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976 results about "Cell sheet" patented technology

Cell sheets can be prepared using cells from various biological tissues and organs*. These can be used alone as tissue for transplantation, or used in combination with sheets composed of other cells, and can be used either singly, or in stacked arrangements of multiple cell sheets.

Spread method of polycrystalline silicon solar cell

The invention relates to a spread method of a polycrystalline silicon solar cell. The spread method is characterized in that the spread method comprises the following processing steps of entering a boat, warming, oxidizing, spreading, redistributing, cooling and going out the boat, wherein the spreading step comprises low temperature pre-deposition and then high temperature spreading. Reaction between a phosphorus source and a silicon wafer cannot be completed under low temperature, so that the low temperature pre-deposition is carried out on low temperature source communication at a first step of spreading, the phosphorus source cannot spread (or conduct spreading with low rate) inside a silicon wafer, the phosphorus source only accumulates on the surface of the silicon wafer, and a phosphorus film with certain thickness is formed on the surface of the silicon wafer after source communication for certain time; and the high temperature spreading is carried out on high temperature source communication at a second step, phosphorus on the surface of an original silicon wafer is reacted with the silicon wafer and spreads to the inside of the silicon wafer, and spreading rates of the center point and the periphery of the silicon wafer are same. Therefore, spreading uniformity is good, concentration distribution of impurities on the surface of the silicon wafer and inside the silicon wafer body is even, sheet resistance uniformity is improved, and final photoelectric conversion efficiency of a cell sheet is improved accordingly.
Owner:JIANGYIN XINHUI SOLAR ENERGY

Cardiac muscle tonifying tablet taking acellular biological membrane as carrier as well as preparation method and application of cardiac muscle tonifying tablet

ActiveCN104436305AHelps maintain sterilityImprove decellularization efficiencyProsthesisCross-linkNutrition support
The invention discloses a cardiac muscle tonifying tablet taking an acellular biological membrane as a carrier as well as a preparation method and application of the cardiac muscle tonifying tablet. The cardiac muscle tonifying tablet taking the acellular biological membrane as the carrier is prepared by the following steps: carrying out decellularizing treatment on a natural biological membrane; then, cross-linking nutrient substances on the acellular biological membrane; planting target cells on the acellular biological membrane cross-linked with the nutrient substances; and cultivating a cell sheet which is preliminarily constructed, so that the cardiac muscle tonifying tablet taking the acellular biological membrane as the carrier is prepared. The cardiac muscle tonifying tablet disclosed by the invention is good in proliferation and differentiation activities and mechanical strength, and is sufficient in nutrition support; the cardiac muscle tonifying tablet is not only a breakthrough in tissue engineering but also suitable for the clinical treatment of myocardial infarction. The cardiac muscle tonifying tablet disclosed by the invention is reliable in principle, good in reproducibility, and suitable for standard production.
Owner:JINAN UNIVERSITY

Photovoltaic cell appearance defect classification method based on multi-channel residual neural network

The invention relates to a photovoltaic cell appearance defect classification method based on a multi-channel residual neural network. The method classifies the photovoltaic cell appearance defects based on a depth learning algorithm of a multi-channel input residual neural network. Firstly, the acquired photovoltaic cell sheet appearance image is preprocessed. 20% of that target image are randomly selected as a t sample set, the remaining target images are manually sorted, label are added, the size of the target images is quantized and multi-channel information in the target images is extracted, so that the training sample sets with fixed scales are obtained respectively, and the sample sets are verified. The training set is inputted into the residual neural network, and the multidimensional output eigenvalue matrix of the image is obtained. According to the extracted multi-dimensional eigenvalue matrix, the verification set image features are loaded into softmax classifier for classification, and the classification results are compared with the labels, and the test data and multi-dimensional eigenvalue matrix are loaded into the classifier to obtain the final classification. Thisapplication has high accuracy and high speed.
Owner:HEBEI UNIV OF TECH
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