In this study, the magnetically-induced vibrations of functionalized magnetic particle suspensions were probed for the development of a novel optical spectroscopy technique. Through this work (1) the frequency-dependence of the faraday rotation in ferrofluids and (2) the extension of this system to elucidating particle size and conformation as an alternative immunossay to costly and labor/time intensive Western Blotting and ELISA has been shown. With its sensitivity and specificity, this method has proven to be a promising multi-functional tool in biosensing, diagnostic, and therapeutic nanomedicine efforts. Due to its ubiquitous nature in all optically-transparent materials, the farady rotation, or circular birefringence, was developed as a robust and sensitive nanoscale biomolecule characterization technique through Brownian relaxation studies of particle suspensions. Current efforts have shown the applicability of this phenomenon in solid, pure liquid, and colloidal samples as well as simultaneous advancements of magnetic nanoparticle research in the magnetometric and magneto-optical regimes. By merging these two fields, a clinically relevant spectroscopy (fd-FRS, Frequency Domain Faraday Rotation Spectroscopy) was developed based on a newly revised model stemming from Debye relazation theory. Through this work, an optical bench with a variable permeability core electromagnet and a frequency-domain lock-in amplifier setup (DC to 20 kHz) have been used to distinguish between Fe3O4-core nanoparticles with functionalization layers of PEG4/PEG8 polymer with future applications involving the Anti-BSA/BSA antibody/antigen couple. Particle concentrations down to 500 nM (magnetic nanoparticles) and 0.01 Volume % (magnetic beads) were studied with diameters ranging from 200 nm to 1μm. currently, the characteristic peak corresponding to the out-of-phase relazation of the suspended particles has been elusive, despite a wide particle size distribution and the use of a balanced photodetector. Future work will involved highly monodisperse samples, faster scan times, and thermal characterization applications of fs-FRS.

The transport of a biomolecule through a nanopore occurs in many biological functions such as, DNA or RNA transport across nuclear pores and the translocation of proteins across the eukaryotic endoplasmic reticulum. In addition to the biological processes, it has potential applications in technology such as, drug delivery, gene therapy, and single molecule sensing. The DNA translocation through a synthetic nanopore device is considered as the basis for cheap and fast sequencing technology. Motivated by the experimental advances, many theoretical models have been developed. In this thesis, we explore the dynamics of driven translocation of a semiflexible polymer through a nanopore in two dimensions (2D) using Langevin dynamics (LD) simulation. By carrying out extensive simulation as a function of different parameters such as, driving force, length and rigidity of the chain, viscosity of the solvent, and diameter of the nanopore, we provide a detailed description of the translocation process. Our studies are relevant for fundamental understanding of the translocation process which is essential for making accurate nano-pore based devices.

京都大学 / 新制・課程博士 / 博士(理学) / 甲第24167号 / 理博第4858号 / 新制||理||1695(附属図書館) / 京都大学大学院理学研究科物理学・宇宙物理学専攻 / (主査)教授 早川 尚男, 教授 佐々 真一, 教授 山本 潤 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

This thesis compiles the publications I coauthored during my doctoral studies at University of Leipzig on the subject of optimizing thermodynamic systems, focusing on three optimization perspectives: maximum efficiency, maximum power, and maximum efficiency at given power. We considered two currently intensely studied models in finite-time thermodynamics, i.e., low-dissipation models and Brownian systems. The low-dissipation model is used to derive general bounds on the performance of real-world machines, while Brownian systems allow us to better understand the practical limits and features of small systems. First, we derived maximum efficiency at given power for various low-dissipation setups, with a particular focus on the behavior close to maximum power, which helps us to determine whether it is more beneficial to operate the system at maximum power, near maximum power or in a different regime. Then, we move to the design of maximum-efficiency and maximum-power protocols for Brownian systems under different boundary conditions. Particularly, when the constraints on control parameters are experimentally motivated, we presented a geometric method yielding maximum-efficiency and maximum-power protocols valid for systems with periodically scaled energy spectrum and otherwise arbitrary dynamics. Each chapter contains a short informal introduction to the matter as well as an outlook, pointing out the direction for our research in the future.

