Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Energy Stored in Capacitors01:10

Energy Stored in Capacitors

573
A parallel plate capacitor, when connected to a battery, develops a potential difference across its plates. This potential difference is key to the operation of the capacitor, as it determines how much electrical energy the capacitor can store.
By integrating the equation that relates voltage and current in a capacitor, one can derive an equation for the voltage across the capacitor at any given time. This equation is crucial in understanding and predicting the behavior of capacitors in...
573
Energy Stored in a Capacitor: Problem Solving01:26

Energy Stored in a Capacitor: Problem Solving

1.1K
In 1749, Benjamin Franklin coined the word battery for a series of capacitors connected to store energy. Capacitors store electric potential energy that can be released over a short time. This property means capacitors have a wide range of applications.
Capacitor-discharge ignition is a type of ignition system commonly found in small engines where the energy released from a capacitor ignites an induction coil that, in turn, fires the spark plug.
To calculate the energy stored in a capacitor of...
1.1K
Energy Stored in a Capacitor01:12

Energy Stored in a Capacitor

3.8K
When an archer pulls the string in a bow, he saves the work done in the form of elastic potential energy. When he releases the string, the potential energy is released as kinetic energy of the arrow. A capacitor works on the same principle in which the work done is saved as electric potential energy. The potential energy (UC) could be calculated by measuring the work done (W) to charge the capacitor.
3.8K
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

3.3K
A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
3.3K
Long-term Potentiation01:35

Long-term Potentiation

55.6K
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
55.6K
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

718
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
718

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Differential Image Sensor With Decoupled Static and Dynamic Outputs.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Ultra-robust ionic biogel with multiscale structure and multifunctionality for wearable self-powered human-interactive sensing.

Science bulletin·2026
Same author

A Flexible Wireless Passive Platform for Decoupled Electrolyte and Temperature Sensing Toward Heat‑Stress Assessment.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Wearable thumb sleeves enabled by self-supervised learning with few stretchable sensors and few-shot data for switchable finger tasks.

Science advances·2026
Same author

Bioresorbable acoustic patch for simultaneous sealing and early detection of gastric leakage.

Science advances·2026
Same author

Curvature-Variable Image Sensor Array with Mo<sub>2</sub>TiC<sub>2</sub>T<i><sub><i>x</i></sub></i> MXene for Dark-field Multiview 3D Reconstruction Application.

ACS nano·2026
Same journal

Intimate encapsulation of non-planar electrodes via a viscoplastic interlayer.

National science review·2026
Same journal

The emerging Antarctic amplification.

National science review·2026
Same journal

Reconstructing vegetation biomass in the Middle Jurassic Yanliao Biota from insect fossil assemblages.

National science review·2026
Same journal

Industrial electrocatalytic C-C coupling reaction of C<sub>1</sub> liquid molecules for efficient ethanol synthesis.

National science review·2026
Same journal

Intrinsic auxetic piezoelectricity in bulk ferroelectrics.

National science review·2026
Same journal

Electrochemical in-biosensing computing.

National science review·2026
See all related articles

Related Experiment Video

Updated: Aug 21, 2025

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
08:07

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Published on: March 9, 2019

7.9K

Neuromorphic-computing-based adaptive learning using ion dynamics in flexible energy storage devices.

Shufang Zhao1, Wenhao Ran1, Zheng Lou1

  • 1State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, and Center of Materials Science and Optoelectronic Engineering, University of Chinese Academy of Sciences, Beijing 100083, China.

National Science Review
|November 16, 2022
PubMed
Summary
This summary is machine-generated.

We developed self-adaptive artificial synapses using flexible MXene energy storage devices for high-performance computing. These devices enable selective and linear synaptic weight updates, achieving ~95% accuracy in neural network tasks.

Keywords:
MXeneadaptabilityflexible devicehigh accuracyion dynamicsneuromorphic computing

More Related Videos

A Method for Growing Bio-memristors from Slime Mold
07:46

A Method for Growing Bio-memristors from Slime Mold

Published on: November 2, 2017

9.0K
Fabrication of Carbon-Based Ionic Electromechanically Active Soft Actuators
14:42

Fabrication of Carbon-Based Ionic Electromechanically Active Soft Actuators

Published on: April 25, 2020

8.4K

Related Experiment Videos

Last Updated: Aug 21, 2025

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
08:07

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Published on: March 9, 2019

7.9K
A Method for Growing Bio-memristors from Slime Mold
07:46

A Method for Growing Bio-memristors from Slime Mold

Published on: November 2, 2017

9.0K
Fabrication of Carbon-Based Ionic Electromechanically Active Soft Actuators
14:42

Fabrication of Carbon-Based Ionic Electromechanically Active Soft Actuators

Published on: April 25, 2020

8.4K

Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Science

Background:

  • Neuromorphic devices require adaptive weight adjustment for high-performance computing.
  • Achieving selective and linear synaptic weight updates without altering electrical pulses remains a challenge.

Purpose of the Study:

  • To propose high-accuracy, self-adaptive artificial synapses using tunable and flexible MXene energy storage devices.
  • To enable adaptive synaptic weight adjustment based on stored values, reducing recalculation time and energy loss.

Main Methods:

  • Utilized tunable and flexible MXene energy storage devices as artificial synapses.
  • Employed resistance modulation to regulate ion accumulation/dissipation for selective and linear weight updates.
  • Investigated the feasibility of a neural network using these synapses through training and machine learning.

Main Results:

  • Demonstrated selective and linear synaptic weight updates without changing external pulse stimulation or preprogramming.
  • Achieved approximately 95% recognition accuracy in neural network tasks, including numeric classification.

Conclusions:

  • The proposed MXene-based artificial synapses offer a promising solution for high-accuracy, self-adaptive neuromorphic computing.
  • These devices facilitate efficient and selective synaptic weight modulation, crucial for advanced computing applications.