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Related Concept Videos

Second Order systems II01:18

Second Order systems II

398
In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
398
First Order Systems01:21

First Order Systems

416
First-order systems, such as RC circuits, are foundational in understanding dynamic systems due to their straightforward input-output relationship. Analyzing their responses to different input functions under zero initial conditions reveals significant insights into system behavior.
When a first-order system is subjected to a unit-step input, its response is characterized by its transfer function. By applying the Laplace transform of the unit-step input to the transfer function, expanding the...
416
Second Order systems I01:20

Second Order systems I

584
A servo system exemplifies a second-order system, featuring a proportional controller and load elements that ensure the output position aligns with the input position. The relationship between these components is described by a second-order differential equation. Applying the Laplace transform under zero initial conditions yields the transfer function, showing how inputs are converted to outputs in the system.
By reinterpreting the system, one can derive the closed-loop transfer function, which...
584
Classification of Systems-I01:26

Classification of Systems-I

556
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
556
Classification of Systems-II01:31

Classification of Systems-II

465
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
465
Mechanical Systems01:22

Mechanical Systems

616
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...
616

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Related Experiment Video

Updated: Jan 27, 2026

Development of an Electrochemical DNA Biosensor to Detect a Foodborne Pathogen
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Cell-free systems for development of biosensors.

Stuti Ganatra1, Alok Pandya1

  • 1School of Applied Sciences and Technology, Gujarat Technological University, Ahmedabad, Gujarat, India.

Progress in Molecular Biology and Translational Science
|January 25, 2026
PubMed
Summary

Cell-free systems (CFSs) offer rapid, customizable biosensors for diverse applications. These synthetic biology tools are ideal for point-of-care diagnostics, especially in resource-limited settings.

Keywords:
BiosensorCRISPR diagnostics (SHERLOCK, DETECTR)Cell-free systemsGenetic circuitsMicrofluidicRiboswitchesToehold switches

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Area of Science:

  • Synthetic biology
  • Biochemistry
  • Biosensor technology

Background:

  • Cell-free systems (CFSs) leverage in vitro transcription and translation for controlled biochemical environments.
  • CFSs enable the development of modular and customizable biosensors without living cells.
  • Applications span healthcare, environmental monitoring, agriculture, and food quality assurance.

Purpose of the Study:

  • To provide an in-depth overview of cell-free biosensor design and functionality.
  • To highlight various device formats and use cases for CFS-based biosensors.
  • To discuss current limitations and future engineering solutions for CFS biosensing.

Main Methods:

  • Design and construction of genetic circuits for CFS biosensors.
  • Development of diverse signal output strategies.
  • Integration of CFS technology into various device formats (paper-based, microfluidics, wearables).

Main Results:

  • CFS biosensors demonstrate utility in pathogen detection and environmental contaminant monitoring.
  • These biosensors are particularly valuable for point-of-care (POC) and low-resource diagnostics.
  • Current limitations include shelf-life, sensitivity, and scalability challenges.

Conclusions:

  • Engineering solutions like AI-assisted design and material integration are addressing CFS limitations.
  • The integration of CFS biosensing with IoT and distributed fabrication promises advanced diagnostics.
  • Cell-free biosensors represent a significant advancement in accessible and intelligent diagnostic tools.