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Scientific Software Services :: Graph Plotting :: Graph Digitisation :: DSC Curve Solutions®

DSC Curve Solutions® #382

The world's first, the world's only.
DSC curve solutions:
determines the sample properties by fitting DSC curves with DCS curves.

Software, DSC Curve Solutions®, represents a novel approach for thermal analysis that allows you to simulate DSC curves vividly for experiments under any conditions for a range of given thermal events; so that you can extract the sample properties by fitting DSC curves with DCS curves. This software is now available to all the thermal analysts and polymer scientists or engineers to advance thermal analysis and polymer science and technology.

How can it be useful?

It obtains sample thermal properties by fitting FULL DSC curves with DCS curves - integrating interpretation and verification into one;

It obtains all the relevant thermal properties from any single DSC run; now you can determine curing and crystallisation kinetics from any single non-isothermal DSC run;

It covers a range of thermal events i.e. heat capacity, glass transition, enthalpy relaxation, melting and crystallisation, curing and reactions; furthermore its multi-component function allows you to deconvolute complex DSC curves readily;

It solves your headache when observing a "shifting" "baseline" before and after an endotherm or exotherm that makes your determination of the endotherm or exotherm inaccurate and inconsistent. DCS tells you why the "baseline" "shifts" and makes perfect fitting.

It covers DSC runs under any experimental conditions e.g. linear heating, sinusoidal (one or more frequencies) and/or saw-tooth modulation if you wish;

It is a perfect aid to learn and teach DSC and polymers - you will have a better understanding of the both.

No training, no tutorial and no help would be needed, you'll become an instant expert of DCS.

It is truly a revolution in DSC thermal analysis !
It is truely a revolution in Non-isothermal Kinetics ! It is truly a ...

