Catalytic conversion of glucose to 5-hydroxymethylfurfural as a potential biorenewable platform chemical

The overall goal of the present work is to devise a catalyst system, in which novel catalyst and reactor configuration design will be synergistically performed on the dehydration of glucose. We have successfully demonstrated in the following chapters that MCl3-type Lewis acids are effective catalyst to realize the efficient HMF production. Despite a number of reports on this strategy by us and other groups, a generalized frame to understand the intrinsic properties of employed Lewis acids and kinetic information relevant to processing development on this type of catalysts will also be explored in a systematic manner. After extensively reviewing the challenges and opportunities of HMF production from sugars, we first report the catalytic conversion of glucose in high yields (62%) to HMF using a Lewis acid metal chloride (e.g., AlCl3) and a Brønsted acid (HCl) in a biphasic reactor consisting of water and an alkylphenol compound (2-sec-butylphenol) as the organic phase. The conversion of glucose in the presence of Lewis and Brønsted acidity proceeds through a tandem pathway involving isomerization of glucose to fructose, followed by dehydration of fructose to HMF. The organic phase extracts 97% of the HMF produced, while both acid catalysts remain in the aqueous phase. Water-compatible lanthanide-based Lewis acids were further tested to be able to catalyze the reaction under near-neutral conditions (pH=5.5) and a moderately high yield of 42 mol% could be obtained. The combined catalytic system also showed effectiveness to convert other polysaccharides to HMF. Furthermore, the aqueous phase was recycled and used for multiple times without significant loss of catalytic performance. Further effort to understand the factors governing catalyst activities/selectivites was also undertaken. The glucose conversion kinetic profile was used to reflect the Lewis ac id character of different metal ions. It was found that the pH value of the aqueous solution played an important role in controlling the Lewis activities. For the lanthanide chlorides, their Lewis acidity was comparable under the pH values studied (from 2.5 to 5.5). However, the Lewis acidity strength of other metal salts, such as aluminum chloride, showed dependence on the pH value of the solution. Activation energies with various Lewis acids were also calculated with both glucose and fructose to obtain more insight about the strength of the catalyst-substrate interaction as well as the dehydration reaction. The kinetic isotope effect with labeled glucose molecules was also studied to explore a more mechanistic understanding of the dehydration, which likely involves the 2-H atom of the glucose molecule in the transition state. While understanding the Lewis acidities using homogeneous model catalysts can be insightful, the ultimate practice of catalyst/catalytic processes likely necessitates the development of heteroegeneous catalysts. In this regard, a robust and sustainable catalyst preparation method pyrolizing glucose and taurine in the presence of CNT to obtain a versatile solid acids has been demonstrated. Characterization and textual properties of the catalysts were probed through the utilization of TEM, SEM, TGA, XPS. Additionally, solid state nuclear magnetic resonance(ssNMR) spectroscopy has been exploited to further elucidate the chemical nature of carbon species deposited on the surface of CNT. Al(OTf)3 as a model Lewis acidic metal salt was successfully supported on sulfonic groups tethered CNTs and tested for C6 sugar dehydration for the production of HMF in tetrahydrofuran(THF)/water solvent system with good recyclability. In addition, an integrated catalytic pathway has also been demonstrated to utilize HMF-derived 1,2,6-hexanetriol as starting materials for the production of nylon 6,6 monomers-adipic acid and hexanediamine. To realize this goal, gold nanoparticle supported on both carbon and metal oxide based materials have been synthesized and tested for 1,6-hexanediol oxidation using molecular oxygen. On the other hand, homogeneous iodine-containing molecules have been exploited for the conversion of 1,6-hexanediol to adiponitrile, which can be subsequently hydrogenated to produce the desired diamine

Accurate Eye Tracking from Dense 3D Surface Reconstructions using Single-Shot Deflectometry

