M.Tech.

Optical trapping techniques have become an important and irreplaceable tool in many research disciplines for reaching non-invasively into the microscopic world and to manipulate, cut, assemble and transform micro-objects with nanometer precision and sub-micrometer resolution. Further advances in optical trapping techniques promise to bridge the gap and bring together the macroscopic world and experimental techniques and applications of Microsystems in areas of physics, chemistry and biology. In order to understand the optical trapping process and to improve and tailor experimental techniques and applications in a variety of scientific disciplines, an accurate knowledge of trapping forces exerted on particles and their dependency on environmental and morphological factors is of crucial importance. Furthermore, the recent trend in novel laser trapping experiments sees the use of complex laser beams in trapping arrangements for achieving more controllable laser trapping techniques. Focusing of such beams with a high numerical aperture (NA) objective required for efficient trapping leads to a complicated amplitude, phase and polarisation distributions of an electromagnetic field in the focal region. Current optical trapping models based on ray optics theory and the Gaussian beam approximation are inadequate to deal with such a focal complexity. Novel applications of the laser trapping such as the particle-trapped scanning near field optical microscopy (SNOM) and optical-trap nanometry techniques are currently investigated largely in the experimental sense or with approximated theoretical models. These applications are implemented using the efficient laser trapping with high NA and evanescent wave illumination of the sample for high resolution sensing. The proper study of these novel laser trapping applications and the potential benefits of implementation of these applications with complex laser beams requires an exact physical model for the laser trapping process and a nanometric sensing model for detection of evanescent wave scattering. This thesis is concerned with comprehensive and rigorous modelling and characterisation of the trapping process of spherical dielectric particles implemented using far-field and near-field optical trapping modalities. Two types of incident illuminations are considered, the plane wave illumination and the doughnut beam illumination of various topological charges. The doughnut beams represent one class of complex laser beams. However, our optical trapping model presented in this thesis is in no way restricted to this type of incident illumination, but is equally applicable to other types of complex laser beam illuminations. Furthermore, the thesis is concerned with development of a physical model for nanometric sensing, which is of great importance for optical trapping systems that utilise evanescent field illumination for achieving high resolution position monitoring and imaging. The nanometric sensing model, describing the conversion of evanescent photons into propagating photons, is realised using an analytical approach to evanescent wave scattering by a microscopic particle. The effects of an interface at which the evanescent wave is generated are included by considering the scattered field reflection from the interface. Collection and imaging of the resultant scattered field by a high numerical aperture objective is described using vectorial diffraction theory. Using our sensing model, we have investigated the dependence of the scattering on the particle size and refractive index, the effects of the interface on the scattering cross-section, morphology dependent resonance effects associated with the scattering process, and the effects of the incident angle of a laser beam undergoing total internal reflection to generate an evanescent field. Furthermore, we have studied the detectability of the scattered signal using a wide area detector and a pinhole detector. A good agreement between our experimental measurements of the focal intensity distribution in the back focal region of the collecting objective and the theoretical predictions confirm the validity of our approach. The optical trapping model is implemented using a rigorous vectorial diffraction theory for characterisation of the electromagnetic field distribution in the focal region of a high NA objective. It is an exact model capable of considering arbitrary amplitude, phase and polarisation of the incident laser beam as well as apodisation functions of the focusing objective. The interaction of a particle with the complex focused field is described by an extension of the classical plane wave Lorentz-Mie theory with the expansion of the incident field requiring numerical integration of finite surface integrals only. The net force exerted on the particle is then determined using the Maxwell stress tensor approach. Using the optical trapping model one can consider the laser trapping process in the far-field of the focusing objective, also known as the far-field trapping, and the laser trapping achieved by focused evanescent field, i.e. near-field optical trapping. Investigations of far-field laser trapping show that spherical aberration plays a significant role in the trapping process if a refractive index mismatch exists between the objective immersion and particle suspension media. An optical trap efficiency is severely degraded under the presence of spherical aberration. However, our study shows that the spherical aberration effect can be successfully dealt with using our optical trapping model. Theoretical investigations of the trapping process achieved using an obstructed laser beam indicate that the transverse trapping efficiency decreases rapidly with increasing size of the obstruction, unlike the trend predicted using a ray optics model. These theoretical investigations are in a good agreement with our experimentally observed results. Far-field optical trapping with complex doughnut laser beams leads to reduced lifting force for small dielectric particles, compared with plane wave illumination, while for large particles it is relatively unchanged. A slight advantage of using a doughnut laser beam over plane wave illumination for far-field trapping of large dielectric particles manifests in a higher forward axial trapping efficiency, which increases for increasing doughnut beam topological charge. It is indicated that the maximal transverse trapping efficiency decreases for reducing particle size and that the rate of decrease is higher for doughnut beam illumination, compared with plane wave illumination, which has been confirmed by experimental measurements. A near-field trapping modality is investigated by considering a central obstruction placed before the focusing objective so that the obstruction size corresponds to the minimum convergence angle larger than the critical angle. This implies that the portion of the incident wave that is passed through the high numerical aperture objective satisfies the total internal reflection condition at the surface of the coverslip, so that only a focused evanescent field is present in the particle suspension medium. Interaction of this focused near-field with a dielectric micro-particle is described and investigated using our optical trapping model with a central obstruction. Our investigation shows that the maximal backward axial trapping efficiency or the lifting force is comparable to that achieved by the far-field trapping under similar conditions for either plane wave illumination or complex doughnut beam illumination. The dependence of the maximal axial trapping efficiency on the particle size is nearly linear for near-field trapping with focused evanescent wave illumination in the Mie size regime, unlike that achieved using the far-field trapping technique.

