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    专

    基于DIP-DIP的试剂盒及其在混合样本鉴识和祖源推断上的应用

    VN214531154A1
    发明人
    朱波峰, 沈春梅, 兰琼
    受让人
    UNIV SOUTHERN MEDICAL
    申请人
    어플라이드 머티어리얼스 인코포레이티드
    申请号
    521039
    申请日
    2021-02-25
    公开(公告)号
    VN214531154A1
    公开(公告)日
    1982-09-07
    IPC分类号
    B23Q003/157
    CPC分类号
    -
    优先权号
    681022
    064722
    优先权日
    2000-11-27
    2002-08-08
    摘要

    A moulding cpd. comprises a mixt. of (a) 5-90 wt.% of epoxy resin having more than one 1,2-epoxy gp. per mol; (b) 0.001-5 wt.% of metal acetylacetonate having solely metal to oxygen bonds; (c) 0.025-30 wt.% of phenolic accelerator; and (d) 10-95 wt.% of filler.

    The compsn. may be in solid or liq. form and has a rapid cure rate and good storage stability. It has good strength, shrinkage and other physical properties.

    Pref. the filler is a fibrous material and comprises 50-95 wt.% of the compsn.; (b) comprises 0.5-3 wt.% and the metal is Al, Ti, Zn or Zr. Pref. the cpd. also contains a fire retardant and a mould release agent.

