Hydrogen Isotope Separation in Metal-Organic Frameworks

In this thesis we present our research on hydrogen isotope separation using metal-organic frameworks (MOFs). Deuterium is one of the two stable isotopes of hydrogen. Despite its wide range of application, currently there is no ideal industrial method that can separate deuterium in a fast and efficient fashion. MOFs are a class of porous materials consisting of metal ions or clusters connected by organic ligands. They have shown great potential in separating hydrogen isotopes via quantum sieving effect. In this thesis, we first provide background on two state-of-art MOFs, Co-MOF-74 and Cu(I)-MFU-4l. Then we elaborate on the statistical theory of selectivity, the mechanism of separation and the basic idea of mass spectrometry, which is the main analytical technique used in this project. We present temperature programmed desorption (TPD) spectra for both samples. Direct separation measurement is made with Co-MOF-74. We confirm that TPD spectra can predict the results of direct separation measurements. The TPD spectra of Cu(I)-MFU-4l predict a selectivity of approximately 6 at easily accessible temperatures (~260K). This shows the practicality of using Cu(I)-MFU-4l for hydrogen isotope separation. Preferential adsorption separation is also performed with Co-MOF-74. The extracted activation energy agrees to within 10% of literature predictions based on quantum zero point energy models

Body Mass Index (BMI) Change and All-Cause Mortality in the Middle-aged and Older Population

M.S. Thesis. University of Hawaiʻi at Mānoa 2017

Clutter Suppression in Ultrasound: Performance Evaluation of Low-Rank and Sparse Matrix Decomposition Methods

Vessel diseases are often accompanied by abnormalities related to vascular shape and size. Therefore, a clear visualization of vasculature is of high clinical significance. Ultrasound Color Flow Imaging (CFI) is one of the prominent techniques for flow visualization. However, clutter signals originating from slow-moving tissue is one of the main obstacles to obtain a clear view of the vascular network. Enhancement of the vasculature by suppressing the clutters is an essential step for many applications of ultrasound CFI. In this thesis, we focus on a state-of-art algorithm framework called Decomposition into Low-rank and Sparse Matrices (DLSM) framework for ultrasound clutter suppression. Currently, ultrasound clutter suppression is often performed by Singular Value Decomposition (SVD) of the data matrix, which is a branch of eigen-based filtering. This approach exhibits two well-known limitations. First, the performance of SVD is sensitive to the proper manual selection of the ranks corresponding to clutter and blood subspaces. Second, SVD is prone to failure in the presence of large random noise in the data set. A potential solution to these issues is the use of DLSM framework. SVD, as a means for singular values, is also one of the widely used algorithms for solving the minimization problem under the DLSM framework. Many other algorithms under DLSM avoid full SVD and use approximated SVD or SVD-free ideas which may have better performance with higher robustness and lower computing time due to the expensive computational cost of full SVD. In practice, these models separate blood from clutter based on the assumption that steady clutter represents a low-rank structure and the moving blood component is sparse. In this thesis, we exploit the feasibility of exploiting low-rank and sparse decomposition schemes, originally developed in the field of computer vision, in ultrasound clutter suppression. Since ultrasound images have different texture and statistical properties compared to images in computer vision, it is of high importance to evaluate how these methods translate to ultrasound CFI. We conduct this evaluation study by adapting 106 DLSM algorithms and validating them against simulation, phantom and in vivo rat data sets. The advantage of simulation and phantom experiments is that the ground truth vessel map is known, and the advantage of the in vivo data set is that it enables us to test algorithms in a realistic setting. Two conventional quality metrics, Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR), are used for performance evaluation. In addition, computation times required by different algorithms for generating the clutter suppressed images are reported. Our extensive analysis shows that the DLSM framework can be successfully applied to ultrasound clutter suppression

Clutter suppression in ultrasound: performance evaluation and review of low-rank and sparse matrix decomposition methods

