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Related Concept Videos

Mechanical Systems01:22

Mechanical Systems

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Mechanical systems are analogous to to electrical networks where springs and masses play similar roles to inductors and capacitors, respectively. A viscous damper in mechanical systems functions similarly to a resistor in electrical networks, dissipating energy. The forces acting on a mass in such systems include an applied force in the direction of motion, counteracted by forces from the spring, a viscous damper, and the mass's acceleration. This interplay of forces is mathematically...
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Electro-mechanical Systems01:19

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Electromechanical systems are intricate configurations that effectively combine electrical and mechanical elements to achieve a desired outcome. Central to many of these systems is the DC motor, a device that converts electrical energy into mechanical motion, enabling various applications ranging from simple fans to complex robotic mechanisms.
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Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
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There are many examples of pressure in fluids in everyday life, such as in relation to blood (high or low blood pressure) and in relation to weather (high- and low-pressure weather systems). A given force can have a significantly different effect, depending on the area over which the force is exerted. For instance, a force applied to an area of 1 mm2 has a pressure that is 100 times greater than the same force applied to an area of 1 cm2. That's why a sharp needle is able to poke through...
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The force applied by fluids against a surface, known as hydrostatic pressure, initiates the transfer of fluid among different compartments. Within our blood vessels, the blood's hydrostatic pressure is a result of the heart's pumping action. At the arteriolar end of capillaries, hydrostatic pressure (capillary blood pressure) exceeds the opposing colloid osmotic pressure created primarily by plasma proteins like albumin. This discrepancy in pressure propels plasma and nutrients from the...
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Fluids can be classified into Newtonian and non-Newtonian fluids based on their response to shear stress. Newtonian fluids have a linear relationship between shear stress and the shear strain rate, following Newton's law of viscosity. Their viscosity remains constant regardless of the shear rate, making their behavior predictable and easier to analyze. Common examples include water, air, oil, and gasoline.
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When robotics met fluidics.

Junjie Zhong1, Jason Riordon1, Tony C Wu2

  • 1Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S3G8, Canada. sinton@mie.utoronto.ca.

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Robotic automation is essential for advancing high-throughput fluidic systems beyond human capabilities. AI-directed robotic and fluidic systems will revolutionize synthetic chemistry and biology, enabling data generation and product synthesis.

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Area of Science:

  • Robotics
  • Fluidics
  • Artificial Intelligence
  • Synthetic Biology
  • Synthetic Chemistry

Background:

  • High-throughput fluidic technologies have significantly improved fluid processing speed and accuracy.
  • Further advancements necessitate the integration of robotic automation to surpass human operator limitations.
  • Progress in fields like gene editing amplifies the demand for intelligent, high-throughput experimental systems.

Purpose of the Study:

  • To explore the potential of integrating robotics and fluidics for fully self-driving fluid systems.
  • To identify the primary fields that will benefit from AI-directed robotic and fluidic systems.
  • To outline the key modalities through which these advanced systems will operate.

Main Methods:

  • Review of current innovations at the intersection of robotics and fluidics.
  • Analysis of the requirements for smart, high-throughput experimentation in modern science.
  • Conceptualization of AI-directed systems for scientific applications.

Main Results:

  • Significant opportunities exist in developing fully self-driving fluid systems.
  • Synthetic chemistry and synthetic biology are poised to be the earliest adopters.
  • Two primary operational modalities are envisioned: centralized data-producing facilities and distributed product-synthesizing/surveillance systems.

Conclusions:

  • The convergence of robotics, fluidics, and AI offers transformative potential for scientific research and development.
  • AI-directed robotic and fluidic systems will enhance capabilities in both data generation and on-demand synthesis.
  • These advancements promise to accelerate discovery and application in synthetic biology and chemistry.