We investigate coarse-grained models of suspended self-thermophoretic microswimmers. Upon heating, the Janus spheres, with hemispheres made of different materials, induce a heterogeneous local solvent temperature that causes the self-phoretic particle propulsion. Starting from molecular dynamics simulations that schematically resolve the molecular composition of the solvent and the microswimmer, we verify the coarse-grained description of the fluid in terms of a local molecular temperature field, and its role for the particle’s thermophoretic self-propulsion and hot Brownian motion. The latter is governed by effective nonequilibrium temperatures, which are measured from simulations by confining the particle position and orientation. They are theoretically shown to remain relevant for any further spatial coarse-graining towards a hydrodynamic description of the entire suspension as a homogeneous complex fluid.

The Maxwell–Boltzmann distribution is a hallmark of statistical physics in thermodynamic equilibrium linking the probability density of a particle’s kinetic energies to the temperature of the system that also determines its configurational fluctuations. This unique relation is lost for Hot Brownian Motion, e.g., when the Brownian particle is constantly heated to create an inhomogeneous temperature in the surrounding liquid. While the fluctuations of the particle in this case can be described with an effective temperature, it is not unique for all degrees of freedom and suggested to be different at different timescales. In this work, we report on our progress to measure the effective temperature of Hot Brownian Motion in the ballistic regime. We have constructed an optical setup to measure the displacement of a heated Brownian particle with a temporal resolution of 10 ns giving a corresponding spatial resolution of about 23 pm for a 0.92 μm PMMA particle in water. Using a goldcoated polystyrene (AuPS) particle of 2.15 μm diameter we determine the mean squared displacement of the particle over more than six orders of magnitude in time. Our data recovers the trends for the effective temperature at long timescales, yet shows also clear effects in the region of hydrodynamic long time tails.

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Yes / The most recent face recognition systems are mainly dependent on feature representations obtained using either local handcrafted-descriptors, such as local binary patterns (LBP), or use a deep learning approach, such as deep belief network (DBN). However, the former usually suffers from the wide variations in face images, while the latter usually discards the local facial features, which are proven to be important for face recognition. In this paper, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the DBN is proposed to address the face recognition problem in unconstrained conditions. Firstly, a novel multimodal local feature extraction approach based on merging the advantages of the Curvelet transform with Fractal dimension is proposed and termed the Curvelet–Fractal approach. The main motivation of this approach is that theCurvelet transform, a newanisotropic and multidirectional transform, can efficiently represent themain structure of the face (e.g., edges and curves), while the Fractal dimension is one of the most powerful texture descriptors for face images. Secondly, a novel framework is proposed, termed the multimodal deep face recognition (MDFR)framework, to add feature representations by training aDBNon top of the local feature representations instead of the pixel intensity representations. We demonstrate that representations acquired by the proposed MDFR framework are complementary to those acquired by the Curvelet–Fractal approach. Finally, the performance of the proposed approaches has been evaluated by conducting a number of extensive experiments on four large-scale face datasets: the SDUMLA-HMT, FERET, CAS-PEAL-R1, and LFW databases. The results obtained from the proposed approaches outperform other state-of-the-art of approaches (e.g., LBP, DBN, WPCA) by achieving new state-of-the-art results on all the employed datasets.

Yes / n this paper, a powerful face recognition system for authentication and identification tasks is presented and a new facial feature extraction approach is proposed. A novel feature extraction method based on combining the characteristics of the Curvelet transform and Fractal dimension transform is proposed. The proposed system consists of four stages. Firstly, a simple preprocessing algorithm based on a sigmoid function is applied to standardize the intensity dynamic range in the input image. Secondly, a face detection stage based on the Viola-Jones algorithm is used for detecting the face region in the input image. After that, the feature extraction stage using a combination of the Digital Curvelet via wrapping transform and a Fractal Dimension transform is implemented. Finally, the K-Nearest Neighbor (K-NN) and Correlation Coefficient (CC) Classifiers are used in the recognition task. Lastly, the performance of the proposed approach has been tested by carrying out a number of experiments on three well-known datasets with high diversity in the facial expressions: SDUMLA-HMT, Faces96 and UMIST datasets. All the experiments conducted indicate the robustness and the effectiveness of the proposed approach for both authentication and identification tasks compared to other established approaches.