Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Differential Form of Maxwell's Equations01:17

Differential Form of Maxwell's Equations

404
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...
404
Transmission-Line Differential Equations01:26

Transmission-Line Differential Equations

231
Transmission lines are essential components of electrical power systems. They are characterized by the distributed nature of resistance (R), inductance (L), and capacitance (C) per unit length. To analyze these lines, differential equations are employed to model the variations in voltage and current along the line.
Line Section Model
A circuit representing a line section of length Δx helps in understanding the transmission line parameters. The voltage V(x) and current i(x) are measured...
231
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.4K
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
1.4K
Poisson's And Laplace's Equation01:25

Poisson's And Laplace's Equation

2.6K
The electric potential of the system can be calculated by relating it to the electric charge densities that give rise to the electric potential. The differential form of Gauss's law expresses the electric field's divergence in terms of the electric charge density.
2.6K
Electromagnetic Wave Equation01:24

Electromagnetic Wave Equation

985
Maxwell's equations for electromagnetic fields are related to source charges, either static or moving. These fields act on a test charge, whose trajectory can thus be determined using suitable boundary conditions. The objective of electromagnetism is thus theoretically complete.
However, although electric and magnetic fields were first introduced as mathematical constructs to simplify the description of mutual forces between charges, a natural question emerges from Maxwell's equations:...
985
IR Spectroscopy: Hooke's Law Approximation of Molecular Vibration01:16

IR Spectroscopy: Hooke's Law Approximation of Molecular Vibration

1.2K
A covalently bonded heteronuclear diatomic molecule can be modeled as two vibrating masses connected by a spring. The vibrational frequency of the bond can be expressed using an equation derived from Hooke's law, which describes how the force applied to stretch or compress a spring is proportional to the displacement of the spring. In this case, the atoms behave like masses, and the bond acts like a spring.
According to Hooke's law, the vibrational frequency is directly proportional to...
1.2K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Ambient-compatible precursor engineering for efficient perovskite photovoltaics.

Nature communications·2026
Same author

Extending the Weakly Confined Regime of Perovskite Nanocrystals for Fast Emission at Low Temperature.

ACS nano·2026
Same author

Oxygen Vacancy Formation in ZnSeTe Blue Quantum Dot Light-Emitting Diodes.

Journal of the American Chemical Society·2025
Same author

Deactivation of Interfacial Recombination Center for Thermally Stable Perovskite Solar Cells.

Journal of the American Chemical Society·2025
Same author

Challenges of II-VI and III-V Blue Quantum Dot Light-Emitting Diodes.

Advanced materials (Deerfield Beach, Fla.)·2025
Same author

Spontaneous formation of robust two-dimensional perovskite phases.

Science (New York, N.Y.)·2025
Same journal

Solid-State NMR Quantification of Brønsted-Lewis Acid Site Cooperativity in Zeolites for Glucose Conversion.

The journal of physical chemistry letters·2026
Same journal

Ion-Pairing-Mediated Selective Transport of Rare Earth Elements through Functionalized Graphene Nanopores.

The journal of physical chemistry letters·2026
Same journal

Ligand-Tuned CISS-Effect of Atomically Precise Metal Oxido Nanoclusters.

The journal of physical chemistry letters·2026
Same journal

Data-Driven Exploration of the Polyethylene Catalyst Chemical Space via Machine Learning.

The journal of physical chemistry letters·2026
Same journal

Role of Ultrafast Electron-Thermal-Phonon Interactions in High Harmonic Generation and Dephasing from Graphene.

The journal of physical chemistry letters·2026
Same journal

Real-Time Vibrational Spectroscopy Reveals an Inversion Transition State in the Photoisomerization of Phenylazoimidazole.

The journal of physical chemistry letters·2026
查看所有相关文章

相关实验视频

Updated: Jun 3, 2025

A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors
11:15

A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors

Published on: May 30, 2016

25.1K

神经常规微分方程用于预测和加速光子相关性光谱学.

Andrew H Proppe1, Kin Long Kelvin Lee1,2, Weiwei Sun1

  • 1Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.

The journal of physical chemistry letters
|January 6, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了g2NODE,这是一种深度学习模型,可以显著加快单光子发射器的量子光学属性评估. 这个人工智能工具使用最小的数据来生成完整的,无噪声的实验,将获取时间缩短了20倍.

更多相关视频

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution
08:48

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Published on: September 5, 2012

11.8K
Real-Time Monitoring of Neurocritical Patients with Diffuse Optical Spectroscopies
07:12

Real-Time Monitoring of Neurocritical Patients with Diffuse Optical Spectroscopies

Published on: November 19, 2020

2.1K

相关实验视频

Last Updated: Jun 3, 2025

A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors
11:15

A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors

Published on: May 30, 2016

25.1K
Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution
08:48

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Published on: September 5, 2012

11.8K
Real-Time Monitoring of Neurocritical Patients with Diffuse Optical Spectroscopies
07:12

Real-Time Monitoring of Neurocritical Patients with Diffuse Optical Spectroscopies

Published on: November 19, 2020

2.1K

科学领域:

  • 量子光学是一种量子光学.
  • 材料科学 材料科学 材料科学
  • 人工智能的人工智能

背景情况:

  • 评估固态单光子发射器的量子光学特性至关重要,但耗时.
  • 光子相关里埃光谱 (PCFS) 提供了详细的光谱信息,但需要大量的实验时间.

研究的目的:

  • 开发一种新的深度学习模型,以加快量子发射器的表征.
  • 为了减少光子相关谱的实验采集时间.

主要方法:

  • 开发了一个神经普通微分方程模型,命名为g2NODE.
  • g2NODE从一小部分噪音相关函数预测了完整的,无噪音的干扰测试实验.
  • 该模型使用模拟和实验数据进行了验证.

主要成果:

  • g2NODE成功地从10-20个噪音测量中生成了完整的无噪声干涉图.
  • 这种方法使实验采集时间加快了20倍,将时间缩短到几分钟.
  • 该模型准确地预测了高达200个阶段位置的实验.

结论:

  • g2NODE为光子相关谱学提供了显著的加速.
  • 这种深度学习方法增强了PCFS用于表征新型量子发射材料的实用性.
  • 该方法提出了一种新的人工智能驱动的战略,用于量子光学中的实验性表征.