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

Atomic Nuclei: Types of Nuclear Relaxation01:28

Atomic Nuclei: Types of Nuclear Relaxation

253
Nuclear relaxation restores the equilibrium population imbalance and can occur via spin–lattice or spin–spin mechanisms, which are first-order exponential decay processes.
In spin–lattice or longitudinal relaxation, the excited spins exchange energy with the surrounding lattice as they return to the lower energy level. Among several mechanisms that contribute to spin–lattice relaxation, magnetic dipolar interactions are significant. Here, the excited nucleus transfers...
253
Atomic Nuclei: Nuclear Relaxation Processes01:23

Atomic Nuclei: Nuclear Relaxation Processes

622
In the absence of an external magnetic field, nuclear spin states are degenerate and randomly oriented. When a magnetic field is applied, the spins begin to precess and orient themselves along (lower energy) or against (higher energy) the direction of the field. At equilibrium, a slight excess population of spins exists in the lower energy state. Because the direction of the magnetic field is fixed as the z-axis,  the precessing magnetic moments are randomly oriented around the z-axis.
622
Atomic Nuclei: Nuclear Spin State Overview01:03

Atomic Nuclei: Nuclear Spin State Overview

866
NMR-active nuclei have energy levels called 'spin states' that are associated with the orientations of their nuclear magnetic moments. In the absence of a magnetic field, the nuclear magnetic moments are randomly oriented, and the spin states are degenerate. When an external magnetic field is applied, the spin states have only 2 + 1 orientations available to them. A proton with = ½ has two available orientations. Similarly, for a quadrupolar nucleus with a nuclear spin value of...
866
Spin–Spin Coupling Constant: Overview01:08

Spin–Spin Coupling Constant: Overview

876
In bromoethane, the three methyl protons are coupled to the two methylene protons that are three bonds away. In accordance with the n+1 rule, the signal from the methyl protons is split into three peaks with 1:2:1 relative intensities. The methylene protons appear as a quartet, with the relative intensities of 1:3:3:1.
Qualitatively, any spin plus-half nucleus polarizes the spins of its electrons to the minus-half state. Consequently, the paired electron in the hydrogen–carbon bond must...
876
¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

1.0K
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...
1.0K
Atomic Nuclei: Nuclear Spin State Population Distribution01:14

Atomic Nuclei: Nuclear Spin State Population Distribution

938
Near absolute zero temperatures, in the presence of a magnetic field, the majority of nuclei prefer the lower energy spin-up state to the higher energy spin-down state. As temperatures increase, the energy from thermal collisions distributes the spins more equally between the two states. The Boltzmann distribution equation gives the ratio of the number of spins predicted in the spin −½ (N−) and spin +½ (N+) states.
938

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

Thermally Activated Fluxionality Accelerates Nonradiative Decay in Titania Nanoclusters.

The journal of physical chemistry letters·2026
Same author

Synthesis of a Series of Ln(III) (Ln = La, Ce, Lu) Aryl Complexes and Analysis of Their Ln-L Bonding Using Multinuclear NMR Spectroscopy and DFT Calculations.

Inorganic chemistry·2026
Same author

Comparison of Bonding in Isostructural Cerium and Thorium Parent Amide Complexes.

Inorganic chemistry·2026
Same author

Elucidating metal (Zr, Hf, Th, U)-hydride covalency using <sup>1</sup>H NMR chemical shifts and density functional calculations.

Communications chemistry·2026
Same author

Leveraging the redox activities of cerium and dibenzotetrathiafulvalene to discover a photo-responsive magnetic material.

Chemical science·2026
Same author

Assessment of trajectory surface hopping methods in long-time nonadiabatic dynamics.

The Journal of chemical physics·2026
Same journal

Accurate Density Functional Theory Forces for Charged Noncovalent Complexes.

The journal of physical chemistry letters·2026
Same journal

Dopant-Centered versus Intersite Synergistic Mechanisms in H<sub>2</sub> Dissociation on Single-Atom Alloys.

The journal of physical chemistry letters·2026
Same journal

Post-Translational Modification as an Allosteric Switch in Hsp90: How Dual Phosphorylation Locks Chaperone Complexes into Hyperstabilized States.

The journal of physical chemistry letters·2026
Same journal

LHCSR1 Functions as a Dimmer Switch for Light Harvesting.

