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Chirality is the most intriguing yet essential facet of nature, governing life’s biochemical processes and precision. It can be observed from a snail shell pattern in a macroscopic world to an amino acid, the minutest building block of life. Most of the snails around the world have right-coiled shells because of the intrinsic chirality in their genes. All the amino acids present in the human body exist in an enantiomerically pure state, except for glycine - the sole achiral amino acid.
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Chirality is a term that describes the lack of mirror symmetry in an object. In other words, chiral objects cannot be superposed on their mirror images. For example, our feet are chiral, as the mirror image of the left foot, the right foot, cannot be superposed on the left foot.
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It is essential to understand the difference between chiral and achiral interactions and the implications thereof in optical activity and their applications. Just as our feet, which are chiral, interact uniquely with chiral objects, such as a pair of shoes, but identically with achiral socks, enantiomers of a molecule exhibit different properties only when they interact with other chiral media. An example of a significant implication from this facet is the phenomenon known as optical activity,...
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The concept of prochirality leads to the nomenclature of the individual faces of a molecule and plays a crucial role in the enantioselective reaction. It is a concept where two or more achiral molecules react to produce chiral products. A typical process is the reaction of an achiral ketone to generate a chiral alcohol. Here, the achiral reactant reacts with an achiral reducing agent, sodium borohydride, to generate an equimolar mixture of the chiral enantiomers of the product. For example, an...
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Molecules that possess multiple chiral centers can afford a large number of stereoisomers. For instance, while some molecules like 2-butanol have one chiral center, defined as a tetrahedral carbon atom with four different substituents attached, several molecules like butane-2,3-diol have multiple chiral centers. A simple formula to predict the number of stereoisomers possible for a molecule with n chiral centers is 2n. However, there can be a lower number where some of the stereoisomers are...
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Chirality is most prevalent in carbon-based tetrahedral compounds, but this important facet of molecular symmetry extends to sp3-hybridized nitrogen, phosphorus and sulfur centers, including trivalent molecules with lone pairs. Here, the lone pair behaves as a functional group in addition to the other three substituents to form an analogous tetrahedral center that can be chiral.
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A Micropatterning Assay for Measuring Cell Chirality
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Self-Learning Perfect Optical Chirality via a Deep Neural Network.

Yu Li1,2, Youjun Xu3, Meiling Jiang1,2

  • 1School of Physics, State Key Lab for Mesoscopic Physics, Academy for Advanced Interdisciplinary Studies, Nano-optoelectronics Frontier Center of Ministry of Education, Peking University, Beijing 100871, China.

Physical Review Letters
|December 7, 2019
PubMed
Summary
This summary is machine-generated.

We developed a new AI framework combining Bayesian optimization and deep convolutional neural networks to design metallic nanostructures with enhanced optical chirality. This approach optimizes light-matter interactions for advanced applications.

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Area of Science:

  • Optics and Photonics
  • Materials Science
  • Artificial Intelligence

Background:

  • Optical chirality is crucial for manipulating light polarization states.
  • Designing nanostructures with strong chirality requires advanced methods.
  • Artificial intelligence offers novel approaches for light-matter interaction studies.

Purpose of the Study:

  • To develop a self-consistent framework for calculating and optimizing optical properties of metallic nanostructures.
  • To leverage AI for designing nanostructures with enhanced optical chirality.
  • To provide insights into the origin of optimized optical properties.

Main Methods:

  • Integration of Bayesian optimization and deep convolutional neural network algorithms.
  • Calculation of near-field electric-field distributions.
  • Analysis of far-field reflection spectra.

Main Results:

  • A self-consistent framework for optical property optimization was established.
  • The AI framework successfully suggested improved nanostructure designs.
  • The method enabled self-learning of structure-property relationships.

Conclusions:

  • The developed AI framework accelerates the design of nanostructures with tailored optical chirality.
  • This approach facilitates understanding and predicting light-matter interactions in nanostructures.
  • The framework has broad applicability in nanophotonics and materials design.