. The structure and properties of the blend were examined by DSC, measurement of mechanical properties, and scanning electron microscopy. The results show that a phase-separated 6s-PCL-b-PLA copolymer results in two- to three-fold improvement in tensile toughness without the loss of modulus. A possible hypothesis for the mechanism of tensile toughness in the blend has been proposed.

ODR-VS method for a high packing fraction of dispersed TRISO particles

The stochastic dispersion of tristructural isotropic (TRISO)-coated particles brings challenge for accurate modeling and neutron transport analysis. Existed explicit modeling methods for stochastic media, like the random sequential addition (RSA) method and discrete element method (DEM), have difficulties in building dispersed particles that do not touch each other with packing fractions higher than 38%. In this paper, we proposed a new packing method for this kind of model by combining the optimized dropping and rolling (ODR) method and the virtual surface (VS) method. The testing results for different packing fractions indicate that our modified ODR method can improve the packing efficiency hundreds of times compared with the DEM. In addition, we successfully construct a stochastic geometry with no touching particle configuration. In this model, the packing fraction achieves 40%, demonstrating that our method is capable of handling model of no contact dispersed particles with a packing fraction higher than 38%.

Mesoscopic study of concrete with random aggregate model using phase field method

In the present work, the phase field method implemented by the user-defined element (UEL) subroutine of commercial finite element software ABAQUS is adopted to investigate the failure pattern and mechanical responses of meso-scale concrete specimens. To build the structure model of meso-scale concrete specimens, the recently proposed random sequential addition (RSA) method is used. With the proposed numerical model, the uniaxial tensile test was carried out as a benchmark test to investigate the influence of several parameters, such as the volume fraction, size, and major axis direction of aggregates on the mechanical responses and failure mode of concrete specimens. Then, two classic examples, namely, a three-point bending (TPB) test and a four-point bending (FPB) test, are investigated with the proposed numerical model. The numerical results from the proposed numerical model indicate that: A higher interfacial transition zone (ITZ) content has an adverse effect on the peak strength of concrete; A negative correlation is shown between major axis direction of aggregates and peak strength of concrete; Intergranular fracture mode is observed, in which the damage areas of concrete are mainly distributed around the ITZ; The linear elastic stiffness and peak strength are almost independent of the aggregate distribution, but the fracture propagation patterns are sensitive to meso-structures of concrete; and numerical results of TPB and FPB tests also indicate that the notch of concrete has non-negligible influences on fracture propagation path, as well as linear elastic stiffness and peak load of concrete specimens.

Universal Suzuki-Miyaura Catalyst-Transfer Polymerization for Precision Synthesis of Strong Donor/Acceptor-Based Conjugated Polymers and Their Sequence Engineering.

Catalyst-transfer polymerization has revolutionized the field of polymer synthesis due to its living character, but for a given catalyst system, the polymer scope is rather narrow. Herein we report a highly efficient Suzuki-Miyaura catalyst-transfer polymerization (SCTP) that covers a wide range of monomers from electron-rich (donor, D) to electron-deficient (acceptor, A) (hetero)arenes by rationally designing boronate monomers and using commercially available Buchwald RuPhos and SPhos Pd G3 precatalysts. Initially, we optimized the controlled polymerization of 3,4-propylenedioxythiophene (ProDOT), benzotriazole (BTz), quinoxaline (QX), and 2,3-diphenylquinoxaline (QXPh) by introducing new boronates, such as 4,4,8,8-tetramethyl-1,3,6,2-dioxazaborocane and its N-benzylated derivative, to modulate the reactivity and stability of the monomers. As a result, PProDOT, PBTz, PQX, and PQXPh were prepared with controlled molecular weight and narrow dispersity (Đ < 1.29) in excellent yield (>85%). A detailed investigation of the polymer structures using 1H NMR and MALDI-TOF spectrometry supported the chain-growth mechanism and the high initiation efficiency of the SCTP method. In addition, the use of RuPhos-Pd showing excellent catalyst-transfer ability on both D/A monomers led to unprecedented controlled D-A statistical copolymerization, thereby modulating the HOMO energy level (from -5.11 to -4.80 eV) and band gap energy (from 1.68 to 1.91 eV) of the resulting copolymers. Moreover, to demonstrate the living nature of SCTP, various combinations of D-A and A-A block copolymers (PBTz-b-PProDOT, PQX-b-PProDOT, and PQX-b-PBTz) were successfully prepared by the sequential addition method. Finally, simple but powerful one-shot D-A block copolymerization was achieved by maximizing the rate difference between a fast-propagating pinacol boronate donor and a slow-propagating acceptor to afford well-defined poly(3-hexylthiophene)-b-poly(benzotriazole).

One-Pot Synthesis of Supertough, Sustainable Polyester Thermoplastic Elastomers Using Block-Like, Gradient Copolymer as Soft Midblock

It remains challenging to synthesize supertough thermoplastic elastomers (TPEs) since the stretchability and tensile strength are mutually exclusive. Here, we report a one-pot strategy for the prep...

