多檢測器凝膠滲透色譜GPC付費測試:
GPC付費測試項目 | 測試編號 | 對外價格(含稅)RMB | 說明 |
首檔 | QFT-GPC-1 | 敬請來電 | · 其中如果首檔次收費標準可以通過產(chǎn)品線經(jīng)理、產(chǎn)品專家、售后部門、實驗室應用經(jīng)理/專家直接提供給有需求用戶 · 成熟方法樣品,普通有機流動相如THF,DMF,三檢測器檢測分子量和分子構象信息 · 成熟方法樣品,普通水相(NaNO3、Na2SO4、PBS等鹽類),三檢測器檢測分子量和分子構象信息 |
第二檔 | QFT-GPC-2 | 敬請來電 | · 第二檔次以上報價,包含方法摸索,需要產(chǎn)品線經(jīng)理、產(chǎn)品專家和實驗室應用經(jīng)理/專家協(xié)商*,然后通過售后部門給客戶報價 · 成熟方法樣品,有機體系流動相但屬于易制du流動相(需要備案),如甲苯,氯fang,丙酮等流動相,如客戶自行提供流動相可按照首檔收費。三檢測器檢測分子量和分子構象信息 · 成熟方法樣品,特殊水性流動相,如甲酸、乙酸水溶液等流動相。三檢測器檢測分子量和分子構象信息 · 需要復雜操作的SELS方式操作 |
第三檔 | QFT-GPC-3 | 敬請來電 | · 成熟方法樣品,有機體系流動相但是操作復雜流動相,如HFIP、DMSO、88%甲酸 |
方法摸索 | QFT-GPC-4 | 敬請來電 | · 用戶樣品復雜,沒有成熟方法,需要不同試劑摸索,需要不同樣品制備方式摸索,每摸索一個試劑條件,需要報價一次“方法摸索"費用 · 對于一些客戶提出的復雜實驗方案 |
典型分析樣品:
色譜柱 | 流動相 | 典型樣品 |
苯乙烯-二乙烯基苯 | THF | PS, PC, PVC, PBT, Nylon, XS(二甲苯溶出物),合成橡膠和,PLA(聚乳酸), PLGA,PVA,Polyaniline,PB(Polybutadiene),ABS樹脂,瀝青,聚對二氧環(huán)己酮,酚醛樹脂, 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,PAN,PVDF,PPS(戊聚糖多硫酸酯),Polyacrylamide (PAAm) ,Poly(N-isopropylacrylamide) (PNIPAm),dendrimer |
苯乙烯-二乙烯基苯 | DMAC | 纖維素 |
苯乙烯-二乙烯基苯 | 氯fang | PET, PBT,PLA |
苯乙烯-二乙烯基苯 | 甲苯 | 硅油,硅氧烷,天然橡膠 |
苯乙烯-二乙烯基苯 | HFIP | Nylon |
苯乙烯-二乙烯基苯 | DMSO | 原淀粉,纖維素 |
丙烯酸酯 | 水(NaNO3) | PEO/PEG,聚苯乙烯磺酸鈉,多糖,卡拉膠,水溶性纖維素系列,改性淀粉,透明質(zhì)酸(HA,*),肝素,右旋糖酐,DNA,蒲璐蘭多糖,果膠,阿拉伯膠,PAA(聚丙烯酸),等等 |
陽離子化樹脂 | 水(乙酸) | 殼聚糖,明膠,瓜爾膠等等 |
氧化硅 | 水(PBS) | 蛋白,IgG單抗,多肽,氨基酸 ,等等
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結果報告:
色譜原始譜圖,重復性進樣結果,分子量Mn,Mw,Mz和分子量分布PD,分子尺寸,Mark Houwink 曲線和參數(shù)
測試原理:
1.相對分子量
傳統(tǒng)GPC/SEC配備一個濃度檢測器(示差檢測器或者紫外檢測器),檢測每個組分的流出時間和濃度。通過傳統(tǒng)GPC檢測樣品的分子量,首先要通過一系列已知分子量的標準樣品,建立分子量對流出時間/體積的標定曲線(如下圖)。然后注入待測樣品,根據(jù)先前建立的標準曲線,計算出相對分子量Mw, Mn,Mz及其分布和 PD = Mw/Mn。
相對分子量檢測只需要一個RI檢測器和一套GPC前端分離系統(tǒng),特點是,結構簡單,但是相對分子量結果是相對于標準樣品,忽略了待測樣品和標準樣品之間的結構差異(主要是分子密度的差異),所以除非使用與待測樣品相同種類的標準樣品進行校正,其得到的相對分子量和其真實/分子量具有較大差異,其分子量分布和其真實分布之間也有所差別。如果使用不同種類的標準樣品進行色譜校正,將得到不同的相對分子量信息。色譜泵對于傳統(tǒng)校正方法的影響大,如果色譜泵工作不正常導致樣品流出時間改變,會影響檢測結果的重復性。
單一示差檢測器GPC大量存在于市場中,適合作為對于測試要求不高的檢測工具,尤其適合高分子生產(chǎn)過程中的QA/QC過程。
2.光散射和分子量:
光散射檢測器是檢測高分子分子量的直接方法。 高分子的散射光符合瑞利散射方程。在GPC樣品檢測濃度(極?。l件下,當趨近于0度角檢測散射光時,通過瑞利散射方程我們得到:
其中Rθ|θ→0是瑞利比,代表樣品散射光強度,K’是常數(shù),c是濃度,Mw是分子量
凝膠滲透色譜GPC設備中,經(jīng)常使用示差RI檢測器和光散射檢測器連用,RI檢測器提供每個分離組分的濃度信息,而光散射檢測器檢測散射光強,二者配合使用,檢測每一個流出組分的分子量信息。
光散射檢測器的使用,使得測試擺脫了對于標準樣品、校正曲線的依賴,并且其得到的分子量和分子量分布更加準確的和高分子的各種性能關聯(lián)起來。
3. 粘度檢測器
在線粘度檢測器被設計為惠斯通橋的結構。
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" />
其原理是測定樣品溶液通過四毛細管橋所產(chǎn)生的壓力差,從而得到每個被分離的組分的增比粘度,再通過濃度檢測器RI得到的組分濃度,我們可以計算出高分子的特性粘度,或者說分子密度的倒數(shù):
通過特性粘度,我們可以得到高分子的:特性粘度(IV),流體力學半徑 Rh(可小于1nm),Mark-Houwink曲線K和a值,分子結構信息,高分子構象信息(鏈折疊),高分子的支化度和支化頻率。
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