doi:10.11777/j.issn1000-3304.2020.20234

引用本文: 陈咏萱, 周东山, 胡文兵. 示差扫描量热法进展及其在高分子表征中的应用[J]. 高分子学报, 2021, 52(4): 423-444. doi: 10.11777/j.issn1000-3304.2020.20234 shu

Citation: Yong-xuan Chen, Dong-shan Zhou and Wen-bing Hu. Progress of Differential Scanning Calorimetry and Its Application in Polymer Characterization[J]. Acta Polymerica Sinica, 2021, 52(4): 423-444. doi: 10.11777/j.issn1000-3304.2020.20234 shu

示差扫描量热法进展及其在高分子表征中的应用

南京大学化学化工学院配位化学国家重点实验室　南京 210023

作者简介: 胡文兵，男，1966年生. 南京大学化学化工学院高分子系教授、博士生导师. 1989年本科毕业于复旦大学材料科学系，1995年博士毕业于复旦大学高分子科学系. 分别于1998~1999年赴德国弗莱堡大学物理系、2000~2001年美国田纳西大学化学系、2001~2003年荷兰物质科学研究院(FOM)原子与分子物理研究所从事博士后研究. 2004年至今，在南京大学任教. 2008年获杰出青年科学基金资助，2020年入选美国物理学会会士(APS Fellow). 主要研究方向为采用蒙特卡洛分子模拟和Flash DSC研究高分子结晶机理及材料热导率表征;

通讯作者: 胡文兵, E-mail: wbhu@nju.edu.cn

摘要: 示差扫描量热法(DSC)是表征材料热性能和热反应的一种高效研究工具，具有操作简便、应用广泛、测量值物理意义明确等优点. 近年来DSC技术的发展大大拓展了高分子材料表征的测试范围，促进了对高分子物理转变的热力学和动力学的深入研究. 温度调制示差扫描量热法(TMDSC)是DSC在20世纪90年代的标志性进展，它在传统DSC的线性升温速率的基础之上引入了调制速率，从而可将总热流信号分解为可逆信号和不可逆信号两部分，并能测量准等温过程的可逆热容. 闪速示差扫描量热法(FSC)是DSC技术近年来的创新性发展，它采用体积微小的氮化硅薄膜芯片传感器替代传统DSC的坩埚作为试样容器和控温系统，实现了超快速的升降温扫描速率以及微米尺度上的样品测试，使得对于高分子在扫描过程中的结构重组机制的分析以及对实际的生产加工条件的直接模拟成为可能. 本文从热分析基础出发，依次对传统DSC、TMDSC和FSC进行了介绍，内容覆盖其发展历史、方法原理、操作技巧及其在高分子表征中的应用举例，最后对DSC未来的发展和应用进行了展望. 本文希望通过综述DSC原理、实验技巧和应用进展，帮助读者加深对DSC这一常用表征技术的理解，进一步拓展DSC表征高分子材料的应用.

关键词:

English

Progress of Differential Scanning Calorimetry and Its Application in Polymer Characterization

State Key Lab of Coordination Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023

Corresponding author: wbhu@nju.edu.cn

Received Date: 2020-10-26
Accepted Date: 2020-11-17
Available Online: 2021-04-03

Abstract: Differential scanning calorimetry (DSC) is a highly efficient tool to characterize the thermal properties and to investigate thermal reactions of materials owing to its advantages of simplicity and universality as well as the well-defined measurement results with a clear physical meaning. In recent years, the tremendous development of DSC technique has greatly extended the measurement range for polymer characterization, facilitating the further research on thermodynamics and kinetics of physical transitions in polymer materials. Temperature-modulated DSC (TMDSC), a remarkable advance in DSC technique in 1990s, introduces scanning rate perturbation into the traditional linear heating rate so that the overall heat flux could be separated into reversible and non-reversible signals and furthermore, the reversing heat capacity could be measured in the quasi-isothermal process. Fast scanning chip-calorimetry (FSC) is a new progress of DSC technique in recent years, which adopts a miniature chip made of silicon nitride thin films to replace the traditional crucible as the sample holder and temperature controller, thus achieving ultra-fast heating and cooling rates for the measurement with sample mass on micro- and nanogram scale, which makes it possible to analyze the structural reorganization phenomena occurring frequently during temperature scanning and to simulate the actual condition of polymer processing. This review starts with the fundamentals of thermal analysis, followed with a sequential introduction of DSC, TMDSC and FSC by covering their histories, principles, experimental skills as well as their practical examples of polymer characterization. In the end, the prospects of the development and application of DSC are highlighted. We hope this survey would help the readers to gain a deeper understanding of the commonly used DSC technique and encourages them to expand further applications of DSC techniques in polymer characterization.

Key words:

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Figure 1. Illustration of heat-flux DSC (Mettler-Toledo heat-flux DSC) with the heating rate controlled through the furnace temperature. There are two sets of thermocouples measuring the heat flow between the furnace and the pan for sample and reference and two central terminals bringing the average T signal from all the thermocouples out to the computer.

