skip to main content


The NSF Public Access Repository (NSF-PAR) system and access will be unavailable from 11:00PM ET on Friday, December 15 until 2:00 AM ET on Saturday, December 16 due to maintenance. We apologize for the inconvenience.

Search for: All records

Creators/Authors contains: "Gao, Xinfeng"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Abstract Data assimilation (DA) aims to combine observations/data with a model to maximize the utility of information for obtaining the optimal estimate. The maximum likelihood ensemble filter (MLEF) is a sequential DA method or a filter-type method. Weaknesses of the filter method are assimilating time-integrated observations and estimating empirical parameter estimation. The reason is that the forward model is employed outside of the analysis procedure in this type of DA method. To overcome these weaknesses, the MLEF is now extended as a smoother and the novel maximum likelihood ensemble smoother (MLES) is proposed. The MLES is a smoothing method with variational-like qualities, specifically in the cost function. Rather than using the error information from a single temporal location to solve for the optimal analysis update as done by the MLEF, the MLES can include observations and the forward model within a chosen time window. The newly proposed DA method is first validated by a series of rigorous and thorough performance tests using the Lorenz 96 model. Then, as DA is known to be used extensively to increase the predictability of the commonly chaotic dynamical systems seen in meteorological applications, this study demonstrates the MLES with a model chaotic problem governed by the 1D Kuramoto–Sivashinky (KS) equation. Additionally, the MLES is shown to be an effective method in improving the estimate of uncertain empirical model parameters. The MLES and MLEF are then directly compared and it is shown that the performance of the MLES is adequate and that it is a good candidate for increasing the predictability of a chaotic dynamical system. Future work will focus on an extensive application of the MLES to highly turbulent flows. 
    more » « less
  2. The syntheses of (DIM)Ni(NO 3 ) 2 and (DIM)Ni(NO 2 ) 2 , where DIM is a 1,4-diazadiene bidentate donor, are reported to enable testing of bis boryl reduced N-heterocycles for their ability to carry out stepwise deoxygenation of coordinated nitrate and nitrite, forming O(Bpin) 2 . Single deoxygenation of (DIM)Ni(NO 2 ) 2 yields the tetrahedral complex (DIM)Ni(NO)(ONO), with a linear nitrosyl and κ 1 -ONO. Further deoxygenation of (DIM)Ni(NO)(ONO) results in the formation of dimeric [(DIM)Ni(NO)] 2 , where the dimer is linked through a Ni–Ni bond. The lost reduced nitrogen byproduct is shown to be N 2 O, indicating N–N bond formation in the course of the reaction. Isotopic labelling studies establish that the N–N bond of N 2 O is formed in a bimetallic Ni 2 intermediate and that the two nitrogen atoms of (DIM)Ni(NO)(ONO) become symmetry equivalent prior to N–N bond formation. The [(DIM)Ni(NO)] 2 dimer is susceptible to oxidation by AgX (X = NO 3 − , NO 2 − , and OTf − ) as well as nitric oxide, the latter of which undergoes nitric oxide disproportionation to yield N 2 O and (DIM)Ni(NO)(ONO). We show that the first step in the deoxygenation of (DIM)Ni(NO)(ONO) to liberate N 2 O is outer sphere electron transfer, providing insight into the organic reductants employed for deoxygenation. Lastly, we show that at elevated temperatures, deoxygenation is accompanied by loss of DIM to form either pyrazine or bipyridine bridged polymers, with retention of a BpinO − bridging ligand. 
    more » « less
  3. null (Ed.)
  4. null (Ed.)
  5. null (Ed.)
  6. null (Ed.)