AN IMPROVED DISTINCT ELEMENT METHOD FOR HIGH PACKING FRACTION STOCHASTIC MEDIA MODELING

Due to the generality and flexibility of Monte Carlo method in geometric modeling, Monte Carlo method plays an important role in accurate simulation of random media. At present, rand om sequential addition method (RSA) and distinct element method (DEM) are more accurate and mature explicit modeling methods. The former approach has the problem of upper limit of packing fraction, which is suitable for stochastic geometry with lower filling rate. DEM method can fill random medium model with packing fraction higher than 60%, but DEM is not suitable for non-contact dispersed particles based on the interaction between particles. There fore, an improved DEM method is proposed to solve the problem of modeling non-contact p articles dispersed in the stochastic media with high packing fraction. The virtual surfaces are constructed outside of the outer layer of particles to make them in contact with each other. Thus, the particle system is suitable for DEM method. The construction of virtual surface does not affect the neutron transport process. The correctness of the improved DEM is verified by comparing the total filling particle number and calculation results ofkeffwith RSA method. At the same time, according to the distribution of filling particles, the improved DEM method fills the particles more uniformly.

One Pot Hydrogenation of Furfural to 2‐Methyl Tetrahydrofuran over Supported Mono‐ and Bi‐metallic Catalysts

Abstract2‐Methyltetrahydrofuran is a valuable commercial product that can be obtained by direct hydrogenation of furfural. In the present study, among several carbon supported bimetallic Ir−Ni catalysts with different loadings screened, 4% Ir‐4% Ni/C catalyst showed excellent activity in terms of direct conversion (99%) to 2‐MeTHF with a maximum selectivity of ∼74% at 220 °C and 750 psig, suppressing the formation of side chain as well as ring opening products. The catalytic activity was found to be mainly affected by catalyst preparation methods, metal loadings, surface composition, temperature, pressure and catalyst loading. HR‐TEM and STEM revealed well dispersed Ir−Ni NPs having the particle sizes in the range of 2 to 5 nm. Different phases of Ir i. e. Ir° and IrO2 as well as oxygen vacancies were found to be responsible for hydrogenation of furfural to 2‐methyl furan while, Ni° and NiO were responsible for further hydrogenation to 2‐MeTHF. The synergic effect between Ir and Ni was established through XPS, H2‐TPR analysis. With the help of some control experiments, the plausible reaction pathway was also proposed. The catalyst prepared by co‐impregnation method found more effective than prepared by sequential addition method. At lower Ni loadings of 1% and 2%, low temperature of 160 °C as well as at low H2 pressure of 250 psig, mixture of furfuryl alcohol and 2‐methyl furan were formed selectively. Catalyst could be successfully reused up to 3 times without leaching of metals.

Thermoresponsive property of well-defined poly(N-methyl-N-n-propylacrylamide) and its copolymer architectures prepared by hydrosilylation-promoted group transfer polymerization

N-methyl-N-n-propylacrylamide (MnPAm) was polymerized using a versatile organocatalytic group transfer polymerization (GTP) system to afford the well-defined poly(N-methyl-N-n-propylacrylamide) (PMnPAm), in which a silyl ketene aminal was in-situ formed as the initiator by the B(C6F5)3-catalyzed 1,4-hydrosilylation of MnPAm and the stable hydrosilane of Me2EtSiH. After confirming the controlled/living feature for the hydrosilylation-promoted GTP system by the chain extension experiment, a series of copolymer architectures composed of the PMnPAm segment (A) as the thermoresponsive moiety and the poly(N,N-dimethylacrylamide) (PDMAm) segment (B) as the water-soluble one were synthesized using the sequential monomer addition method, i.e., AB-statistical copolymer, AB-diblock copolymer, and ABA- and BAB-triblock copolymers. The successful synthesis of PMnPAm provided careful insights into the thermoresponsive property with the cloud point temperature (Tc) around or below room temperature (29.3–14.5 °C) depending on the molecular weights. The thermoresponsive property for all of the prepared copolymer architectures of the AB-statistical copolymer, AB-diblock copolymer, and ABA- and BAB-triblock copolymers was described along with their aggregation behavior above the Tc for the thermoresponsive PMnPAm segment.

Improving discrete particle packing models for the microstructural formation simulation of Portland cement

The 3D solid-phase spatial distribution strongly influences the electrical, mechanical and chemical properties of Portland cement microstructure. The commonly applied random sequential addition (RSA) method in cement microstructure formation simulation is causing over-computation of volume expansion due to the unrealistic initial geometry condition. However, it is difficult to search for a suitable model to represent the initial 3D pore structure with sufficient pore density range and compatibility with existing 3D cement microstructure models. An approach is proposed based on introducing a pseudo-contact mechanics analysis step to the RSA-discrete particle packing simulation scheme. The key control parameter to obtained specific pore density is identified to be the coefficient of friction after adjusting relative velocity distribution, lattice elastic constant and particle/domain size ratio. The proposed method enables the generation of random 3D pore structure with the same lattice configuration of major discrete cement microstructure formation models and Lattice Boltzmann Method (LBM), allowing more realistic 3D structure input and coupled modelling.