The journal of physical chemistry letters·2026
Same journal

Sparse Linear Surrogates Match Neural Network Potentials on the SPICE Biomolecular Benchmark with Three Orders of Magnitude Smaller Training Sets.

The journal of physical chemistry letters·2026
Same journal

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

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

相关实验视频

Updated: Jun 4, 2025

Measuring the Spin-Lattice Relaxation Magnetic Field Dependence of Hyperpolarized [1-13C]pyruvate
11:57

Measuring the Spin-Lattice Relaxation Magnetic Field Dependence of Hyperpolarized [1-13C]pyruvate

Published on: September 13, 2019

6.5K

机器学习绘图方法用于计算旋转放松动力学.

Mohammad Shakiba1, Adam B Philips1, Jochen Autschbach1

  • 1Department of Chemistry, University at Buffalo, The State University of New York, Buffalo, New York 14260, United States.

The journal of physical chemistry letters
|December 21, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种机器学习方法,用于预测原子系统属性. 它使用比传统计算更少的数据准确预测电场梯度和旋转放松率.

更多相关视频

Author Spotlight: Exploring Intrinsically Disordered Protein Dynamics Through NMR Relaxation Experiments
09:25

Author Spotlight: Exploring Intrinsically Disordered Protein Dynamics Through NMR Relaxation Experiments

Published on: November 1, 2024

1.8K
Paramagnetic Relaxation Enhancement for Detecting and Characterizing Self-Associations of Intrinsically Disordered Proteins
07:24

Paramagnetic Relaxation Enhancement for Detecting and Characterizing Self-Associations of Intrinsically Disordered Proteins

Published on: September 23, 2021

1.7K

相关实验视频

Last Updated: Jun 4, 2025

Measuring the Spin-Lattice Relaxation Magnetic Field Dependence of Hyperpolarized [1-13C]pyruvate
11:57

Measuring the Spin-Lattice Relaxation Magnetic Field Dependence of Hyperpolarized [1-13C]pyruvate

Published on: September 13, 2019

6.5K
Author Spotlight: Exploring Intrinsically Disordered Protein Dynamics Through NMR Relaxation Experiments
09:25

Author Spotlight: Exploring Intrinsically Disordered Protein Dynamics Through NMR Relaxation Experiments

Published on: November 1, 2024

1.8K
Paramagnetic Relaxation Enhancement for Detecting and Characterizing Self-Associations of Intrinsically Disordered Proteins
07:24

Paramagnetic Relaxation Enhancement for Detecting and Characterizing Self-Associations of Intrinsically Disordered Proteins

Published on: September 23, 2021

1.7K

科学领域:

  • 计算化学和材料科学.
  • 机器学习在量子力学中的应用.
  • 开发用于预测分子属性的方法.

背景情况:

  • 预测原子系统的特性往往需要计算上昂贵的高级理论计算.
  • 电场梯度 (EFG) 对于理解核特性和旋转放松至关重要.
  • 计算EFG和旋转放松率的现有方法可能耗时.

研究的目的:

  • 开发和验证一种机器学习 (ML) 方法来预测电场梯度 (EFG) 张量.
  • 使用ML预测的EFG来计算水溶液中离子的自旋放松率.
  • 在数据要求方面证明ML方法的效率.

主要方法:

  • 采用机器学习映射方法,使用原子轨道重叠,密度或Kohn-Sham (KS) Fock矩阵元素从扩展紧密结合作为输入特征.
  • 这些特征被用来预测EFG张量,这些张量通常在较高的理论水平 (例如混合函数) 得到.
  • 然后,预测的EFG张量被用来计算各种离子的四极异极旋转放松率.

主要成果:

  • 该ML方法成功地预测了高准确度的EFG张量.
  • 预测的EFG张量器能够准确计算水溶液中的几个离子的自旋放松率.
  • 该方法在使用直接计算所需数据的仅一小部分的情况下,对于旋转放松率实现了良好的准确性 (2-8%的相对误差).

结论:

  • 机器学习为预测EFG张量和旋转放松率提供了一个高效而准确的替代方案.
  • 与传统方法相比,这种方法显著降低了计算成本和数据需求.
  • 开发的ML模型有望加速材料发现和理解分子动力学.