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Viscosity of Fluid

Viscosity of Fluid

Viscosity measures the resistance a fluid offers to flow and deformation. It results from internal friction between layers of fluid moving relative to one another. Dynamic viscosity, denoted by the Greek letter mu (μ), quantifies the force needed to move one fluid layer over another. For Newtonian fluids like water and air, the relationship between the shearing stress and the rate of shearing strain is linear, meaning their viscosity remains constant regardless of the applied stress.

Viscosity

Viscosity

When water is poured into a glass, it falls freely and quickly, whereas if honey or maple syrup is poured over a pancake, it flows slowly and sticks to the surface of the container. This difference in the flow of different kinds of liquids arises due to the fluid friction between the liquid layers and the liquid and the surrounding material. This property of fluids is called fluid viscosity. In this example, water has a lower viscosity than honey and maple syrup.
The SI unit of viscosity is...

Surface Tension, Capillary Action, and Viscosity

Surface Tension, Capillary Action, and Viscosity

Surface Tension
The various IMFs between identical molecules of a substance are examples of cohesive forces. The molecules within a liquid are surrounded by other molecules and are attracted equally in all directions by the cohesive forces within the liquid. However, the molecules on the surface of a liquid are attracted only by about one-half as many molecules. Because of the unbalanced molecular attractions on the surface molecules, liquids contract to form a shape that minimizes the number...

Polymers: Molecular Weight Distribution

Polymers: Molecular Weight Distribution

For any given polymer, the weight average molecular weight (Mw) is higher than, if not equal to, the number average molecular weight (Mn). The only situation in which the weight average molecular weight and the number average molecular weight are equal is when a polymer consists only of chains with equal molecular weight. However, this never happens in a synthetic polymer, since it is difficult to control the polymerization process up to a molecular level with accuracy to a hundred percent.

Polymer Classification: Crystallinity

Polymer Classification: Crystallinity

Unlike ionic or small covalent molecules, polymers do not form crystalline solids due to the diffusion limitations of their long-chain structures. However, polymers contain microscopic crystalline domains separated by amorphous domains.
Crystalline domains are the regions where polymer chains are aligned in an orderly manner and held together in proximity by intermolecular forces. For example, chains in the crystalline domains of polyethylene and nylon are bound together by van der Waals...

Step-Growth Polymerization: Overview

Step-Growth Polymerization: Overview

Step-growth or condensation polymerization is a stepwise reaction of bi or multifunctional monomers to form long-chain polymers. As all the monomers are reactive, most of the monomers are consumed at the early stages of the reaction to form small chains of reactive oligomers, which then combine to form long polymer chains in the late stages. Hence, the reaction has to proceed for a long time to achieve high molecular weight polymers.
Many natural and synthetic polymers are produced by...

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Related Experiment Video

Updated: Nov 5, 2025

Molecular Entanglement and Electrospinnability of Biopolymers

Molecular Entanglement and Electrospinnability of Biopolymers

Published on: September 3, 2014

Dataset on viscosity and starch polymer properties to predict texture through modeling.

Reuben James Q Buenafe^1,2, Vasudev Kumanduri³, Nese Sreenivasulu¹

¹Grain Quality and Nutrition Center, International Rice Research Institute, Los Baños, Laguna 4031 Philippines.

|May 17, 2021

Summary

Developing a rice classification tool using pasting and starch properties can help breed varieties with better cooking and eating quality. This method aids both consumers and farmers in selecting superior rice grains.

Keywords:

Cooking and eating quality Indica Random forest model Rice properties

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Area of Science:

Agricultural Science
Food Science
Material Science

Background:

Consumer and farmer demand for rice varieties with superior eating and cooking quality is increasing.
Accurate classification tools are needed to screen rice varieties based on their quality attributes.
Pasting and starch structure properties are key indicators of rice quality.

Purpose of the Study:

To present data for a novel breeding tool that predicts rice cooking and eating quality.
To compile and graphically represent pasting, starch structure, sensory, and routine quality data of rice samples.
To demonstrate the data processing and acquisition methods for predicting rice texture.

Main Methods:

Compilation of pasting properties data (e.g., viscosity).
Analysis of starch structure properties.
Integration of sensory and routine quality data.
Graphical representation of all collected data.

Main Results:

Data visualization of key rice quality parameters.
Demonstration of data processing pipelines for quality prediction.
Establishment of a foundation for a predictive model for rice texture.

Conclusions:

The presented data and methods support the development of a novel breeding tool for rice quality assessment.
Viscosity and starch polymer properties are crucial for predicting cooking and eating quality.
This approach can accelerate the breeding of rice varieties with desirable texture characteristics.