Vessel diseases are often accompanied by abnormalities related to vascular shape and size. Therefore, a clear visualization of vasculature is of high clinical significance. Ultrasound color flow imaging (CFI) is one of the prominent techniques for flow visualization. However, clutter signals originating from slow-moving tissue are one of the main obstacles to obtain a clear view of the vascular network. Enhancement of the vasculature by suppressing the clutters is a significant and irreplaceable step for many applications of ultrasound CFI. Currently, this task is often performed by singular value decomposition (SVD) of the data matrix. This approach exhibits two well-known limitations. First, the performance of SVD is sensitive to the proper manual selection of the ranks corresponding to clutter and blood subspaces. Second, SVD is prone to failure in the presence of large random noise in the dataset. A potential solution to these issues is using decomposition into low-rank and sparse matrices (DLSM) framework. SVD is one of the algorithms for solving the minimization problem under the DLSM framework. Many other algorithms under DLSM avoid full SVD and use approximated SVD or SVD-free ideas which may have better performance with higher robustness and less computing time. In practice, these models separate blood from clutter based on the assumption that steady clutter represents a low-rank structure and that the moving blood component is sparse. In this paper, we present a comprehensive review of ultrasound clutter suppression techniques and exploit the feasibility of low-rank and sparse decomposition schemes in ultrasound clutter suppression. We conduct this review study by adapting 106 DLSM algorithms and validating them against simulation, phantom, and in vivo rat datasets. Two conventional quality metrics, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), are used for performance evaluation. In addition, computation times required by different algorithms for generating clutter suppressed images are reported. Our extensive analysis shows that the DLSM framework can be successfully applied to ultrasound clutter suppression

Electronic anisotropy in magic-angle twisted trilayer graphene

Due to its potential connection with nematicity, electronic anisotropy has been the subject of intense research effort on a wide variety of material platforms. The emergence of spatial anisotropy not only offers a characterization of material properties of metallic phases, which cannot be accessed via conventional transport techniques, but it also provides a unique window into the interplay between Coulomb interaction and broken symmetry underlying the electronic order. In this work, we utilize a new scheme of angle-resolved transport measurement (ARTM) to characterize electron anisotropy in magic-angle twisted trilayer graphene. By analyzing the dependence of spatial anisotropy on moir\'e band filling, temperature and twist angle, we establish the first experimental link between electron anisotropy and the cascade phenomenon, where Coulomb interaction drives a number of isospin transitions near commensurate band fillings. Furthermore, we report the coexistence between electron anisotropy and a novel electronic order that breaks both parity and time reversal symmetry. Combined, the link between electron anisotropy, cascade phenomenon and PT-symmetry breaking sheds new light onto the nature of electronic order in magic-angle graphene moir\'e systems.Comment: Main text 7 pages, 5 figures. Total 17 pages, 15 figures. arXiv admin note: text overlap with arXiv:2209.1296

Momentum-polarized superconductivity in twisted trilayer graphene

The spin-configuration of a Cooper pair is often informative of the pairing symmetry. According to the BCS theory, a conventional, s-wave superconductor arises from Cooper pairing in the singlet channel. Whereas a triplet Cooper pair is directly linked to a spatial wavefunction in the p- or f-wave channel, a hallmark of unconventional superconductivity. In multilayer graphene, the nature of the pairing instability is further complicated by emergent orders in the momentum-space, such as valley and momentum polarization. The presence of momentum-space instability suggests that the spin channel alone is insufficient to describe the superconducting pairing symmetry. In this work, we use angle-resolved nonreciprocal transport measurement to investigate the influence of momentum-space instabilities. We uncover a new cascade phenomenon across the moir\'e band fillings, where a series of transitions between momentum-polarized states appear outside the regular sequence of Dirac revivals. Moreover, we identify a new aspect of superconductivity, which is defined by its coexistence with spontaneous momentum polarization. Our findings point towards a direct link between spontaneously broken rotational and time-reversal symmetries, which has intriguing implications on the nature of the pairing instability.Comment: Main text pages 1-7, 4 figures. Method pages 8-11, 5 figures. SI page 12-22, 15 figure

Determination of the effective dose of dexmedetomidine to achieve loss of consciousness during anesthesia induction

