Yi Cui

Bio: Yi Cui is an academic researcher from Stanford University. The author has contributed to research in topics: Anode & Lithium. The author has an hindex of 220, co-authored 1015 publications receiving 199725 citations. Previous affiliations of Yi Cui include KAIST & University of California, Berkeley.

Tuning of Plasmons in Transparent Conductive Oxides by Carrier Accumulation

Abstract: A metal naturally displays dramatic changes in its optical properties near the plasma frequency where the permittivity changes from a negative to a positive value, and the material turns from highly reflective to transparent For many applications, it is desirable to achieve such large optical changes by electrical gating However, this is challenging given the high carrier density of most metals, which causes them to effectively screen externally applied electrical fields Indium tin oxide (ITO) is a low-electron-density metal that does afford electric tuning of its permittivity in the infrared spectral range Here, we experimentally show the tunability of the plasma frequency of an ITO thin film by changing its sheet carrier density via gating with an ionic liquid By applying moderate gate bias values up to 14 V, the electron density increases in a thin (∼3 nm) accumulation layer at the surface of the 15-nm-thick ITO film This results in notable blue shifts in the plasma frequency These optical and

Three-Dimensional Analysis of Particle Distribution on Filter Layers inside N95 Respirators by Deep Learning.

Abstract: The global COVID-19 pandemic has changed many aspects of daily lives. Wearing personal protective equipment, especially respirators (face masks), has become common for both the public and medical professionals, proving to be effective in preventing spread of the virus. Nevertheless, a detailed understanding of respirator filtration-layer internal structures and their physical configurations is lacking. Here, we report three-dimensional (3D) internal analysis of N95 filtration layers via X-ray tomography. Using deep learning methods, we uncover how the distribution and diameters of fibers within these layers directly affect contaminant particle filtration. The average porosity of the filter layers is found to be 89.1%. Contaminants are more efficiently captured by denser fiber regions, with fibers <1.8 μm in diameter being particularly effective, presumably because of the stronger electric field gradient on smaller diameter fibers. This study provides critical information for further development of N95-type respirators that combine high efficiency with good breathability.

Ultralight and fire-extinguishing current collectors for high-energy and high-safety lithium-ion batteries

Abstract: Inactive components and safety hazards are two critical challenges in realizing high-energy lithium-ion batteries Metal foil current collectors with high density are typically an integrated part of lithium-ion batteries yet deliver no capacity Meanwhile, high-energy batteries can entail increased fire safety issues Here we report a composite current collector design that simultaneously minimizes the ‘dead weight’ within the cell and improves fire safety An ultralight polyimide-based current collector (9 μm thick, specific mass 154 mg cm−2) is prepared by sandwiching a polyimide embedded with triphenyl phosphate flame retardant between two superthin Cu layers (~500 nm) Compared to lithium-ion batteries assembled with the thinnest commercial metal foil current collectors (~6 µm), batteries equipped with our composite current collectors can realize a 16–26% improvement in specific energy and rapidly self-extinguish fires under extreme conditions such as short circuits and thermal runaway Batteries need to be energy-dense as well as safe Yi Cui and team develop an ultralight polyimide-based current collector with embedded fire retardants that enables lithium-ion batteries with much-enhanced safety and energy density

Incorporating prior biological knowledge for network-based differential gene expression analysis using differentially weighted graphical LASSO.

Abstract: Conventional differential gene expression analysis by methods such as student’s t-test, SAM, and Empirical Bayes often searches for statistically significant genes without considering the interactions among them. Network-based approaches provide a natural way to study these interactions and to investigate the rewiring interactions in disease versus control groups. In this paper, we apply weighted graphical LASSO (wgLASSO) algorithm to integrate a data-driven network model with prior biological knowledge (i.e., protein-protein interactions) for biological network inference. We propose a novel differentially weighted graphical LASSO (dwgLASSO) algorithm that builds group-specific networks and perform network-based differential gene expression analysis to select biomarker candidates by considering their topological differences between the groups. Through simulation, we showed that wgLASSO can achieve better performance in building biologically relevant networks than purely data-driven models (e.g., neighbor selection, graphical LASSO), even when only a moderate level of information is available as prior biological knowledge. We evaluated the performance of dwgLASSO for survival time prediction using two microarray breast cancer datasets previously reported by Bild et al. and van de Vijver et al. Compared with the top 10 significant genes selected by conventional differential gene expression analysis method, the top 10 significant genes selected by dwgLASSO in the dataset from Bild et al. led to a significantly improved survival time prediction in the independent dataset from van de Vijver et al. Among the 10 genes selected by dwgLASSO, UBE2S, SALL2, XBP1 and KIAA0922 have been confirmed by literature survey to be highly relevant in breast cancer biomarker discovery study. Additionally, we tested dwgLASSO on TCGA RNA-seq data acquired from patients with hepatocellular carcinoma (HCC) on tumors samples and their corresponding non-tumorous liver tissues. Improved sensitivity, specificity and area under curve (AUC) were observed when comparing dwgLASSO with conventional differential gene expression analysis method. The proposed network-based differential gene expression analysis algorithm dwgLASSO can achieve better performance than conventional differential gene expression analysis methods by integrating information at both gene expression and network topology levels. The incorporation of prior biological knowledge can lead to the identification of biologically meaningful genes in cancer biomarker studies.

Nanoscale photon management in silicon solar cells

Abstract: Light absorption in a photovoltaic device becomes critical as the thickness of an absorber layer is decreased to reduce cost. To enhance light absorption, photon management at the nanoscale has been studied because conventional methods, which are based on micrometer-sized structure, do not work well for thinner solar cells. This article reviews recent progress in photon management on the nanoscale for increasing light absorption in Si solar cells. The methodology for the absorption enhancement will be discussed, followed by advances in nanofabrication techniques that make the methodology a scalable and viable solution. The authors conclude with a discussion of the challenge of photon management schemes and future directions for light trapping in ultra-thin Si solar cells.

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.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。