    权利要求
    1 - 21 . (canceled)
    22 . A system for identifying perishable products in a retail store based on analysis of image data and for automatically generating offers relating to the identified products, the system comprising: at least one processor configured to: receive a set of images depicting a plurality of perishable products of at least one product type displayed on at least one shelving unit in a retail store; analyze the set of images to determine information about a displayed inventory of the plurality of perishable products; access a database storing information about the at least one product type; use the information about the displayed inventory and the stored information about the at least one product type to determine at least one offer associated with the at least one product type; and provide the at least one offer to at least one customer of the retail store.
    23 . The system of claim 22 , wherein the information about the displayed inventory includes quality indicators for the plurality of perishable products, and wherein the at least one processor is configured to: determine the quality indicators for the plurality of perishable products based on an analysis of the set of images; and use the determined quality indicators for the plurality of perishable products to determine the at least one offer associated with the at least one product type.
    24 . The system of claim 23 , wherein the at least one processor is configured to use the determined quality indicators to determine a shelf-life estimation of the plurality of perishable products, and to determine the at least one offer associated with the at least one product type based on shelf-life estimation of the plurality of perishable products.
    25 . The system of claim 22 , wherein the at least one processor is configured to identify a specific customer that will likely to be interested in discounted products from the at least one product type.
    26 . A computer program product for identifying perishable products in a retail store based on analysis of image data and for automatically generating offers relating to the identified products embodied in a non-transitory computer-readable medium and executable by at least one processor, the computer program product including instructions for causing the at least one processor to execute a method comprising: receiving a set of images depicting a plurality of perishable products of at least one product type displayed on at least one shelving unit in a retail store; analyzing the set of images to determine information about a displayed inventory of the plurality of perishable products; accessing a database storing information about the at least one product type; using the information about the displayed inventory and the stored information about the at least one product type to determine at least one offer associated with the at least one product type; and providing the at least one offer to at least one customer of the retail store.
    27 . The computer program product of claim 26 , wherein the stored information includes information about additional perishable products from the at least one product type available in a storage area of the retail store, and the method further includes: using the information about the displayed inventory and the information about the additional perishable products available in the storage area to determine the at least one offer associated with the at least one product type.
    28 . The computer program product of claim 26 , wherein the stored information includes information about additional perishable products from the at least one product type scheduled to be delivered to the retail store, and the method further includes: using the information about the displayed inventory and the information about the additional perishable products scheduled to be delivered to the retail store to determine the at least one offer associated with the at least one product type.
    29 . The computer program product of claim 26 , wherein the stored information includes costs associated with the at least one product type, and the method further includes: using the information about the displayed inventory and the costs associated with the at least one product type to determine the at least one offer associated with the at least one product type.
    30 . The computer program product of claim 26 , wherein the stored information includes a current price for the at least one product type, and the method further includes: using the information about the displayed inventory and the current price for the at least one product to determine the at least one offer associated with the at least one product type.
    31 . The computer program product of claim 26 , wherein the stored information includes details of at least one previous sale of products from the at least one product type, and the method further includes: using the information about the displayed inventory and the details of the at least one previous sale to determine the at least one offer associated with the at least one product type.
    32 . The computer program product of claim 31 , wherein the details of at least one previous sale includes demographic characteristic of customers that bought the at least one product type, and wherein the method further includes: providing the at least one offer to at least one customer of the retail store based on a predetermined demographic characteristic associated with the at least one customer.
    33 . The computer program product of claim 26 , wherein the method further includes: receiving one or more additional images depicting the at least one shelving unit in the retail store after the at least one offer was provided to at least one customer; analyzing the one or more additional images to determine current inventory of products from the at least one product type; when the current inventory of products from the at least one product type is less than a threshold, informing the at least one customer that the offer is expired.