BackgroundDexmedetomidine (DEX) is a sedative with greater preservation of cognitive function, reduced respiratory depression, and improved patient arousability. This study was designed to investigate the performance of DEX during anesthesia induction and to establish an effective DEX induction strategy, which could be valuable for multiple clinical conditions.MethodsPatients undergoing abdominal surgery were involved in this dose-finding trial. Dixon's up-and-down sequential method was employed to determine the effective dose of DEX to achieve the state of “loss of consciousness”, and an effective induction strategy was established with continuous infusion of DEX and remifentanil. The effects of DEX on hemodynamics, respiratory state, EEG, and anesthetic depth were monitored and analyzed.ResultsThrough the strategy mentioned, the depth of surgical anesthesia was successfully achieved by DEX-led anesthesia induction. The ED50 and ED95 of the initial infusion rate of DEX were 0.115 and 0.200 μg/kg/min, respectively, and the mean induction time was 18.3 min. The ED50 and ED95 of DEX to achieve the state of “loss of consciousness” were 2.899 (95% CI: 2.703–3.115) and 5.001 (95% CI: 4.544–5.700) μg/kg, respectively. The mean PSI on the loss of consciousness was 42.8 among the patients. During anesthesia induction, the hemodynamics including BP and HR were stable, and the EEG monitor showed decreased α and β powers and increased θ and δ in the frontal and pre-frontal cortices of the brain.ConclusionThis study indicated that continuous infusion of combined DEX and remifentanil could be an effective strategy for anesthesia induction. The EEG during the induction was similar to the physiological sleep process

Hydrogen Isotope Separation in Metal-Organic Frameworks

In this thesis we present our research on hydrogen isotope separation using metal-organic frameworks (MOFs). Deuterium is one of the two stable isotopes of hydrogen. Despite its wide range of application, currently there is no ideal industrial method that can separate deuterium in a fast and efficient fashion. MOFs are a class of porous materials consisting of metal ions or clusters connected by organic ligands. They have shown great potential in separating hydrogen isotopes via quantum sieving effect. In this thesis, we first provide background on two state-of-art MOFs, Co-MOF-74 and Cu(I)-MFU-4l. Then we elaborate on the statistical theory of selectivity, the mechanism of separation and the basic idea of mass spectrometry, which is the main analytical technique used in this project. We present temperature programmed desorption (TPD) spectra for both samples. Direct separation measurement is made with Co-MOF-74. We confirm that TPD spectra can predict the results of direct separation measurements. The TPD spectra of Cu(I)-MFU-4l predict a selectivity of approximately 6 at easily accessible temperatures (~260K). This shows the practicality of using Cu(I)-MFU-4l for hydrogen isotope separation. Preferential adsorption separation is also performed with Co-MOF-74. The extracted activation energy agrees to within 10% of literature predictions based on quantum zero point energy models

Catalytic Reduction of Organic Dyes by Multilayered Graphene Platelets and Silver Nanoparticles in Polyacrylic Acid Hydrogel

Graphene oxide has been widely used in the oxidative degradation of environmental pollutants. However, its catalytic role can be questioned as graphene oxide with oxygen-containing functional groups may also act as reactant in oxidative reactions. Herein, hydrogel composites loaded with multilayered graphene platelets showed excellent catalytic performance for the reduction of a wastewater organic pollutant (methylene blue) under NaBH4, which proved the catalytic role of multilayered graphene platelets. The liquid-based direct exfoliation method was used to prepare two-dimensional materials, which is compatible with other liquid phase methods to prepare nanomaterials. Hydrogel composites composed of multilayered graphene platelets, silver nanoparticles, and polyacrylic acid hydrogels were synthesized in water solution under irradiation with ultraviolet light, demonstrating the advantages of synthesizing nanocomposites using the liquid-based direct exfoliation method

Research on experimental method of carbon nanotubes space environment effect

Due to its unique structure and superior performance, carbon nanotubes are proposed to have great potential applications in many fields. Recently, more and more concerns have been focused on the application of carbon nanotubes in space technology. It is believed that carbon nanotubes can have a broad impact on space missions in future with benefits principally in space technology, such as lightweight structure materials, environment protection materials, energy gerneration and storage and nanoelcetronics. However, carbon nanotubes would suffer chemical and physical damage from the space environment when it was used in spacecraft, just like all the other space materials. The environment where spacecrafts operate is extremely intricate, primarily represented by electromagnetic irradiation, charged particle irradiation, high vacuum, cold and hot alternation, atomic oxygen erosion, clash from space debris, and other factors. The mostly effect in space environment is atomic oxygen and debris for grapheme. This paper is about the research on the experimental methods of carbon nanotubes space environment effect with atomic oxygen and space debris as examples, in a bid to propose the experimental scheme of carbon nanotubes space environment effect. It will be great helpful to promote the applications of carbon nanotubes in space technology