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产品名称:

GPC付费测试服务/分子量测试

产品型号: 量通技术 产品时间: 2020-08-20
GPC付费测试服务/分子量测试原理:1. 相对分子量测试 通过传统GPC检测样品的分子量,首先要通过一系列已知分子量的标准样品,建立分子量对流出时间/体积的标定曲线,根据先前的标准曲线,计算出相对分子量Mw, Mn,Mz及其分布。 2. 光散射和绝对分子量测试: 高分子的散射光符合瑞利散射方程,在GPC样品检测浓度(极稀)条件下,当趋近于0度角检测散射光时,通过瑞利散射方程计算分子量。

产品概述

多检测器凝胶渗透色谱GPC付费测试              

GPC付费测试项目

测试编号

对外价格(含税)RMB

说明

首档

QFT-GPC-1

敬请来电 

·       其中如果首档次收费标准可以通过产品线经理、产品专家、售后部门、实验室应用经理/专家直接提供给有需求用户

·       成熟方法样品,普通有机流动相如THFDMF,三检测器检测绝对分子量和分子构象信息

·       成熟方法样品,普通水相(NaNO3Na2SO4PBS等盐类),三检测器检测绝对分子量和分子构象信息

第二档

QFT-GPC-2

敬请来电

·       第二档次以上报价,包含方法摸索,需要产品线经理、产品专家和实验室应用经理/专家协商*,然后通过售后部门给客户报价

·       成熟方法样品,有机体系流动相但属于易制du流动相(需要备案),如甲苯,氯fang,丙酮等流动相,如客户自行提供流动相可按照首档收费。三检测器检测绝对分子量和分子构象信息

·       成熟方法样品,特殊水性流动相,如甲酸、乙酸水溶液等流动相。三检测器检测绝对分子量和分子构象信息

·       需要复杂操作的SELS方式操作

第三档

QFT-GPC-3

敬请来电

·       成熟方法样品,有机体系流动相但是操作复杂流动相,如HFIPDMSO88%甲酸

方法摸索

QFT-GPC-4

敬请来电 

·       用户样品复杂,没有成熟方法,需要不同试剂摸索,需要不同样品制备方式摸索,每摸索一个试剂条件,需要报价一次“方法摸索”费用

·       对于一些客户提出的复杂实验方案

典型分析样品:

色谱柱

流动相

典型样品

苯乙烯-二乙烯基苯

THF

PS, PC, PVC, PBT, Nylon, XS(二甲苯溶出物),合成橡胶和,PLA(聚乳酸), PLGAPVAPolyanilinePBPolybutadiene),ABS树脂,沥青,聚对二氧环己酮,酚醛树脂, PCL,聚噻吩, Polyvinyl Alcohol (PVOH) Polyvinyl Pyridine (PVP) 等等

苯乙烯-二乙烯基苯

DMF

聚氨酯,聚丙烯酸脂系列,PANPVDFPPS(戊聚糖多硫酸酯),Polyacrylamide (PAAm) Poly(N-isopropylacrylamide) (PNIPAm)dendrimer

苯乙烯-二乙烯基苯

DMAC

纤维素

苯乙烯-二乙烯基苯

氯fang

PET, PBTPLA

苯乙烯-二乙烯基苯

甲苯

硅油,硅氧烷,天然橡胶

苯乙烯-二乙烯基苯

HFIP

Nylon

苯乙烯-二乙烯基苯

DMSO

原淀粉,纤维素

丙烯酸酯

水(NaNO3

PEO/PEG,聚苯乙烯磺酸钠,多糖,卡拉胶,水溶性纤维素系列,改性淀粉,透明质酸(HA,玻璃酸钠),肝素,右旋糖酐,DNA,蒲璐兰多糖,果胶,阿拉伯胶,PAA(聚丙烯酸),等等

阳离子化树脂

水(乙酸)

壳聚糖,明胶,瓜尔胶等等

氧化硅

水(PBS

蛋白,IgG单抗,多肽,氨基酸 ,等等

 

结果报告:

色谱原始谱图,重复性进样结果,绝对分子量MnMwMz和分子量分布PD,分子尺寸,Mark Houwink 曲线和参数

测试原理:

1.相对分子量

        传统GPC/SEC配备一个浓度检测器(示差检测器或者紫外检测器),检测每个组分的流出时间和浓度。通过传统GPC检测样品的分子量,首先要通过一系列已知分子量的标准样品,建立分子量对流出时间/体积的标定曲线(如下图)。然后注入待测样品,根据先前建立的标准曲线,计算出相对分子量Mw, MnMz及其分布和 PD = Mw/Mn  

      相对分子量检测只需要一个RI检测器和一套GPC前端分离系统,特点是,结构简单,但是相对分子量结果是相对于标准样品,忽略了待测样品和标准样品之间的结构差异(主要是分子密度的差异),所以除非使用与待测样品相同种类的标准样品进行校正,其得到的相对分子量和其真实/绝对分子量具有较大差异,其分子量分布和其真实分布之间也有所差别。如果使用不同种类的标准样品进行色谱校正,将得到不同的相对分子量信息。色谱泵对于传统校正方法的影响大,如果色谱泵工作不正常导致样品流出时间改变,会影响检测结果的重复性。

    单一示差检测器GPC大量存在于市场中,适合作为对于测试要求不高的检测工具,尤其适合高分子生产过程中的QA/QC过程。

 2.光散射和绝对分子量:

           光散射检测器是检测高分子绝对分子量的直接方法。 高分子的散射光符合瑞利散射方程。在GPC样品检测浓度(极稀)条件下,当趋近于0度角检测散射光时,通过瑞利散射方程我们得到:

 

        其中Rθ|θ→0是瑞利比,代表样品散射光强度,K’是常数,c是浓度,Mw是分子量

         凝胶渗透色谱GPC设备中,经常使用示差RI检测器和光散射检测器连用,RI检测器提供每个分离组分的浓度信息,而光散射检测器检测散射光强,二者配合使用,检测每一个流出组分的绝对分子量信息

        光散射检测器的使用,使得测试摆脱了对于标准样品、校正曲线的依赖,并且其得到的绝对分子量和分子量分布更加准确的和高分子的各种性能关联起来。

 

3. 粘度检测器

        在线粘度检测器被设计为惠斯通桥的结构。

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" />

       其原理是测定样品溶液通过四毛细管桥所产生的压力差,从而得到每个被分离的组分的增比粘度,再通过浓度检测器RI得到的组分浓度,我们可以计算出高分子的特性粘度,或者说分子密度的倒数:

  通过特性粘度,我们可以得到高分子的:特性粘度(IV),流体力学半径 Rh(可小于1nm),Mark-Houwink曲线Ka值,分子结构信息,高分子构象信息(链折叠),高分子的支化度和支化频率。

 

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