Zhen Li

Bio: Zhen Li is an academic researcher from Wuhan University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 127, co-authored 1712 publications receiving 71351 citations. Previous affiliations of Zhen Li include Tsinghua University & Hong Kong University of Science and Technology.

Suppressing photo-oxidation of non-fullerene acceptors and their blends in organic solar cells by exploring material design and employing friendly stabilizers

Abstract: In addition to a high power conversion efficiency, ambient stability is another impact factor for the successful commercialization of organic solar cells (OSCs). Understanding the role of photovoltaic materials is the key to address this challenge, but no such studies have been systematically performed on non-fullerene acceptors (NFAs). In this work, we firstly investigate the role of NFA photo-oxidation in device degradation. Relevant investigation of physical dynamics underlines the effects on the device performance for NFA photo-oxidation acting as trap states in exposed blends. In addition, taking ITIC as an example, we shed some light on the possible mechanisms of NFA photo-oxidation, which cannot be eliminated by relevant strategies and principles of material design. These results drive us to further investigate the photobleaching rates of thirty-three NFAs, including fused-ring electron acceptors and perylene diimide acceptor derivatives. Surprisingly, most of them show a higher optical density loss as compared to their fullerene-based counterparts. In view of relevant comparative analysis in the Discussion section, we further propose some design strategies to improve the photo-oxidation stability of NFAs. More importantly, we also find a stabilizer (namely nickel chelate S6) that can effectively suppress the photo-oxidation of NFAs and their blends and thus improve the ambient stability of OSCs.

Determination of band gaps of self-assembled carbon nanotube films using Tauc/Davis–Mott model

Abstract: The band gaps of self-assembled single-walled carbon nanotube (SWNT) films have been determined through curve fitting using the semi-empirical Tauc and Davis–Mott model, based on the measurement of optical absorption at the visible and near infrared range. This study provides a practicable option for the determination of band gaps for ultra-thin SWNT films or multi-walled carbon nanotube films whose vHs peaks cannot be well resolved in absorption spectra.

Myc/miR-378/TOB2/cyclin D1 functional module regulates oncogenic transformation.

Abstract: The c-Myc transcription factor activates a cascade of downstream targets to form a complex transcriptional program that ultimately leads to cellular transformation. Although a large number of protein-encoding genes as well as non-coding RNAs were identified as Myc targets, only a few have been validated to be functionally important for c-Myc-driven transformation. Here, we identify a microRNA (miRNA), miR-378, as a novel target of the c-Myc oncoprotein that is able to cooperate with activated Ras or HER2 to promote cellular transformation. Mechanistically, miR-378 achieves this oncogenic effect, at least in part, by targeting and inhibiting the anti-proliferative BTG family member, TOB2, which is further elucidated as a candidate tumor suppressor to transcriptionally repress proto-oncogene cyclin D1. Therefore, our study identifies miR-378-TOB2-cyclin D1 as a functional module to mediate the cross talk between Myc and Ras signaling in cellular transformation.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Highly accurate protein structure prediction with AlphaFold

Abstract: Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’8—has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.