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We synthesized a TS-1 catalyst to directly hydroxylate benzene to phenol with H 2 O 2 as oxidant and water as solvent. The samples were characterized by FT-IR (Fourier Transform Infrared), DR UV-Vis (Diffused Reflectance Ultraviolet Visible), XRD (X-ray diffraction), SEM(scanning electron microscope), TEM (Transmission Electron Microscope), XPS (X-ray photoelectron spectroscopy), ICP (inductively coupled plasma spectrum), and N 2 adsorption-desorption. A desirable phenol yield of 39% with 72% selectivity was obtained under optimized conditions: 0.15 g (0.34 to the mass of benzene) TS-1, 5.6 mmol C 6 H 6 , reaction time 45 min, 0.80 mL H 2 O 2 (30%), 40.0 mL H 2 O, and reaction temperature 70 • C. The reuse of the TS-1 catalyst illustrated that the catalyst had a slight loss of activity resulting from slight Ti leaching from the first run and then kept stable. Almost all of the Ti species added in the preparation were successfully incorporated into the TS-1 framework, which were responsible for the good catalytic activity. Extraframework Ti species were not selective for hydroxylation.

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Purpose To improve estimation of myelin water fraction (MWF) in the brain from multi‐echo gradient‐echo imaging data. Methods A systematic sensitivity analysis was first conducted to characterize the conventional exponential models used for MWF estimation. A new estimation method was then proposed for improved estimation of MWF from practical gradient‐echo imaging data. The proposed method uses an extended signal model that includes a finite impulse response filter to compensate for practical signal variations. This new model also enables the use of prelearned parameter distributions as well as low‐rank signal structures to improve parameter estimation. The resulting parameter estimation problem was solved optimally in the Bayesian sense. Results Our sensitivity analysis results showed that the conventional exponential models were very sensitive to measurement noise and modeling errors. Our simulation and experimental results showed that our proposed method provided a substantial improvement in reliability, reproducibility, and robustness of MWF estimates over the conventional methods. Clinical results obtained from stroke patients indicated that the proposed method, with its improved capability, could reveal the loss of myelin in lesions, demonstrating its translational potentials. Conclusion This paper addressed the problem of robust MWF estimation from gradient‐echo imaging data. A new method was proposed to provide improved MWF estimation in the presence of significant noise and modeling errors. The performance of the proposed method has been evaluated using both simulated and experimental data, showing significantly improved robustness over the existing methods. The proposed method may prove useful for quantitative myelin imaging in clinical applications.