Eye-tracking plays a crucial role in the development of virtual reality devices, neuroscience research, and psychology. Despite its significance in numerous applications, achieving an accurate, robust, and fast eye-tracking solution remains a considerable challenge for current state-of-the-art methods. While existing reflection-based techniques (e.g., "glint tracking") are considered the most accurate, their performance is limited by their reliance on sparse 3D surface data acquired solely from the cornea surface. In this paper, we rethink the way how specular reflections can be used for eye tracking: We propose a novel method for accurate and fast evaluation of the gaze direction that exploits teachings from single-shot phase-measuring-deflectometry (PMD). In contrast to state-of-the-art reflection-based methods, our method acquires dense 3D surface information of both cornea and sclera within only one single camera frame (single-shot). Improvements in acquired reflection surface points("glints") of factors $>3300 \times$ are easily achievable. We show the feasibility of our approach with experimentally evaluated gaze errors of only $\leq 0.25^\circ$ demonstrating a significant improvement over the current state-of-the-art

Channel Capacity and Bounds In Mixed Gaussian-Impulsive Noise

Communication systems suffer from the mixed noise consisting of both non-Gaussian impulsive noise (IN) and white Gaussian noise (WGN) in many practical applications. However, there is little literature about the channel capacity under mixed noise. In this paper, we prove the existence of the capacity under p-th moment constraint and show that there are only finite mass points in the capacity-achieving distribution. Moreover, we provide lower and upper capacity bounds with closed forms. It is shown that the lower bounds can degenerate to the well-known Shannon formula under special scenarios. In addition, the capacity for specific modulations and the corresponding lower bounds are discussed. Numerical results reveal that the capacity decreases when the impulsiveness of the mixed noise becomes dominant and the obtained capacity bounds are shown to be very tight

Breathing New Life into 3D Assets with Generative Repainting

Diffusion-based text-to-image models ignited immense attention from the vision community, artists, and content creators. Broad adoption of these models is due to significant improvement in the quality of generations and efficient conditioning on various modalities, not just text. However, lifting the rich generative priors of these 2D models into 3D is challenging. Recent works have proposed various pipelines powered by the entanglement of diffusion models and neural fields. We explore the power of pretrained 2D diffusion models and standard 3D neural radiance fields as independent, standalone tools and demonstrate their ability to work together in a non-learned fashion. Such modularity has the intrinsic advantage of eased partial upgrades, which became an important property in such a fast-paced domain. Our pipeline accepts any legacy renderable geometry, such as textured or untextured meshes, orchestrates the interaction between 2D generative refinement and 3D consistency enforcement tools, and outputs a painted input geometry in several formats. We conduct a large-scale study on a wide range of objects and categories from the ShapeNetSem dataset and demonstrate the advantages of our approach, both qualitatively and quantitatively. Project page: https://www.obukhov.ai/repainting_3d_asset

Low-Rank Based Image Analyses for Pathological MR Image Segmentation and Recovery

The presence of pathologies in magnetic resonance (MR) brain images causes challenges in various image analysis areas, such as registration, atlas construction and atlas-based segmentation. We propose a novel method for the simultaneous recovery and segmentation of pathological MR brain images. Low-rank and sparse decomposition (LSD) approaches have been widely used in this field, decomposing pathological images into (1) low-rank components as recovered images, and (2) sparse components as pathological segmentation. However, conventional LSD approaches often fail to produce recovered images reliably, due to the lack of constraint between low-rank and sparse components. To tackle this problem, we propose a transformed low-rank and structured sparse decomposition (TLS2D) method. The proposed TLS2D integrates the structured sparse constraint, LSD and image alignment into a unified scheme, which is robust for distinguishing pathological regions. Furthermore, the well recovered images can be obtained using TLS2D with the combined structured sparse and computed image saliency as the adaptive sparsity constraint. The efficacy of the proposed method is verified on synthetic and real MR brain tumor images. Experimental results demonstrate that our method can effectively provide satisfactory image recovery and tumor segmentation