    34 . The computer program product of claim 26 , wherein the method further includes: receiving one or more additional images depicting the at least one shelving unit in the retail store after the at least one offer was provided to the at least one customer; analyzing the one or more additional images to determine a current inventory of products of the at least one product type; using the current inventory of products of the at least one product type and the stored information to determine at least one updated offer associated with the at least one product type; and providing the at least one updated offer to at least one customer.
    35 . The computer program product of claim 26 , wherein the method further includes: obtaining information indicative of products from multiple product types that a customer is likely to buy; using the information about the displayed inventory, the stored information about the at least one product type, and the information indicative of products that the customer is likely to buy to determine a personalized offer for the customer; and providing the personalized offer for the customer.
    36 . The computer program product of claim 35 , wherein the method further includes: determining the information indicative of products from multiple product types that the customer is likely to buy based on cashier data.
    37 . The computer program product of claim 35 , wherein the method further includes: determining the information indicative of products from multiple product types that the customer is likely to buy based on image analysis of products found in the customer's cart.
    38 . The computer program product of claim 26 , wherein providing an offer to a customer includes transmitting a message to a mobile communication device associated with the customer to display information related to the offer.
    39 . The computer program product of claim 26 , wherein providing an offer to a customer includes causing an augmented reality system associated with the customer to display information related to the offer.
    40 . The computer program product of claim 26 , wherein providing an offer to a customer includes causing a display attached to a cart associated with the customer to display information related to the offer.
    41 . A method for identifying perishable products in a retail store based on analysis of image data and for automatically generating offers relating to the identified products, the method comprising: receiving a set of images depicting a plurality of perishable products of at least one product type displayed on at least one shelving unit in a retail store; analyzing the set of images to determine information about a displayed inventory of the plurality of perishable products; accessing a database storing information about the at least one product type; using the information about the displayed inventory and the stored information about the at least one product type to determine at least one offer associated with the at least one product type; and providing the at least one offer to at least one customer of the retail store.
    42 - 221 . (canceled)
    说明书
    [0001]一种膜状和定向绳状双壁纳米碳管的制备方法
    [0002]技术领域:
    [0003]本发明涉及一种纳米碳管的制备方法,适用于制备膜状和定向绳状双壁纳米碳管。
    [0004]背景技术:
    [0005]自从1991年日本科学家发现多壁纳米碳管以来,由于其独特的一维纳米结构和许多优异的性能,一直是国内外物理化学界和材料学界研究的前沿和热点。
    [0006]经过各国科学家十年多的努力,在纳米碳管制备方面虽然已取得了一些成果,如已基本能够实现纳米碳管的大量制备和多壁纳米碳管的定向生长等,但仍然面临着许多困难和挑战:如何提高纳米碳管的纯度和如何实现纳米碳管的可控制备(层数、直径、导电性及单壁纳米碳管的定向生长等),这些都阻碍了人们对纳米碳管结构、性能及生长机理的进一步认识。如果在制备过程中能够实现碳管的层数,直径和螺旋性可控,那么在科学上,将会带来新的物理现象,并且这些结构可以有望直接作为纳米电子器件应用。
    [0007]现有的制备双壁纳米碳管的方法主要有两种:单壁纳米碳管中C60热聚法和氢电弧法。第一种方法中由于在范德瓦尔兹力限制下,只有特定直径的单壁纳米碳管才能生成双壁纳米碳管,因此纯度和产量低,并且由于受填充的C60多少和密度分布的限制,通常生成的双壁纳米碳管是不完整的;由于氢电弧法是不平衡的反应过程,因此产物纯度变化大,且不利于连续化及大规模生产,并且在该过程中所生成的大量石墨片在后处理中不容易去除,影响了双壁纳米碳管本征性能的测定及应用。
    [0008]发明内容:
    [0009]本发明的目的在于提供一种大量制备高纯度、高质量膜状和定向绳状双壁纳米碳管的新方法,该方法具有设备简单,操作容易,能耗低,产物纯度高、可控性高及有望连续、大量生产等优点,因此可作为一种适于可控制备的理想方法。
    [0010]本发明提供了一种高纯度、高质量膜状和定向绳状双壁纳米碳管的制备方法,该方法采用碳源、催化剂、缓冲气体和含硫生长促进剂,在气态下充分混合均匀,然后输入反应区生成双壁纳米碳管,其中:
    [0011]碳源为甲烷、乙烷、苯;
    [0012]缓冲气体为氢气、氩气、氮气之一种或数种的混合气体,碳源与缓冲气体的摩尔比在0.0778-0.156范围内;
    [0013]催化剂为二茂铁,催化剂与碳源的摩尔比为1/200-2/5,优选为3/100-3/10;
    [0014]含硫生长促进剂为噻吩或H2S,其中,含硫生长促进剂与碳源的摩尔比为3/1000-1/10,优选为1/100-3/50;
    [0015]所用升温速率为10-35℃/min,优选为20-30℃/min;
    [0016]达到最终反应温度1050-1250℃,保温5-60min,优选为5-20min;
    [0017]本发明的特点:1.通过控制各反应参数,使生成的纳米碳管为双壁纳米碳管;2.以氢气作为缓冲气体,减少杂质的生成,提高产物的纯度;3.通过对反应区温度分布的控制,减少热解碳的生成及覆盖在所生成的双壁纳米碳管的表面,提高产物的纯度;4.大流量的反应气体在反应体内的流动,其方向与双壁纳米碳管的取向一致,在范德华力的作用下气流携带的双壁纳米碳管相互聚集,于是可以获得较好取向的双壁纳米碳管绳。具体过程如下:金属有机化合物被加热形成气态,连同小流量碳源带来的低温下挥发的硫的碳氢化合物或小流量硫化氢气体,及碳源和缓冲气体混合均匀并预热后,一起导入反应区,在较低温度下金属有机化合物就会分解,游离出金属原子,在气流的作用下,游离的金属原子碰撞,形成一定尺寸的颗粒时,就会与碳源分子发生作用使碳氢化合物分解,经过溶解、扩散过程,在催化剂颗粒的另一侧结晶析出,生成纳米碳管。如果反应条件适宜,则可以生成双壁纳米碳管,其产物为绳状和半透明膜状两种。
    [0018]附图说明:
    [0019]图1.双壁纳米碳管绳的扫描电镜照片(60μm)。
    [0020]图2.双壁纳米碳管绳的扫描电镜照片(10μm)。
    [0021]图3.双壁纳米碳管的透射电镜照片。
    [0022]图4双壁纳米碳管的高分辨照片。
    [0023]具体实施方式:
    [0024]实施例1
    [0025]碳源甲烷流量250sccm,载气氢气流量1800sccm,带含硫生长促进剂噻吩的甲烷流量32sccm,催化剂为二茂铁,称量1.0g。
    [0026]反应区快速升温到300℃,以20-30℃/min升温到1100℃,保温5min。
    [0027]产物呈绳状和膜状,电镜下观察表明,产物为双壁纳米碳管。
    [0028]实施例2
    [0029]碳源甲烷流量250sccm,载气氢气流量1800sccm,带含硫生长促进剂噻吩的甲烷流量32sccm,催化剂为二茂铁,称量2.0g。
    [0030]反应区快速升温到300℃,以20-30℃/min升温到1100℃,保温10min。
    [0031]产物呈绳状和膜状,电镜下观察分别如图1、2、3所示,可以看到产物非常纯净,且双壁纳米碳管绳有较好的取向性;高分辨观察表明,产物为双壁纳米碳管(图4);其中双壁纳米碳管内、外径分别在0.8-2.8nm和1.6-3.6nm的范围内。
    [0032]实施例3
    [0033]碳源甲烷流量280sccm,载气氢气流量1800sccm,硫化氢流量3sccm,催化剂为二茂铁,称量2.0g。
    [0034]反应区快速升温到300℃,以20-30℃/min升温到1100℃,保温10min。
    [0035]产物呈绳状和膜状,电镜下观察表明,产物为双壁纳米碳管。
    [0036]实施例4
    [0037]碳源乙炔流量140sccm,载气氢气流量1800sccm,带含硫生长促进剂噻吩的甲烷流量32sccm,催化剂为二茂铁,称量2.0g。
    [0038]反应区快速升温到300℃,以20-30℃/min升温到1140℃,保温10min。
    [0039]产物呈绳状和膜状,电镜下观察表明,产物为双壁纳米碳管。
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