Zugl.: Trier, Univ., Diss., 2006 u.d.T.: Haaf, Holger M.: Finanzmarktmanipulationen am Beispiel von Futures - der symmetrische Fall

<p>Debattartikel</p>

The physical manipulation of biological cells is very attractive now in biotechnology (Butler, 1991)) because it opens the possibility of examining and manipulating single molecules. Other methods are based on chemical effects, electrical effects, etc., and they generally do not allow researchers to examine single molecules cell and, thus, to understand their interaction which may encode many useful pieces of information. Such physical manipulation is fully performed by robotic devices. <p> In order to automate the process of physical manipulation, micro machine vision for the fast identification of the objects involved is required. Typical objects that are involved are cells, cell elements, holders and injectors. <p> In the research described in this thesis, which was carried out in the Advanced Engineering Design Laboratory of the Mechanical Engineering Department, University of Saskatchewan, algorithms for the three objects (the cell, holder and injector) were developed, implemented and tested. The results obtained have shown that the fastest identification times for these three objects are respectively 0.12s for the cell oocyte, 6.78s/100 frames for the holder, and 6.72s/100 frames for the injector. These performances are acceptable in the context of the physical manipulation of biological cells.<p> The goal of the research described in this thesis was to develop algorithms that would give a fast recognition of the cell manipulation system. With the aid of the algorithms, an automatic operation of the cell manipulation system would be achieved. Image process and pattern recognition techniques were used in developing the Visual C++ GUI algorithms that would automatically recognize the components of the cell manipulation system for the purpose of manipulating the cells.

The physical manipulation of biological cells is very attractive now in biotechnology (Butler, 1991)) because it opens the possibility of examining and manipulating single molecules. Other methods are based on chemical effects, electrical effects, etc., and they generally do not allow researchers to examine single molecules cell and, thus, to understand their interaction which may encode many useful pieces of information. Such physical manipulation is fully performed by robotic devices. <p> In order to automate the process of physical manipulation, micro machine vision for the fast identification of the objects involved is required. Typical objects that are involved are cells, cell elements, holders and injectors. <p> In the research described in this thesis, which was carried out in the Advanced Engineering Design Laboratory of the Mechanical Engineering Department, University of Saskatchewan, algorithms for the three objects (the cell, holder and injector) were developed, implemented and tested. The results obtained have shown that the fastest identification times for these three objects are respectively 0.12s for the cell oocyte, 6.78s/100 frames for the holder, and 6.72s/100 frames for the injector. These performances are acceptable in the context of the physical manipulation of biological cells.<p> The goal of the research described in this thesis was to develop algorithms that would give a fast recognition of the cell manipulation system. With the aid of the algorithms, an automatic operation of the cell manipulation system would be achieved. Image process and pattern recognition techniques were used in developing the Visual C++ GUI algorithms that would automatically recognize the components of the cell manipulation system for the purpose of manipulating the cells.

Thesis (M. Dip. Tech.)--Technikon Witwatersrand, 2002. / Supervisor: Dr. Harold Humphries. Also available via World Wide Web.

Thesis (M.Tech. (Chiropractic))--Technikon Witwatersrand, 2004. / Supervisor: Barrett Losco; co-supervisor: Pauline Moolman. Also available via World Wide Web.

Thesis (M. Tech.(Chiropractic))--Technikon Witwatersrand, 2004. / Supervisor: Malany Moodley ; Co-supervisor: Neil De Villiers. Includes abstract. Includes bibliographical references (leaves 107-114). Also available via World Wide Web.

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