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Nonlinearity in drug pharmacokinetics is caused by various factors influencing how a drug is absorbed, distributed, metabolized, and excreted. Understanding these nonlinear processes is crucial for predicting drug behavior in the body and optimizing drug dosing regimens.
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Dynamical nonlinear memory capacitance in biomimetic membranes.

Joseph S Najem1,2, Md Sakib Hasan3, R Stanley Williams4

  • 1Department of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, Knoxville, TN, 37916, USA.

Nature Communications
|July 21, 2019
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Summary
This summary is machine-generated.

Researchers developed a novel biomolecular memcapacitor using lipid bilayers. This device exhibits volatile, voltage-controlled capacitive memory and synapse-like plasticity, paving the way for efficient neuromorphic computing.

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Area of Science:

  • Materials Science
  • Neuroscience
  • Nanotechnology

Background:

  • Next-generation computing requires efficient nanoscale memory elements for signal processing and memory co-location.
  • While memristors are well-studied, other memelements like memcapacitors are underexplored.
  • Biomimetic materials offer potential for novel computing paradigms.

Purpose of the Study:

  • To report the first volatile, voltage-controlled memcapacitor.
  • To investigate capacitive memory arising from biomimetic lipid bilayers.
  • To explore tuneable signal processing and learning in these novel memelements.

Main Methods:

  • Fabrication of a lipid bilayer memcapacitor mimicking biological membranes.
  • Characterization of voltage-controlled, hysteretic geometrical changes (radius and thickness).
  • Analysis of nonlinear dynamics and state variables governing memory behavior.

Main Results:

  • Demonstration of a novel, volatile, voltage-controlled memcapacitor.
  • Identification of membrane radius and thickness as key state variables.
  • Observation of synapse-like short-term capacitive plasticity for tuneable learning.

Conclusions:

  • This biomolecular memcapacitor represents a significant advancement in neuromorphic computing materials.
  • The findings accelerate the development of low-energy, biomolecular neuromorphic memelements.
  • The system serves as a model for studying capacitive memory in neuronal membranes.