Long-Lived, Energetic States of Small Molecules: Spectroscopy, Pattern Recognition, and Formation/Destruction Mechanisms

Abstract

Metastable, electronically excited states of atoms and small molecules have chemical and photophysical properties that are relevant to Air Force missions in communication, upper atmospheric modeling, and high speed vehicle detection; tracking, and identification A second-generation supersonic molecular beam apparatus with optimized multispectral capabilities has been designed, constructed, and tested. A series of experiments on acetylene has defined the fundamental mechanisms of three complementary signal channels: UV-LIF (Ultraviolet Laser Induced Fluorescence), IR-LIF (Laser Induced infrared Fluorescence from photofragments) and SEELEM (Surface Electron Ejection from Laser Excited Metastable molecules). Simultaneous measurements of S1, T1; and T3 characters of each eigenstate permit the causal mechanisms of photoexcitation, intersystem crossing, and collisional deexcitation to be identified. Powerful statistical pattern-recognition techniques have been developed which permit the disentanglement of key structural and dynamical features in the spectra and the ability to distinguish between direct (statistical) and doorway-mediated (causal) limiting mechanisms for intersystein crossing.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 2002
Accession Number
ADA399721

Entities

People

  • Robert J. Silbey
  • Robert W. Field

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Alkynes
  • Chemistry
  • Detection
  • Detectors
  • Electrons
  • Infrared Detectors
  • Laser Applications
  • Laser Beams
  • Laser Induced Fluorescence
  • Laser Spectroscopy
  • Lasers
  • Measurement
  • Pattern Recognition
  • Spectra
  • Spectroscopy
  • Spin-Orbit Interaction
  • Ultraviolet Lasers

Fields of Study

  • Physics

Readers

  • Molecular Photonics/Laser Physics
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • Directed Energy
  • Directed Energy - Lasers
  • Hypersonics
  • Hypersonics - Hypersonic Flight
  • Microelectronics