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Figure 2. Illustration of power-compensation DSC as invented by Perkin Elmer with the reference and the sample separately heated by two platinum resistance thermometers in two calorimeters mounted in a constant temperature block.

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Figure 3. Illustration of the calibration of temperature and heat-flow rate with the standard material Indium for DSC measurement. The curve is characterized by its baseline and the endothermic process with some characteristic temperatures including the beginning of melting, T_b, the extrapolated onset of melting, T_m, the peak temperature, T_p, and the end of melting where the baseline is finally recovered, T_e. Generally, T_m is the most reproducible point as an accurate measure of the equilibrium temperature which are used for the temperature calibration. The peak area below the baseline can be compared with the expected fusion heat of standard materials for the calibration of the heat flow rate.

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Figure 4. Heat flow curves of standard sapphire and unknown specimens where D_s(mW) is the vertical displacement between the baseline and the specimen DSC thermal curves at a given temperature while D_st(mW) is vertical displacement between the baseline and the sapphire DSC thermal curves at a given temperature.

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Figure 5. The heat-flow rate (the upper curve in the left axis) and its derivative (the lower curve in the right axis) curves in the glass transition region with some characteristic temperatures including the beginning of glass transition T_b, the extrapolated onset temperature T_b1, the midpoint temperature T_g, the inflection temperature T_i, the extrapolated end temperature T_e1 and the temperature of return-to-baseline T_e as listed. The glass transition is determined by T_g (°C)—the point on the thermal curve corresponding to the half of the heat flow difference between the extrapolated onset and extrapolated end.

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Figure 6. Typical temperature profile of sinusoidal TMDSC (blue and solid curve) and its underlying heating rate curve with $ \;{\beta }_{0} $ of 1 K·min⁻¹ (red and dashed line). The amplitude of modulation A_T is 0.5 K, the period of modulation $ {t}_{{\rm{p}}} $ is 60 s. (Reprinted with permission from Ref.[24]; Copyright (2009) Polymer Bulletin)

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Figure 7. The heat-flow curve measured by the sinusoidal temperature-modulated DSC (Reprinted with permission from Ref.[24]; Copyright (2009) Polymer Bulletin)

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Figure 8. The total heat-flow curve of a sinusoidal TMDSC curve. (Reprinted with permission from Ref.[24]; Copyright (2009) Polymer Bulletin)

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Figure 9. Typical temperature profile for sawtooth TMDSC (solid line) and its deconvoluted underlying heating rate $ \;{\beta }_{0} $ of 1 K·min⁻¹and the reversing rate of temperature change of ±3 K·min⁻¹ (dashed lines). T₁and T₂ indicate the beginnings and ends of the cycles, respectively. (Reprinted with permission from Ref.[35]; Copyright (2014) Springer Nature)

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Figure 10. Illustration of the linear thermal response (solid lines) for the temperature profile of Fig 11. The lightly dotted boxes and the heavily dotted boxes separately indicate the underlying and the reversing responses. The heavy line represents the heat flow rate $\textit{ϕ} \left(t\right)$. The pseudo-isothermal level (Ps), the zero level (0) and the value of upper and lower limits of the heat flow rate ${\textit{ϕ} }_{{\rm{h}}}$ and ${\textit{ϕ} }_{\mathrm{c}}$ are marked, respectively. (Reprinted with permission from Ref.[35]; Copyright (2014) Springer Nature)

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Figure 11. Illustration of the nonlinear thermal response in each cycle measured by sawtooth TMDSC where HF_h and HF_c separately represent the heat flow rate measured in the heating and cooling half cycles.

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Figure 12. The heat capacity curves of poly(ethylene terephthalate) (PET) measured by sawtooth TMDSC with temperature profile of Fig. 11. The heat flow data is analyzed with the standard DSC method: reversing heat capacity from Eq. (26), total heat capacity from Eq. (25), non-reversing heat capacity from the difference between total and reversing heat capacity, and imbalance of heat capacity from Eq. (28). Also listed are the ATHAS data bank data for the heat capacity of amorphous PET. (Reprinted with permission from Ref.[35]; Copyright (2014) Springer Nature)

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Figure 13. The apparent heat capacity curves of PET during the heating process after crystallized by cooling from the melt to 44% crystallinity. The standard DSC curve and TMDSC curve are separately with intermediate and heavy thickness. Also plotted are the data-bank information (thin line) and the computed heat capacity for the sample of 44% crystalline PET (broken line). (Reprinted with permission from Ref.[36]; Copyright (2014) Elsevier)

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Figure 14. Illustration of reversible premelting on the fold-end surface of polymer lamellar crystals. There exists a local force balance between the recovery tendency of the stretched loops and the thickening tendency of the lamellar crystals (see arrows). (Reprinted with permission from Ref.[39]; Copyright (2014) American Chemical Society).