Laser absorption behavior of randomly packed powder-bed during selective laser melting of SiC and TiB2 reinforced Al matrix composites

In this paper, a randomly packed powder-bed model by sequential addition method was established mathematically and physically to simulate the condition of the interaction between the laser beam and powder particles. The ray-tracing calculation was used to analyze laser absorptivity and irradiance distribution occurring on powder particle surface as well as the influence of reinforcements on the laser energy absorption during selective laser melting additive manufacturing of pure AlSi10Mg, SiC/AlSi10Mg and TiB2/AlSi10Mg materials. The simulation results reveal that addition of reinforcements enhanced the interactions of laser beam with powder particles, which were illustrated by an increase of laser ray track spots on the particles surface. A respective absorptivity fluctuated from 0.37 to 0.59 and from 0.34 to 0.49 for the randomly packed SiC/AlSi10Mg and TiB2/AlSi10Mg, both of which were significantly enhanced with respective to that of AlSi10Mg powder. The addition of ceramic reinforcement particles can significantly improve the absorption irradiance of laser for powder-bed of pure AlSi10Mg, while the SiC reinforcement was more effective in enhancing this effect than TiB2. The influence of different absorption irradiance on the melt pool dimensions and surface morphologies were studied experimentally to verify the reliability of this simulation.

Shielding effectiveness and bandgaps of interpenetrating phase composites based on the Schwarz Primitive surface

Conductive composites possessing a polymeric matrix have been developed as an auspicious class of materials yielding superior properties to metal-based materials. The electromagnetic shielding effectiveness and bandgaps of a novel interpenetrating phase composite with a polymeric matrix are studied computationally. This composite is generated from a so-called Schwarz Primitive surface, a member of the triply periodic minimal surfaces family. The shielding effectiveness of the resulting Primitive-based composite is compared with those of composites reinforced with periodically and randomly distributed spherical conductive particles. For the composites with random spherical particles, the random sequential addition method is used to generate the realizations of fillers followed by the Monte Carlo relaxation step to obtain an equilibrated configuration. The Primitive-based composite shows higher shielding effectiveness due to the interconnectivity of both phases (conductive phase and polymeric matrix) leading to a higher effective electrical conductivity. Employing a finite element analysis leads to dispersion curves, which reveal the existence of electromagnetic bandgaps at low frequencies and low volume fractions of the conductive phase, in comparison to those of other structures reported in the literature. The Primitive-based composite shows the bandgaps for transverse-electric modes, where the widths of the bandgaps vary with the volume fraction of the conductive phase.

Thermal conductivity of rubber composite materials with a hybrid AlN/carbon fiber filler

Composite materials with high thermal conductivity have the advantages of corrosion resistance, fatigue resistance, easy processing, and electrical insulation and hence are widely used in industries concerned with high heat dissipation, such as aviation, aerospace, and electronics. Adding with high thermal conductivity, such as carbon black, metal particles, carbon fiber, and carbon nanotubes, to rubber matrix materials is recognized as an efficient method to improve the thermal conductivity of the resulting composite materials. However, when single filler is used, the content of the often must be improved to obtain high thermal conductivity, which entails a complicated process and high production cost. In this study, a hybrid filler of spherical aluminum nitride (AlN) and carbon fiber is added to rubber matrix materials. These hybrid filler particles greatly influence the thermal conductivity of the composite materials. To explore the effects of the hybrid on the thermal conductivity of the composite materials, a three-dimensional random representative volume element (RVE) model filled with both spherical AlN and carbon fiber was developed using a random sequential addition method and homogenization theory. The size of the RVE model was 40 μm×40 μm×40 μm. To prevent the formation of larger fillers by the aggregation of small fillers, a lower volume fraction of filler was investigated in this study. ANSYS software was used to simulate the influence of the space distribution, volume ratio, and filling fraction of the two on the thermal conductivity of the composite material with hybrid fillers. A steady-state heat conduction model was adopted, and a temperature gradient from 20 to 40°C was applied in the X , Y , and Z directions. The results showed that the thermal conductivity was different in the three directions due to the difference in the orientation of the carbon fibers. It can be seen from the temperature distribution clouds that there are many thermal network paths in the direction of higher thermal conductivity. The average thermal conductivity in all directions could macroscopically characterize the thermal conductivity of the composite materials. Carbon fiber plays a dominant role in the thermal conductivity of the composite material, and the thermal conductivity increases linearly with the ratio of the fillers. When the volume fraction of the carbon fibers is constant, the thermal conductivity of the composite material increases slowly with the increase in the AlN fraction. In comparison, when the volume fraction of AlN is constant, the thermal conductivity of the composite material increases rapidly with the increase of in the carbon fiber fraction. Because of enhanced synergistic effects between the in the matrix, a lower filling fraction of carbon fibers could be used to obtain higher thermal conductivity when designing the structure of composite materials. The results of this study provide a theoretical basis for the preparation of composite materials with high thermal conductivity.