58 results about "Crystallization kinetics" patented technology

Composites of single-walled carbon nanotubes (SWNTs) and a ceramic support (e.g., silica) comprising a small amount of catalytic metal, e.g., cobalt and molybdenum, are described. The particle comprising the metal and ceramic support is used as the catalyst for the production of the single-walled carbon nanotubes. The nanotube-ceramic composite thus produced can be used “as prepared” without further purification providing significant cost advantages. The nanotube-ceramic composite has also been shown to have improved properties versus those of purified carbon nanotubes in certain applications such as field emission devices. Use of precipitated and fumed silicas has resulted in nanotube-ceramic composites which may synergistically improve the properties of both the ceramic (e.g., silica) and the single-walled carbon nanotubes. Addition of these composites to polymers may improve their properties. These properties include thermal conductivity, thermal stability (tolerance to degradation), electrical conductivity, modification of crystallization kinetics, strength, elasticity modulus, fracture toughness, and other mechanical properties. Other nanotube-ceramic composites may be produced based on Al2O3, MgO and ZrO2, for example, which are suitable for a large variety of applications.

Provided are thermoplastic elastomer compositions, many dynamically vulcanized, with superior crystallization kinetics, methods for making the compositions and articles made therefrom. The thermoplastic elastomer compositions comprise at least a propylene polymer thermoplastic, a rubber and a crystallization additive. The processes for preparation of the thermoplastic elastomer compositions comprises dynamic vulcanization of mixture of a propylene polymer thermoplastic, a rubber and a cure agent followed by addition of a crystallization additive. Further, preparation of articles from the thermoplastic elastomer compositions may be accomplished through traditional thermoforming operations useful with conventional thermoplastics.

The invention discloses a method for predicting dynamic recrystallization fractions of high-alloy materials under time-varying working conditions. The method includes steps of (1), acquiring deformed test specimens of the materials through hot compression simulation experiments; (2), acquiring metallographs of the test specimens through metallographic experiments; (3), counting recrystallization fractions of the test specimens by the aid of Photoshop software; (4), acquiring equivalent strain values and average strain rate values of the test specimens by the aid of finite element simulation; (5), acquiring parameters of the traditional dynamic recrystallization kinetic model by means of regression; (6), improving the traditional dynamic recrystallization kinetic models to obtain novel models capable of predicting the dynamic recrystallization fractions under the time-varying working conditions. The method has the advantages that the method can be used for building the dynamic recrystallization kinetic models for the high-alloy materials and also can be used for predicting the dynamic recrystallization fractions under the time-varying working conditions.

Solid-state shear pulverization of semi-crystalline polymers and copolymers thereof and related methods for enhanced crystallization kinetics and physical / mechanical properties.

The invention relates to a method for acquiring a mobility parameter of a re-crystallized structure evolution crystal boundary of a metal material. The method comprises the following steps: (1) performing static re-crystallization or dynamic re-crystallization physical thermal simulation test on a metal material sample; (2) acquiring an average grain size of static re-crystallization or dynamic re-crystallization of the sample by utilizing a microstructure representation test; (3) establishing a re-crystallization dynamical model on the basis of a structural evolution numerical simulation method, wherein the model includes nucleation and growing sub-models; (4) describing a nucleation rate sub-model through a nucleation rate, excluding the parameters, such as nucleation activation energy, difficult to acquire through the test and the parameters without physical significance; (5) expressing the migration rate of the crystal boundary of a re-crystallization growing sub-model by the product of the driving force and the mobility parameter of the crystal boundary, wherein the mobility parameter of the crystal boundary is a unique fitting parameter in the whole model; (6) adjusting the mobility parameter of the crystal boundary by adopting an inverse optimization algorithm, comparing a numerical simulation result with a test result and confirming the parameter when a difference between the numerical simulation result with the test result is less than a threshold value (0.01-0.1).