Tsinghua University

About: Tsinghua University is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: Computer science & Catalysis. The organization has 129978 authors who have published 200506 publications receiving 4549561 citations. The organization is also known as: Tsinghua & THU.

Optimizing Binding Energies of Key Intermediates for CO2 Hydrogenation to Methanol over Oxide-Supported Copper.

Abstract: Rational optimization of catalytic performance has been one of the major challenges in catalysis. Here we report a bottom-up study on the ability of TiO2 and ZrO2 to optimize the CO2 conversion to methanol on Cu, using combined density functional theory (DFT) calculations, kinetic Monte Carlo (KMC) simulations, in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) measurements, and steady-state flow reactor tests. The theoretical results from DFT and KMC agree with in situ DRIFTS measurements, showing that both TiO2 and ZrO2 help to promote methanol synthesis on Cu via carboxyl intermediates and the reverse water–gas-shift (RWGS) pathway; the formate intermediates, on the other hand, likely act as a spectator eventually. The origin of the superior promoting effect of ZrO2 is associated with the fine-tuning capability of reduced Zr3+ at the interface, being able to bind the key reaction intermediates, e.g. *CO2, *CO, *HCO, and *H2CO, moderately to facilitate methanol formation. This ...

Recent developments in polydopamine: an emerging soft matter for surface modification and biomedical applications

Abstract: After more than four billion years of evolution, nature has created a large number of fascinating living organisms, which show numerous peculiar structures and wonderful properties. Nature can provide sources of plentiful inspiration for scientists to create various materials and devices with special functions and uses. Since Messersmith proposed the fabrication of multifunctional coatings through mussel-inspired chemistry, this field has attracted considerable attention for its promising and exiciting applications. Polydopamine (PDA), an emerging soft matter, has been demonstrated to be a crucial component in mussel-inspired chemistry. In this review, the recent developments of PDA for mussel-inspired surface modification are summarized and discussed. The biomedical applications of PDA-based materials are also highlighted. We believe that this review can provide important and timely information regarding mussel-inspired chemistry and will be of great interest for scientists in the chemistry, materials, biology, medicine and interdisciplinary fields.

Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface.

Abstract: Objective. Recently, canonical correlation analysis (CCA) has been widely used in steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs) due to its high efficiency, robustness, and simple implementation. However, a method with which to make use of harmonic SSVEP components to enhance the CCA-based frequency detection has not been well established. Approach. This study proposed a filter bank canonical correlation analysis (FBCCA) method to incorporate fundamental and harmonic frequency components to improve the detection of SSVEPs. A 40-target BCI speller based on frequency coding (frequency range: 8–15.8 Hz, frequency interval: 0.2 Hz) was used for performance evaluation. To optimize the filter bank design, three methods (M1: sub-bands with equally spaced bandwidths; M2: sub-bands corresponding to individual harmonic frequency bands; M3: sub-bands covering multiple harmonic frequency bands) were proposed for comparison. Classification accuracy and information transfer rate (ITR) of the three FBCCA methods and the standard CCA method were estimated using an offline dataset from 12 subjects. Furthermore, an online BCI speller adopting the optimal FBCCA method was tested with a group of 10 subjects. Main results. The FBCCA methods significantly outperformed the standard CCA method. The method M3 achieved the highest classification performance. At a spelling rate of ~33.3 characters/min, the online BCI speller obtained an average ITR of 151.18 ± 20.34 bits min−1. Significance. By incorporating the fundamental and harmonic SSVEP components in target identification, the proposed FBCCA method significantly improves the performance of the SSVEP-based BCI, and thereby facilitates its practical applications such as high-speed spelling.