Near-infrared light-driven biomass conversion.

Current photocatalytic technologies mainly rely on the input of high-energy ultraviolet-visible (UV-vis) light to obtain the desired excited states with adequate energy to drive redox reactions, precluding the use of low-energy near-infrared (NIR) light that occupies ~50% of the solar spectrum. Here, we report the efficient utilization of NIR light by coupling the low-energy NIR photons with reactive biomass conversion. A unique mechanism of photothermally synergistic photocatalysis was revealed for the selective biomass conversion under NIR light. Using biomass-derived 5-hydroxymethylfurfural (HMF) conversion as a model reaction, it was found that NIR and UV-vis light featured markedly different reaction patterns. 5-Formyl-2-furancarboxylic acid (FFCA) was almost exclusively produced under NIR light, whereas UV-vis light favored the formation of 2,5-diformylfuran (DFF) as the major product. This work provides a paradigm for sustainable and selective chemical synthesis using the Earth's abundant resources, sunlight and biomass.

Solving conformal defects in 3D conformal field theory using fuzzy sphere regularization.

Defects in conformal field theory (CFT) are of significant theoretical and experimental importance. The presence of defects theoretically enriches the structure of the CFT, but at the same time, it makes it more challenging to study, especially in dimensions higher than two. Here, we demonstrate that the recently-developed theoretical scheme, fuzzy (non-commutative) sphere regularization, provides a powerful lens through which one can dissect the defect of 3D CFTs in a transparent way. As a notable example, we study the magnetic line defect of 3D Ising CFT and clearly demonstrate that it flows to a conformal defect fixed point. We have identified 6 low-lying defect primary operators, including the displacement operator, and accurately extract their scaling dimensions through the state-operator correspondence. Moreover, we also compute one-point bulk correlators and two-point bulk-defect correlators, which show great agreement with predictions of defect conformal symmetry, and from which we extract various bulk-defect operator product expansion coefficients. Our work demonstrates that the fuzzy sphere offers a powerful tool for exploring the rich physics in 3D defect CFTs.

Operator Product Expansion Coefficients of the 3D Ising Criticality via Quantum Fuzzy Spheres.

Conformal field theory (CFT) plays a crucial role in the study of various critical phenomena. While much attention has been paid to the critical exponents of different universalities, which correspond to the conformal dimensions of CFT primary fields, other important and intricate data such as operator product expansion (OPE) coefficients governing the fusion of two primary fields, have remained largely unexplored, especially in dimensions higher than 2D (or equivalently, 1+1D). Motivated by the recently proposed fuzzy sphere regularization, we investigate the operator content of 3D Ising criticality from a microscopic perspective. We first outline the procedure for extracting OPE coefficients on the fuzzy sphere and then compute 13 OPE coefficients of low-lying CFT primary fields. Our results are highly accurate and in agreement with the numerical conformal bootstrap data of 3D Ising CFT. Moreover, we were able to obtain 4 OPE coefficients, including f_{T_{µν}T_{ρη}ε}, which were previously unknown, thus demonstrating the superior capabilities of our scheme. Expanding the horizon of the fuzzy sphere regularization from the state perspective to the operator perspective opens up new avenues for exploring a wealth of new physics.

Using consensus bayesian network to model the reactive oxygen species regulatory pathway.

Bayesian network is one of the most successful graph models for representing the reactive oxygen species regulatory pathway. With the increasing number of microarray measurements, it is possible to construct the bayesian network from microarray data directly. Although large numbers of bayesian network learning algorithms have been developed, when applying them to learn bayesian networks from microarray data, the accuracies are low due to that the databases they used to learn bayesian networks contain too few microarray data. In this paper, we propose a consensus bayesian network which is constructed by combining bayesian networks from relevant literatures and bayesian networks learned from microarray data. It would have a higher accuracy than the bayesian networks learned from one database. In the experiment, we validated the bayesian network combination algorithm on several classic machine learning databases and used the consensus bayesian network to model the Escherichia coli's ROS pathway.