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Spin–Spin Coupling Constant: Overview01:08

Spin–Spin Coupling Constant: Overview

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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...
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Spin–Spin Coupling: One-Bond Coupling01:17

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Coupling interactions are strongest between NMR-active nuclei bonded to each other, where spin information can be transmitted directly through the pair of bonding electrons. While nuclei polarize their electrons to the opposite spins, the bonding electron pair has opposite spins. Configurations with antiparallel nuclear spins are expected to be lower in energy. When coupling makes antiparallel states more favorable, J is considered to have a positive value. The one-bond coupling constant, 1J,...
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Spin–Spin Coupling: Two-Bond Coupling (Geminal Coupling)01:20

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Two NMR-active nuclei bonded to a central atom can be involved in geminal or two-bond coupling. Geminal coupling is commonly seen between diastereotopic protons in chiral molecules and unsymmetrical alkenes, among others.
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Spin–Spin Coupling: Three-Bond Coupling (Vicinal Coupling)01:22

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Vicinal or three-bond coupling is commonly observed between protons attached to adjacent carbons. Here, nuclear spin information is primarily transferred via electron spin interactions between adjacent C‑H bond orbitals. This generally favors the antiparallel arrangement of spins, so 3J values are usually positive.
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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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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 one, the...
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Stochastic Spin-Orbit Torque Devices as Elements for Bayesian Inference.

Yong Shim1, Shuhan Chen1, Abhronil Sengupta2

  • 1School of Electrical & Computer Engineering, Purdue University, West Lafayette, Indiana, 47907, USA.

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Summary
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This study demonstrates a spintronic device for probabilistic inference, mimicking brain neuron behavior for advanced computing. This innovation could lead to hardware directly supporting Bayesian inference computations.

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Area of Science:

  • Neuromorphic Engineering
  • Spintronics
  • Computational Neuroscience

Background:

  • Cognitive intelligence may be enabled by probabilistic inference from real-time data.
  • Stochastic functionalities are observed in human brain cortical microcircuit neurons.
  • Neuromorphic computing utilizes probabilistic computational units influenced by input stimuli.

Purpose of the Study:

  • To experimentally demonstrate a spintronic device for controllable stochastic switching.
  • To enable probabilistic inference using a spintronic element.
  • To explore applications in unconventional computing scenarios.

Main Methods:

  • Fabrication and characterization of Ta/CoFeB/MgO heterostructures.
  • Utilizing spin-orbit torque and thermal noise for probabilistic switching.
  • Experimental demonstration of the device's stochastic switching functionality.

Main Results:

  • Successful demonstration of a spintronic device acting as a controllable stochastic switching element.
  • Harnessing probabilistic switching for applications in unconventional computing.
  • Direct mapping of the device's functionality to Bayesian inference computational units.

Conclusions:

  • Spintronic devices can directly mimic computational units for Bayesian inference.
  • This work paves the way for hardware supporting probabilistic inference.
  • Potential for advancing cognitive intelligence through novel computing architectures.