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相关概念视频

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

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When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
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Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)01:15

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)

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Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...
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Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

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Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
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Potential Due to a Polarized Object01:29

Potential Due to a Polarized Object

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A neutral atom consists of a positively charged nucleus surrounded by a negatively charged electron cloud. When placed in an external electric field, the external electric force pulls the electrons and nucleus apart, opposite to the intrinsic attraction between the nucleus and the electrons. The opposing forces balance each other with a slight shift between the center of masses of the nucleus and the electron cloud, resulting in a polarized atom. On the other hand, a few molecules, like water,...
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π Electron Effects on Chemical Shift: Overview01:27

π Electron Effects on Chemical Shift: Overview

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An applied magnetic field causes loosely bound π-electrons in organic molecules to circulate, producing a local or induced diamagnetic field over a large spatial volume. As the molecules tumble in solution, the field generated by π-electrons in spherical substituents results in a zero net field. However, the net field generated by π-electrons in non-spherical substituents is not zero. The effect of this induced field depends on the orientation of the molecule with respect to B0,...
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¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

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Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
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通过接近最佳的量子过程断层扫描检索空间依赖的极化转换.

Francesco Di Colandrea, Lorenzo Amato, Roberto Schiattarella

    Optics express
    |October 20, 2023
    PubMed
    概括

    研究人员探索了量子过程断层扫描的遗传和机器学习方法. 这些技术准确地重建光学偏振转换,提供更快的操作,特别是实时应用.

    科学领域:

    • 量子光学就是一个量子光学.
    • 量子信息科学是一种量子信息科学.
    • 地元表面光学学

    背景情况:

    • 光学波形板可以被建模为单量子位单元运算符.
    • 量子过程断层扫描 (QPT) 从测量中重建量子运算.
    • 最大概率估计是一种标准的QPT方法,但可能是计算密集的.

    研究的目的:

    • 研究遗传学和机器学习算法的应用,用于光学过程断层扫描.
    • 将这些新方法的性能与标准技术进行比较.
    • 用旋转轨道元表面来实验性地描述空间依赖的极化转换.

    主要方法:

    • 模拟光波板作为单量子位单元运算符.
    • 利用遗传算法和神经网络进行断层重建.
    • 实验实施使用旋转轨道元表面与有图案的双折射.
    • 将重建准确度和速度与最大概率估计进行比较.

    主要成果:

    • 遗传学和机器学习方法都能够准确地重建极化转换.
    • 这些方法表明运行时间很快,特别是在最小的投射测量时.
    • 基于神经网络的方案为实时表征提供了显著的加速.

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  • 实现了空间依赖的极化转换的成功实验性表征.
  • 结论:

    • 基因和机器学习方法为光学过程断层扫描提供了高效和准确的替代方案.
    • 开发的技术扩展了用于描述光学和量子过程的现有方法.
    • 这些发现为在更复杂的量子系统中优化断层扫描方法铺平了道路.