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相关概念视频

¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

1.2K
The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
A broadband decoupling technique is used to simplify these complex, sometimes overlapping, signals. Broadband decoupling relies on a...
1.2K
Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule01:10

Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule

1.6K
In the AX proton spin system, proton A can sense the two spin states of a coupled proton X, resulting in a doublet NMR signal with two peaks of equal (1:1) intensity. When proton A is coupled to two equivalent protons (AX2 spin system), the spin states of each X can be aligned with or against the external field, creating three possible scenarios. This results in a 1:2:1  triplet signal, where the central peak corresponds to the chemical shift of A and is twice as large or intense as the...
1.6K
¹H NMR: Complex Splitting01:13

¹H NMR: Complex Splitting

1.4K
A proton M that is coupled to a proton X results in doublet signals for M. However, NMR-active nuclei can be simultaneously coupled to more than one nonequivalent nucleus. When M is coupled to a second proton A, such as in styrene oxide, each peak in the doublet is split into another doublet.
Splitting diagrams or splitting tree diagrams are routinely used to depict such complex couplings. While drawing splitting diagrams, the splitting with the larger coupling constant is usually applied...
1.4K
¹H NMR Signal Multiplicity: Splitting Patterns01:13

¹H NMR Signal Multiplicity: Splitting Patterns

5.4K
When protons A and X are coupled, their nuclear spin energy levels are slightly modified. This is because the energy required to excite proton A to a spin state parallel to proton X is slightly different from the energy required for it to become anti-parallel to spin X. Consequently, there are two possible excitation frequencies for A (A1 and A2), depending on the spin state of X, and vice versa. The mutual nature of coupling implies that the difference between frequencies A1 and A2, indicated...
5.4K
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

1.3K
Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
1.3K
Atomic Emission Spectroscopy: Lab01:29

Atomic Emission Spectroscopy: Lab

278
AES is a powerful analytical technique, especially effective when used with plasma sources, producing abundant spectra in characteristic emission lines. The Inductively Coupled Plasma (ICP), in particular, yields superior quantitative analytical data due to its high stability, low noise, low background, and minimal interferences under optimal experimental conditions. However, newer air-operated microwave sources are emerging as promising alternatives that could be more cost-effective than...
278

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Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
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人工智能"看到"分裂的电子

John P Perdew1

  • 1Departments of Physics and Chemistry, Temple University, Philadelphia, PA 19122.

Science (New York, N.Y.)
|December 9, 2021
PubMed
概括
此摘要是机器生成的。

机器学习已经开发出一种新的密度函数. 这种计算工具准确地模拟了带有微分电荷和旋转的系统,进步了材料科学.

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科学领域:

  • 计算化学
  • 材料科学

背景情况:

  • 密度函数理论 (DFT) 是一种用于电子结构计算的强大的量子力学方法.
  • 对于现有的DFT函数来说,准确地描述材料中的微分电荷和旋转状态仍然是一个重大挑战.
  • 开发改进的功能对于预测材料属性至关重要.

研究的目的:

  • 使用机器学习算法开发新的密度函数.
  • 确保功能精确地捕捉与分数电荷和旋转相关的电子属性.

主要方法:

  • 使用机器学习技术来训练新的密度函数.
  • 使用先进的计算方法来评估函数的性能.

主要成果:

  • 机器学习的密度函数成功地解释了分数电荷.
  • 函数还在模拟分数旋转状态方面表现出准确性.
  • 这对现有方法来说是一个显著的改进.

结论:

  • 机器学习为创建高度精确的密度函数提供了一个有前途的途径.
  • 开发的功能可以提高凝聚物质物理和化学的预测.