How does it operate? As easy as A-B-C. Key in relevant parameters, click Run as shown in the following screenshot. It takes less than a minute for a single simulation using a PC with CPU 3.06 GHz or higher.	click to enlarge
Example 1a: Jessica has obtained experimental DSC curves (shown in black) for a polymer material. She uses DCS to fit the DSC curves after inputting all the experimental conditions such as mass of the sample, heating rate etc. into DCS; she then A) adjusts the specific heat capacity to match the plateau of DCS curves with that of DSC's; B) tunes the thermal transfer coefficient, l, which is an indicator of instrument performance, to fit the transient tail; C) finally she determines the melting thermodynamic parameters for the sample when she sees the curves are fitting to her satisfaction. In summary, by this DSC Curve Solutions curve fitting Jessica obtains: Specific heat capacity, c_p = 2.03 J/Kg, Melting peak temperature, T_m = 196°C, Half width of the Gaussian crystallite size distribution, m_m² = 250, Asymmetric factor of the Gaussian crystallite size distribution = 0.03, and the Thermal Transfer Coefficient, l = 0.0031 J/Ks.	click to enlarge
Example 1b: An iPP sample was heated from 0°C to 190°C at 10°C /min, then stayed isothermally for 10 min, followed by a cooling at 5°C/min to 30°C, with its DSC curve shown in black line. After inputting all the known experimental parameters, one tuned relevant parameters to fit the DSC curve with the DCS curve to satisfaction, leading to determination of following parameters: Specific heat capacity, c_p = 1.1 J/Kg at 0°C, ramping linearly towards 1.66 J/Kg at 200°C; Melting enthalpy, ΔH = 95.22 J/g, Melting peak temperature, T_m = 161.5°C, Half width of the Gaussian crystallite size distribution, m_m² = 65, Asymmetric factor of the Gaussian crystallite size distribution = -0.06, Crystallisation rate factor, Ak=0.024, Maximum crystallisation rate temperature Tmax = 118.5; half width of the crystallisation rate distribution, m_k² =95, and Avrami index, n = 4. Furthermore, assuming the melting enthalpy, ΔH for 100% crystallised iPP is 207 J/g, one obtains the crystallinitity curve as well.	click to enlarge
Example 1c: The DSC curve for a multi-domain immunoglobulin G (IgG) protein shows two denaturasation (melting) endotherms slightly overlapping (black curve). Using DCS v2.0, Veronica readily deconvoluted the DSC curve and obtained following parameters: Domain 1: Denaturation enthalpy, ΔH = 12.5 J/g, peak temperature, T_m = 61.2°C, Half width of the Gaussian distribution, m_m² = 12, Asymmetric factor of the Gaussian distribution = -0.065, Domain 2: Denaturation enthalpy, ΔH = 5.4 J/g, peak temperature, T_m = 71.7°C, Half width of the Gaussian distribution, m_m² = 37, Asymmetric factor of the Gaussian distribution = 0.	click to enlarge
Example 1d: Adam wants to known the kinetic parameters for an epoxy resin. He heats the epoxy to 290 °C at 5 °C/min, and obtains curing exotherm DSC curve shown in black. Using the autocatalytic model, Adam readily determines the kinetic parameters of the epoxy resin by fitting this SINGLE run DSC curve (black) with the DCS curve (red) to satisfaction. Curing enthalpy, ΔH = -205 J/g; k₁₀ =0 (Activation energy, E_a1 can be any in this case); Frequency factor, k₂₀ = 12200 s^-1; Activation energy, E_a2 = 51450 J/mol; Exponent, m = 0.60; Exponent, n = 1.45	click to enlarge
Example 1e: James has obtained a group of enthalpy relaxation DSC curves (shown in black) for a polymer material, with the quenched and that after 30, 200 and 2000 min. ageing being shown in the figure. He then uses DCS to fit the DSC curves using following paramters: Relaxation enthalpy, ΔH = 5.7 J/g; Frequency factor, k₀ = 1.25 s^-1; Activation energy, E_a = 380 J/mol; Exponent, m = 0.50; Exponent, n = 0.5 In particularly, James has amazingly found the four DSC curves correspond to 100%, 75%, 40, and 0% degree of relaxation respectively (or 0%, 25%, 60% and 100% degree of ageing).	click to enlarge
Example 2: Catherina has a semi-crystalline polymer with 20% initial crystallinity and the ultimate crystallinity being 80%. She runs DSC experiment at 5°C/min from 20 °C to 340 °C, holds isothermally for 10 min, cools down to 10 °C at 15 °C/min and finally heats it up to 340 °C at 10 °C/min. She obtains the numerical experimental DSC curve and the crystallinity curve as follows: (view image)	click to enlarge
Example 3: Peter is an instrumentalist interesting in temperature modulation. He runs Catherina's experiment with a sinusoidal modulation with amplitude and period being 1.0 °C and 45 seconds respectively. He further superimposes a saw-tooth modulation with amplitude and period being 1.0°C/min and 240 seconds on the sinusoidal modulation. He obtains following DSC curves (Catherina's curves shown in Example 2 are superimposed in the screenshot).	click to enlarge
Example 4: DSC has been working for Steve years and years. Steve wants now to show how the Thermal Transfer Coefficient (TTC), λ, has been working for DSC. He runs 3 simulations with different λ values for a given sample under a given set of DSC conditions. As shown in the following figure, the DSC curves vary with λ due to the intrinsic transient effect of DSC measurements that is undesirable. Steve further assumes a sample with step up and a step down changes in its specific heat capacity over a temperature range. He obtains the numerical experimental DSC curves shown below. Steve thus concludes a high λ is desirable to obtain DSC curves with less distortion.	click to enlarge click to enlarge
Example 5: Similar to Steve, Fiona wants to examine the effect of reference heat capacity, i.e. the DSC cell pans. She has obtained the numerical experimental DSC curves as follows. What do these curves tell us ?	click to enlarge

DSC Curve Solutions® Pricing

Commercial Single User License	$ 1,200.00
Academic Single User License	$ 960.00

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Price: $960.00 (€ 864.00)

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