接触角
润湿
坐滴法
下降(电信)
材料科学
润湿转变
光学
工作(物理)
压扁
分析化学(期刊)
化学
复合材料
色谱法
热力学
物理
电信
计算机科学
作者
Rail Kadyrov,Irek I. Mukhamatdinov,Evgeny Statsenko
出处
期刊:Langmuir
[American Chemical Society]
日期:2023-02-14
卷期号:39 (8): 2966-2973
被引量:7
标识
DOI:10.1021/acs.langmuir.2c02702
摘要
Wettability is an important factor that controls the position and transport of fluids in the porous structure of a hydrocarbon reservoir. Direct quantitative wettability measurement methods include determination of the contact angle. One of the popular laboratory methods for its measurement is the sessile drop method, where the droplet of liquid is set onto the solid sample surface and the angle between liquid and solid phases in a 2D profile image is measured using a high-resolution optical subsystem. However, this method has disadvantages connected with three complex factors: two-dimensionality of data, image resolution, and the presence of wetting domains on the solid surface. In this work, we propose a method based on 3D μCT scanning of a sessile drop on the surface of a material and determination of the value of the wetting angle using drop height and volume measurements. Since the contact angle is not measured directly, this makes it possible to obtain averaged contact angle values for the entire droplet and leads to more stable values for individual measurements in comparison with the standard optical method. The results demonstrate that contact angles obtained using the μCT method differ from the values obtained by various fitting techniques of the 2D optical method: μCT-values are higher for hydrophilic interaction by 4-6° and lower for hydrophobic interaction by 12-17°. Such differences are associated with the lower susceptibility of the proposed method to the influence of the gravitational effect, which leads to a flattening of the droplet shape and distortion of angle measurements on the 2D projections. However, the described method showed close contact angles for water drops on a polytetrafluoroethylene surface with values obtained in microgravity and can be helpful for its prediction. It also has relatively low sensitivity to the absolute values of the drop volume and drop segmentation errors.
科研通智能强力驱动
Strongly Powered by AbleSci AI