Jove
Visualize
Contáctanos
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Videos de Conceptos Relacionados

Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

2.8K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
2.8K
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

1.1K
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 the...
1.1K
Differential Form of Maxwell's Equations01:17

Differential Form of Maxwell's Equations

1.1K
James Clerk Maxwell (1831–1879) was one of the significant contributors to physics in the nineteenth century. He is probably best known for having combined existing knowledge of the laws of electricity and the laws of magnetism with his insights to form a complete overarching electromagnetic theory, represented by Maxwell's equations. The four basic laws of electricity and magnetism were discovered experimentally through the work of physicists such as Oersted, Coulomb, Gauss, and...
1.1K
Newtonian Fluid: Problem Solving01:18

Newtonian Fluid: Problem Solving

830
Newtonian fluids exhibit a constant viscosity, meaning their shear stress and shear strain rate are directly proportional. This property ensures a predictable and stable response to applied forces, maintaining a linear relationship between force and flow. Examples include water, air, and light oils, consistently demonstrating this proportional behavior regardless of external conditions.
A velocity gradient forms within the fluid when a Newtonian fluid is placed between two parallel plates, with...
830
Symmetry in Maxwell's Equations01:28

Symmetry in Maxwell's Equations

4.1K
Once the fields have been calculated using Maxwell's four equations, the Lorentz force equation gives the force that the fields exert on a charged particle moving with a certain velocity. The Lorentz force equation combines the force of the electric field and of the magnetic field on the moving charge. Maxwell's equations and the Lorentz force law together encompass all the laws of electricity and magnetism. The symmetry that Maxwell introduced into his mathematical framework may not be...
4.1K
Maxwell's Equation Of Electromagnetism01:29

Maxwell's Equation Of Electromagnetism

3.9K
James Clerk Maxwell (1831–1879) was one of the major contributors to physics in the nineteenth century. Although he died young, he made major contributions to the development of the kinetic theory of gases, to the understanding of color vision, and to understanding the nature of Saturn's rings. He is probably best known for having combined existing knowledge on the laws of electricity and magnetism with his insights into a complete overarching electromagnetic theory, which is...
3.9K

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
Same author

Single-cell multiome profiling reveals neuronal bias of modulatory role of MeCP2 phosphorylation.

Neurobiology of disease·2026
Same author

Modeling and decoupling phase-shift-induced wavefront distortion in high NA spherical Fizeau interferometry via aberration-related iterative algorithm.

Optics express·2026
Same author

Interfacial Microenvironment Effects on the Mechanism of Photocatalytic Methanol Conversion for Hydrogen Evolution.

The journal of physical chemistry letters·2026
Same author

Reconfiguration of d-orbital states drives non-radiative energy dissipation in semiconductors.

Materials horizons·2026
Same author

Antibiotic-drug conjugates: Enhancing chemo-immunotherapy of gemcitabine for pancreatic cancer by eliminating intratumoral bacteria.

Biomaterials·2026
Same author

In Situ Biosynthesis of Pd Nanocrystals in Bifidobacterium Bioreactor as Dual Immune Stimulators for Immuno-Chemodynamic Therapy of Cold Tumor.

Small (Weinheim an der Bergstrasse, Germany)·2026

Video Experimental Relacionado

Updated: Jan 8, 2026

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization
08:03

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization

Published on: November 12, 2014

10.9K

Solucionador iterativo de Maxwell basado en Runge-Kutta para nanoestructuras a gran escala

Ziyao Zhang, Site Zhang, Haofeng Guo

    Optics express
    |December 19, 2025
    PubMed
    Resumen
    Este resumen es generado por máquina.

    Este estudio presenta una solución numérica iterativa mejorada para simular micro-/nanoestructuras grandes. El nuevo método modela con precisión estructuras complejas con reflexiones internas, mejorando la eficiencia en las simulaciones ópticas.

    Palabras clave:
    simulación de nanoestructurasmétodo de propagación de hacesecuaciones de Maxwellmétodos numéricosóptica deنانofotónica

    Más Videos Relacionados

    Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization
    05:37

    Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization

    Published on: August 22, 2025

    579
    Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
    06:37

    Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

    Published on: September 17, 2021

    5.0K

    Videos de Experimentos Relacionados

    Last Updated: Jan 8, 2026

    Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization
    08:03

    Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization

    Published on: November 12, 2014

    10.9K
    Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization
    05:37

    Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization

    Published on: August 22, 2025

    579
    Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
    06:37

    Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

    Published on: September 17, 2021

    5.0K

    Área de la Ciencia:

    • Electromagnetismo computacional
    • Simulación de nanofotónica
    • Métodos numéricos en óptica

    Sus antecedentes:

    • La simulación de micro-/nanoestructuras requiere métodos precisos para las ecuaciones de Maxwell.
    • Los métodos de propagación de haces (BPM) existentes enfrentan desafíos con estructuras grandes y reflexiones internas.
    • Trabajos anteriores incluyen BPM basado en Runge-Kutta (RK-BPM) y condiciones de contorno iterativas.

    Objetivo del estudio:

    • Desarrollar un método numérico eficiente y preciso para simular micro-/nanoestructuras grandes.
    • Extender las capacidades del método de propagación de haces basado en Runge-Kutta (RK-BPM).
    • Modelar con precisión estructuras ópticas complejas con múltiples reflexiones internas.

    Principales métodos:

    • Una solución numérica iterativa utilizando las ecuaciones de Maxwell.
    • Incorporación de un método de propagación de haces basado en Runge-Kutta (RK-BPM) en el dominio k como núcleo de iteración.
    • Integración de un esquema de condición de contorno iterativa para manejar estructuras complejas.

    Principales resultados:

    • El método desarrollado simula con precisión micro-/nanoestructuras grandes.
    • La técnica modela eficientemente estructuras complejas con múltiples reflexiones internas.
    • Se logra alta precisión y eficiencia en las simulaciones ópticas.

    Conclusiones:

    • El RK-BPM iterativo mejorado con condiciones de contorno iterativas proporciona una herramienta poderosa para la simulación de micro-/nanoestructuras.
    • Este método ofrece una mejora significativa para modelar sistemas ópticos grandes y complejos.
    • El enfoque mejora tanto la precisión como la eficiencia de las simulaciones numéricas en nanofotónica.