Biological detection and tagging using tailorable, reactive, highly fluorescent chemosensors.

This program was focused on the development of a fluorogenic chemosensor family that could tuned for reaction with electrophilic (e.g. chemical species, toxins) and nucleophilic (e.g. proteins and other biological molecules) species. Our chemosensor approach utilized the fluorescent properties of well-known berberine-type alkaloids. In situ chemosensor reaction with a target species transformed two out-of-plane, weakly conjugated, short-wavelength chromophores into one rigid, planar, conjugated, chromophore with strong long wavelength fluorescence (530-560 nm,) and large Stokes shift (100-180 nm). The chemosensor was activated with an isourea group which allowed for reaction with carboxylic acid moieties found in amino acids

An investigation of signal performance enhancements achieved through innovative pixel design across several generations of indirect detection, active matrix, flatâ panel arrays

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134990/1/mp9602.pd

Pennsylvania Folklife Vol. 32, No. 4

• Frakturs • Apple Head Dolls are Unique • Tableware and Dutch Folklore • The Pipemaker • Wheat Weaving • Beekeeping: Past and Present • The Pennsylvania Longrifle • Festival Focus • Folk Festival Programs • Quilts • The Country Butcher • Stained Glass • Metal Casting in Sand • Is This Pure Leather? • The Horse and Carriage • Marquetry, Parquetry and Intarsia • Pennsylvania Dutch Cookinghttps://digitalcommons.ursinus.edu/pafolklifemag/1100/thumbnail.jp

Development of the RIOT Web Service and Information Technologies to enable mechanism reduction for HCCI simulations.

Abstract. New approaches are being explored to facilitate multidisciplinary collaborative research of Homogenous Charge Compression Ignition (HCCI) combustion processes. In this paper, collaborative sharing of the Range Identification and Optimization Toolkit (RIOT) and related data and models is discussed. RIOT is a developmental approach to reduce the computational of detailed chemical kinetic mechanisms, enabling their use in modeling kinetically controlled combustion applications such as HCCI. These approaches are being developed and piloted as a part of the Collaboratory for Multiscale Chemical Sciences (CMCS) project. The capabilities of the RIOT code are shared through a portlet in the CMCS portal that allows easy specification and processing of RIOT inputs, remote execution of RIOT, tracking of data pedigree, and translation of RIOT outputs to a table view and to a commonly-used mechanism format. Introduction The urgent need for high-efficiency, low-emission energy utilization technologies for transportation, power generation, and manufacturing processes presents difficult challenges to the combustion research community. The needed predictive understanding requires systematic knowledge across the full range of physical scales involved in combustion processes -from the properties and interactions of individual molecules to the dynamics and products of turbulent multi-phase reacting flows. Innovative experimental techniques and computational approaches are revolutionizing the rate at which chemical science research can produce the new information necessary to advance our combustion knowledge. But the increased volume and complexity of this information often makes it even more difficult to derive the systems-level knowledge we need. Combustion researchers have responded by forming interdisciplinary communities intent on sharing information and coordinating research priorities. Such efforts face many barriers, however, including lack of data accessibility and interoperability, missing metadata and pedigree information, efficient approaches for sharing data and analysis tools, and the challenges of working together across geography, disciplines, and a very diverse spectrum of applications and funding. This challenge is especially difficult for those developing, sharing and/or using detailed chemical models of combustion to treat the oxidation of practical fuels. This is a very complex problem, and the development of new chemistry models requires a series of steps that involve acquiring and keeping track of a large amount of data and its pedigree. Also, this data is developed using a diverse range of codes and experiments spanning ab initio chemistry codes, laboratory kinetics and flame experiments, all the way to reacting flow simulations on massively parallel computers. Each of these processes typically requires different data formats, and often the data and/or analysis codes are only accessible by personally contacting the creator. Chemical models are usually shared in a legacy file format, such as Chemki

Active pixel imagers incorporating pixelâ level amplifiers based on polycrystallineâ silicon thinâ film transistors

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135114/1/mp6364.pd

Computationally efficient Bayesian inference for inverse problems.

Bayesian statistics provides a foundation for inference from noisy and incomplete data, a natural mechanism for regularization in the form of prior information, and a quantitative assessment of uncertainty in the inferred results. Inverse problems - representing indirect estimation of model parameters, inputs, or structural components - can be fruitfully cast in this framework. Complex and computationally intensive forward models arising in physical applications, however, can render a Bayesian approach prohibitive. This difficulty is compounded by high-dimensional model spaces, as when the unknown is a spatiotemporal field. We present new algorithmic developments for Bayesian inference in this context, showing strong connections with the forward propagation of uncertainty. In particular, we introduce a stochastic spectral formulation that dramatically accelerates the Bayesian solution of inverse problems via rapid evaluation of a surrogate posterior. We also explore dimensionality reduction for the inference of spatiotemporal fields, using truncated spectral representations of Gaussian process priors. These new approaches are demonstrated on scalar transport problems arising in contaminant source inversion and in the inference of inhomogeneous material or transport properties. We also present a Bayesian framework for parameter estimation in stochastic models, where intrinsic stochasticity may be intermingled with observational noise. Evaluation of a likelihood function may not be analytically tractable in these cases, and thus several alternative Markov chain Monte Carlo (MCMC) schemes, operating on the product space of the observations and the parameters, are introduced

Ultra-cold molecule production.

The production of Ultra-cold molecules is a goal of many laboratories through out the world. Here we are pursuing a unique technique that utilizes the kinematics of atomic and molecular collisions to achieve the goal of producing substantial numbers of sub Kelvin molecules confined in a trap. Here a trap is defined as an apparatus that spatially localizes, in a known location in the laboratory, a sample of molecules whose temperature is below one degree absolute Kelvin. Further, the storage time for the molecules must be sufficient to measure and possibly further cool the molecules. We utilize a technique unique to Sandia to form cold molecules from near mass degenerate collisions between atoms and molecules. This report describes the progress we have made using this novel technique and the further progress towards trapping molecules we have cooled