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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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Updated: Oct 10, 2025

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
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Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

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La inteligencia artificial "ve" los electrones divididos

John P Perdew1

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

Science (New York, N.Y.)
|December 9, 2021
PubMed
Resumen
Este resumen es generado por máquina.

El aprendizaje automático ha desarrollado una nueva densidad funcional. Esta herramienta computacional modela con precisión los sistemas con carga y espín fraccionados, avanzando en la ciencia de los materiales.

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Área de la Ciencia:

  • Química computacional
  • Ciencias de los materiales

Sus antecedentes:

  • La teoría funcional de densidad (DFT) es un poderoso método mecánico cuántico para los cálculos de la estructura electrónica.
  • Describir con precisión los estados de carga y espín fraccionados en los materiales sigue siendo un desafío importante para los funcionales DFT existentes.
  • El desarrollo de funciones mejoradas es crucial para predecir las propiedades de los materiales.

Objetivo del estudio:

  • Desarrollar una nueva función de densidad utilizando algoritmos de aprendizaje automático.
  • Para garantizar que el funcional capte con precisión las propiedades electrónicas relacionadas con la carga fraccionaria y el giro.

Principales métodos:

  • Utilizó técnicas de aprendizaje automático para entrenar una nueva densidad funcional.
  • Empleó métodos computacionales avanzados para evaluar el rendimiento del funcional.

Principales resultados:

  • La función de densidad aprendida por la máquina explica con éxito la carga fraccionaria.
  • El funcional también demuestra precisión en el modelado de estados de espín fraccionarios.
  • Esto representa una mejora significativa con respecto a los métodos existentes.

Conclusiones:

  • El aprendizaje automático ofrece una vía prometedora para crear funcionales de densidad de alta precisión.
  • La función desarrollada tiene el potencial de mejorar las predicciones en física y química de la materia condensada.