5results about How to "Raise the absolute value" patented technology

This invention, entitled "A Camellia Seed Oil Microcapsule Emulsion and Its Preparation Method and Application," belongs to the field of cosmetic technology. The technical problem it aims to solve is the deficiency in existing technologies where camellia seed oil is prone to rancidity, leading to reduced stability and bioavailability of formulations. This invention provides a camellia seed oil microcapsule emulsion system that can slow down the rancidity process, thus achieving a transition from oil-soluble to water-soluble camellia seed oil. The key technical solution is a camellia seed oil microcapsule emulsion, the raw materials of which consist of camellia seed oil, sodium caseinate, chitosan hydrochloride, PE9010, and a solvent.

This invention discloses a method for preparing functional materials by synergistic Joule heating sintering of dual-state powders. First and second ball-milled powders are prepared according to a target composition. The first ball-milled powder undergoes a first rapid Joule heating pretreatment to obtain a pre-Joule-heat-treated powder. The second ball-milled powder, used as the ball-milling active powder, is mixed with the pre-Joule-heat-treated powder, cold-pressed, and then rapidly Joule-heat-sintered without external densification pressure to obtain a sintered functional material. Optional post-current treatment is performed. The two types of powders have the same, similar, or complementary compositions, and the overall composition after mixing corresponds to the target functional material. The pre-Joule-heat-treated powder provides the phase composition basis, while the ball-milling active powder participates in local reactions, particle bonding, and pore filling, achieving rapid and complete forming. The post-current treatment is used to regulate the phase composition, defect state, and functional properties. This method is applicable to the rapid preparation and optional post-treatment of inorganic functional materials.

A focusing electrode plate, an extraction mechanism, an ion beam generating device, and an ion implanter are disclosed. The focusing electrode plate includes a first channel penetrating the electrode body, through which ions pass. The radial cross-section of the first channel is elongated, and the depth of the first channel is greater than or equal to the width of the entrance end of the first channel. As the depth of the first channel increases, the potential difference between the entrance / exit of the first channel and the interior of the first channel increases, thereby improving the focusing effect on the ion beam.

The application relates to a negative voltage driving circuit and an energy storage system. The input end of the negative voltage driving circuit is connected with a power supply and a pulse driving signal respectively, and the negative voltage driving circuit comprises a first capacitor and a second capacitor. The second capacitor is connected with the gate of a P-type switch tube, and the P-type switch tube is connected between a battery and a conversion circuit. The negative voltage driving circuit is used for charging the first capacitor by the power supply when the pulse driving signal is a first level; and charging the second capacitor by the first capacitor when the pulse driving signal is a second level, so that the second capacitor generates a negative voltage. The second capacitor is also used for maintaining the negative voltage based on the stored electric energy of the second capacitor when the negative voltage driving circuit receives the pulse driving signal with the first level. The negative voltage driving circuit can pull up the driving voltage of the PMOSFET, so as to reduce the on-resistance and improve the reliability.

The invention provides a drill rod thread ultimate bearing capacity prediction model training method and system, and belongs to the field of oil drilling equipment, and the method comprises the steps: collecting sample points of a thread failure related parameter space; constructing a finite element model according to the sample points, and calculating limit torque values of the sample points based on the finite element model; constructing a training data set according to the failure related parameters and the limit torque values of the sample points; and inputting the training data set into the neural network model, and adjusting the neural network model by taking the physical information loss function as an optimization target to obtain a thread ultimate bearing prediction model. According to the method, a physical information loss function is used as a core optimization target, so that physical rule constraint is applied to a neural network predicted value, the learning process is forced to fit data and follow the basic principle of structural mechanics, and the generalization ability and prediction reliability of the model under the extrapolation working condition are remarkably improved.