Ping Liu

Bio: Ping Liu is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Lithium & Battery (electricity). The author has an hindex of 50, co-authored 241 publications receiving 9839 citations. Previous affiliations of Ping Liu include University of Illinois at Urbana–Champaign & General Motors.

Pathways for practical high-energy long-cycling lithium metal batteries

Abstract: State-of-the-art lithium (Li)-ion batteries are approaching their specific energy limits yet are challenged by the ever-increasing demand of today’s energy storage and power applications, especially for electric vehicles. Li metal is considered an ultimate anode material for future high-energy rechargeable batteries when combined with existing or emerging high-capacity cathode materials. However, much current research focuses on the battery materials level, and there have been very few accounts of cell design principles. Here we discuss crucial conditions needed to achieve a specific energy higher than 350 Wh kg−1, up to 500 Wh kg−1, for rechargeable Li metal batteries using high-nickel-content lithium nickel manganese cobalt oxides as cathode materials. We also provide an analysis of key factors such as cathode loading, electrolyte amount and Li foil thickness that impact the cell-level cycle life. Furthermore, we identify several important strategies to reduce electrolyte-Li reaction, protect Li surfaces and stabilize anode architectures for long-cycling high-specific-energy cells. Jun Liu and Battery500 Consortium colleagues contemplate the way forward towards high-energy and long-cycling practical batteries.

Universal quinone electrodes for long cycle life aqueous rechargeable batteries

Abstract: Aqueous rechargeable batteries are promising for grid storage and electric vehicles, but they suffer from poor cycle life due to anode instability. Exploiting stable ion-coordination charge storage and chemical inertness towards aqueous electrolytes, quinones are now reported as stable anodes.

Characterization of Large-Pore MCM-41 Molecular Sieves Obtained via Hydrothermal Restructuring

Abstract: MCM-41 periodic mesoporous silicates were synthesized using cetyltrimethylammonium bromide, and their pore sizes were tailored by postsynthesis hydrothermal treatment method of Khushalani et al., allowing us to obtain large-pore MCM-41 samples with a high degree of structural ordering. It was shown that the pore size enlargement was accompanied by a significant improvement of pore size uniformity, a gradual decrease of the specific surface area, and the pore wall thickening. After a certain upper limit of pore size (i.e., about 6.5 nm) was reached, a further hydrothermal treatment led to samples of diminished quality. The structural uniformity decreases dramatically, i.e., the pore size distribution becomes broader and a small but noticeable amount of micropores develops. Moreover, the appearance of pore blocking effects (as inferred from the irreversibility of nitrogen adsorption−desorption isotherms) indicates that the pore geometry deviates significantly from its initial cylindrical shape characteristi...

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.

Materials for electrochemical capacitors

Abstract: Electrochemical capacitors, also called supercapacitors, store energy using either ion adsorption (electrochemical double layer capacitors) or fast surface redox reactions (pseudo-capacitors). They can complement or replace batteries in electrical energy storage and harvesting applications, when high power delivery or uptake is needed. A notable improvement in performance has been achieved through recent advances in understanding charge storage mechanisms and the development of advanced nanostructured materials. The discovery that ion desolvation occurs in pores smaller than the solvated ions has led to higher capacitance for electrochemical double layer capacitors using carbon electrodes with subnanometre pores, and opened the door to designing high-energy density devices using a variety of electrolytes. Combination of pseudo-capacitive nanomaterials, including oxides, nitrides and polymers, with the latest generation of nanostructured lithium electrodes has brought the energy density of electrochemical capacitors closer to that of batteries. The use of carbon nanotubes has further advanced micro-electrochemical capacitors, enabling flexible and adaptable devices to be made. Mathematical modelling and simulation will be the key to success in designing tomorrow's high-energy and high-power devices.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.