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Figure 15. (a) The heat-flow rate curve (the black curve in the right axis) of the doped iPP as a response to the temperature-modulation program (the red curve in the left axis) with the frequency 12.5 Hz, the amplitude ±1 K and the baseline annealing temperature 398 K. (b) Frequency dependences of specific reversing heat capacities of raw and doped iPP samples measured by sawtooth TMDSC. The dashed line represents the standard specific vibrational heat capacity for iPP melt at 398 K that is cited from the literature [41]. (Reprinted with permission from Ref.[40]; Copyright (2014) Elsevier)

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Figure 16. TMDSC measurement with the underlying heating rate 2 K·min⁻¹, modulation period 80.5 s, and modulation amplitude 1.0 K for PS after annealing for 240 min at 353.15 K in order to separate the reversing and non-reversing contributions to the apparent heat capacity in the glass transition temperature region. Left figure: Modulated heat flow, the sliding averages, and the evaluated reversing and non-reversing heat capacities; Right figure: Expanded scale drawings of the three sliding averages. (Reprinted with permission from Ref.[42]; Copyright (2014) Elsevier)

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Figure 17. Specific reversing heat capacity curves of PLA-H cooled from 373 K to 283 K in TMDSC at different modulation frequencies. The underlying cooling rate is 0.1 K·min⁻¹, and the maximum cooling rate A_Tω remains at π/100 with the modulation amplitude ranging from 0.05 K to 0.5 K and the modulation period ranging from 10 s to 100 s resulting in a wide range of modulation frequency from 0.01 Hz to 100 Hz. (Reprinted with permission from Ref.[43]; Copyright (2014) American Chemical Society)

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Figure 18. The photographs of Flash DSC1 apparatus. Top left: Flash DSC1; Top right: the unloaded chip sensor UFS1; Bottom left: the sample transfer; Bottom right: the membrane of the sample or reference cell on sensor. (Reprinted with permission from METTLER-TOLEDO Company)

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Figure 19. Apparent heat capacity curves of C66 samples obtained on heating at 3000 K·s⁻¹ after cooled at −10 K·s⁻¹ from a stay of 0.2 s at different erasing temperatures ranging from 180 ℃ to 210 ℃ (Reprinted with permission from Ref.[70]; Copyright (2014) Elsevier)

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Figure 20. Apparent heat capacity curves of V30G sample obtained on cooling at various rates as labeled (Reprinted with permission from Ref.[60]; Copyright (2014) Springer Nature).

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Figure 21. Apparent heat capacity curves of V30G sample obtained on heating at various rates as labeled (Reprinted with permission from Ref.[60]; Copyright (2014) Springer Nature)

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Figure 22. Comparison of temperature dependence of crystallization half-times of PA and PK during isothermal crystallization process at various crystallization temperatures (Reprinted with permission from Ref.[73]; Copyright (2014) John Wiley and Sons)

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Figure 23. Summary of temperature dependence of crystallization half-times of PA46, PA66, PA610, PA612, PA1012 and PA12 during isothermal crystallization processes at various temperatures (Reprinted with permission from Ref.[75]; Copyright (2014) Elsevier)

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Figure 24. (a, b) FSC measurement of power law relationships between apparent superheating T_m,onset−T_c and heating rates h for α-crystals and β-crystals of iPP prepared at three crystallization temperatures T_c as labeled. (c) Mote Carlo simulations of power law relationship between apparent superheating T_m,onset−T_c and heating rates h for lamellar iPP crystals with different chain mobility characterized by E_f/E_c and different crystallization temperatures T_c as labeled (Reprinted with permission from Ref.[78]; Copyright (2014) Elsevier)

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Figure 25. (a) Heat flow curves of PLLA crystals after annealing at 152 °C for various periods from 0 s to 600 s; (b) AFM height image of nascent PLLA crystals; (c) AFM height image of PLLA after annealed at 152 °C for 1000 s (Reprinted with permission from Ref.[85]; Copyright (2014) Elsevier)

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Figure 26. Reciprocals of the relaxation time (left axis, pentagons) and cooling rate (right axis, stars) as functions of the inverse of temperature and fictive temperature for PtBs samples at different length scales. The solid lines are VFT fits for the relationship between relaxation time (or cooling rate) and fictive temperature. The confinement-length dependence of fictive temperature at different cooling rates is presented in the inset where the dashed and solid lines are linear fits of the length-scale-dependent fictive temperature measured at high and low cooling rates, respectively. (Reprinted with permission from Ref.[87]; Copyright (2014) American Physical Society)

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Figure 27. (a) Top: Illustration of two indium particles separately placed on the top of a regular-shaped sample and on the surface of the reference cell. Bottom: the photographs of the sample cell and the reference cell. (b) Temperature profile for isothermal crystallization and subsequent melting of the samples. (c) Apparent heat capacity curves of Nylon 46 at various heating rates as labeled and the exothermal peak and endothermal peak indicate separately the melting of the indium on the reference cell and on the top of sample Nylon 46. (d) Melting point differences of two indium particles at various heating rates for three Nylon samples (Reprinted with permission from Ref.[90]; Copyright (2014) Elsevier)

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Figure 28. Illustration of time scales of fast-scan chip-calorimetry measurement and Monte Carlo simulation towards the identical time window of polymer crystallization and melting (Reprinted with permission from Ref.[91]; Copyright (2014) Springer Nature)

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