The airborne microparticle: its physics, chemistry, optics, and transport phenomena

This book is an extensive yet self-contained reference of single microparticle studies as they have been performed for many years by the authors. With the range of theoretical and experimental tools available it has become possible to use the many unique properties of droplets and small particles to investigate phenomena as diverse as, linear and nonlinear optics, solution thermodynamics, gas/solid and gas/liquid chemical reactions, transport properties such as gas phase diffusion coefficients, rate processes in the continuum and non-continuum regimes, trace gas uptake by aerosol droplets related to atmospheric chemistry and ozone depletion, phoretic phenomena, Raman spectroscopy, particle charge, evaporation and condensation processes. Throughout the book the main concern of the authors was to provide the reader with a visualization of the significance and application of the theory by experimental results

Neural Network Analysis of the Resonance Whispering Gallery Mode Characteristics of Biological Agents

A novel technique for the label-free analysis of micro and nanoparticles including biomolecules using optical micro cavity resonance of whispering-gallery-type modes is being developed. Various schemes of the method using both standard and specially produced microspheres have been investigated to make further development for microbial application. It was demonstrated that optical resonance under optimal geometry could be detected under the laser power of less than 1 microwatt. The sensitivity of developed schemes has been tested by monitoring the spectral shift of the whispering gallery modes. Water solutions of ethanol, ascorbic acid, blood phantoms including albumin and HCl, glucose, biotin, biomarker like C reactive protein as well as bacteria and virus phantoms (gels of silica micro and nanoparticles) have been used. Structure of resonance spectra of the solutions was a specific subject of investigation. Probabilistic neural network classifier for biological agents and micro/nano particles classification has been developed. Several parameters of resonance spectra such as spectral shift, broadening, diffuseness and others have been used as input parameters to develop a network classifier for micro and nanoparticles and biological agents in solution. Classification probability of approximately 98 % for probes under investigation have been achieved. Developed approach have been demonstrated to be a promising technology platform for sensitive, lab-on-chip type sensor which can be used for development of diagnostic tools for different biological molecules, e.g. proteins, oligonucleotides, oligosaccharides, lipids, small molecules, viral particles, cells as well as in different experimental contexts e.g. proteomics, genomics, drug discovery, and membrane studies

Neural Network Analysis of the Resonance Whispering Gallery Mode Characteristics of Biological Agents

A novel technique for the label-free analysis of micro and nanoparticles including biomolecules using optical micro cavity resonance of whispering-gallery-type modes is being developed. Various schemes of the method using both standard and specially produced microspheres have been investigated to make further development for microbial application. It was demonstrated that optical resonance under optimal geometry could be detected under the laser power of less than 1 microwatt. The sensitivity of developed schemes has been tested by monitoring the spectral shift of the whispering gallery modes. Water solutions of ethanol, ascorbic acid, blood phantoms including albumin and HCl, glucose, biotin, biomarker like C reactive protein as well as bacteria and virus phantoms (gels of silica micro and nanoparticles) have been used. Structure of resonance spectra of the solutions was a specific subject of investigation. Probabilistic neural network classifier for biological agents and micro/nano particles classification has been developed. Several parameters of resonance spectra such as spectral shift, broadening, diffuseness and others have been used as input parameters to develop a network classifier for micro and nanoparticles and biological agents in solution. Classification probability of approximately 98 % for probes under investigation have been achieved. Developed approach have been demonstrated to be a promising technology platform for sensitive, lab-on-chip type sensor which can be used for development of diagnostic tools for different biological molecules, e.g. proteins, oligonucleotides, oligosaccharides, lipids, small molecules, viral particles, cells as well as in different experimental contexts e.g. proteomics, genomics, drug discovery, and membrane studies