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At least 1,243 records · Page 69

Engineering an aldoxime dehydratase with high activity and isomer tolerance for biosynthesis of an O -protected primary cyanohydrin

O-protected primary cyanohydrins (glycolonitriles) are important building blocks for many difunctionalized compounds and precursors to known bioactive molecules. Their synthesis, however, utilizes toxic cyanide, which raises significant safety concerns for industrial synthesis. Here, in this study, we present a cyanide-free enzymatic synthesis of an o-benzyl protected primary cyanohydrin from an (E)- or (Z)-α-oxygen protected aldoxime using an engineered aldoxime dehydratase enzyme from Bacillus sp. OxB-1 (OxdB). In contrast to many evolved enzymes that tend to “specialize” as their activity increases, we used directed evolution to engineer OxdB for efficient dehydration of both isomers in a mixture of (E)- or (Z)-α-oxygen aldoximes with high activity and substrate loading to achieve near quantitative yield. Using this enzyme, we further demonstrate a cyanide-free chemoenzymatic pathway to an o-protected primary cyanohydrin starting from a readily available aldehyde, where the aldehyde is first condensed with hydroxylamine, followed by dehydration using our evolved enzyme. This pathway was readily scaled up to 1 g scale with high substrate loading, demonstrating its utility in industrial synthesis of these important building block functional groups.

Aldoxime dehydratase↗

5-kWe Free-piston Stirling Engine Convertor

The high reliability, long life, and efficient operation of Free-Piston Stirling Engines (FPSEs) make them an attractive power system to meet future space power requirements with less mass, better efficiency, and less total heat exchanger area than other power convertor options. FPSEs are also flexible in configuration as they can be coupled with many potential heat sources and various heat input systems, heat rejection systems, and power management and distribution systems. Development of a 5-kWe Stirling Convertor Assembly (SCA) is underway to demonstrate the viability of an FPSE for space power. The design is a scaled-down version of the successful 12.5-kWe Component Test Power Converter (CTPC) developed under NAS3-25463. The ultimate efficiency target is 25% overall convertor efficiency (electrical power out over heat in). For the single cylinder prototype now in development, cost and time constraints required use of economical and readily available materials (steel versus beryllium) and components (a commercially available linear alternator) and thus lower efficiency. The working gas is helium at 150 bar mean pressure. The design consists of a displacer suspended on internally pumped gas bearings and a power piston/alternator supported on flexures. Non-contacting clearance seals are used between internal volumes. Heat to and from the prototype convertor is done via pumped liquid loops passing through shell and tube heat exchangers. The preliminary and detail designs of the convertor, controller, and support systems (heating loop, cooling loop, and helium supply system) are complete and all hardware is on order. Assembly and test of the prototype at Foster- Miller is planned for early 2008, when work will focus on characterizing convertor dynamics and steady-state operation to determine maximum power output and system efficiency. The device will then be delivered to Auburn University where assessments will include start-up and shutdown characterization and transient response to temperature and load variations. Future activities may include testing at NASA GRC.

Chapman, Peter A.↗

Exploring the Effects of Piston Bowl Geometry and Injector Included Angle on Dual-Fuel and Single-Fuel RCCI

Abstract Reactivity control compression ignition (RCCI) is a low-temperature combustion technique that has been proposed to meet the current demand for high thermal efficiency and low engine-out emissions. However, its requirement of two separate fuel systems (i.e., a low-reactivity fuel system and a high-reactivity fuel system) has been one of its major challenges in the last decade. This leads to the single-fuel RCCI concept, where the secondary fuel (reformates of diesel) is generated from the primary fuel (diesel) through catalytic partial oxidation reformation. Following the in-depth analysis of the reformate fuel (reformates of diesel) and its benefit as the low-reactivity fuel with diesel, the effects of the start of injection (SOI) timing of diesel and the energy-based blend ratio were also studied in detail. In this study, the effects of piston profile and the injector included angles were experimentally examined using both conventional fuel pairs (gasoline—diesel and natural gas—diesel) and reformate RCCI. A validated computational fluid dynamics (CFD) model was also used for a better understanding of the experimental trends. Comparing a reentrant bowl piston with a shallow bowl piston at a constant compression ratio and SOI, the latter showed better thermal efficiency, regardless of the fuel combination, due to its 10% lower surface area for the heat transfer. Comparing the 150-degree included angle and 60-degree included angle on the shallow bowl piston, the latter showed better combustion efficiency, regardless of the fuel combination, due to its earlier combustion phasing (at constant SOI timing). The effect was particularly prominent on reformate RCCI because of its incredibly high diluent concentration, which retards the combustion further for the 150-deg injector. Later, using convergecfd, seven different injector included angles were studied at a constant SOI. With the change in injector included angle, the region of the cylinder targeted by the fuel spray varies significantly, and it was found to have a significant impact on the combustion efficiency and the engine-out emissions. As the injector included angle changed from 60-deg to 150-deg, the combustion efficiency increased by 15% and the CO, NOx, and HC emissions decreased by 96%, 70%, and 86%, respectively.

Engineering↗

Effects of Port Angle on Scavenging of an Opposed Piston Two-Stroke Engine

Opposed-piston 2-stroke (OP-2S) engines have the potential to achieve higher thermal efficiency than a typical diesel engine. However, the uniflow scavenging process is difficult to control over a wide range of speeds and loads. Scavenging performance is highly sensitive to pressure dynamics, port timings, and port design. This study proposes an analysis of the effects of port vane angle on the scavenging performance of an opposed-piston 2-stroke engine via simulation. A CFD model of a three-cylinder opposed-piston 2-stroke was developed and validated against experimental data collected by Achates Power Inc. One of the three cylinders was then isolated in a new model and simulated using cycle-averaged and cylinder-averaged initial/ boundary conditions. This isolated cylinder model was used to efficiently sweep port angles from 12 degrees to 29 degrees at different pressure ratios. Results indicate that scavenging performance is correlated with the bulk swirl ratio generated by these port angles. Scavenging performance is also sensitive to the pressure ratio across the engine. It was concluded that, for a given pressure ratio, the smallest port angle produces the best scavenging; however, previous work indicates lower bulk swirl ratio during combustion can have an impact on emissions formation.

33 ADVANCED PROPULSION SYSTEMS↗

RICH (Robotic Interface Control & Handling) System for VULCAN

High flux neutron beam and high efficiency detectors warrant quick turn arounds of neutron diffraction measurements at the engineering materials diffractometer VULCAN. Efficient change and alignment of samples and automatic measurements at VULCAN are desired for better use of neutron beam time by users. In this work, we aim to develop a proof-of-concept Robotic Interactive Control & Handling System (RICH) for sample handling at VULCAN that could assist high throughput experiments and reduce the overhead time significantly. This was realized by a six-axis desktop robot with trained AI models. The trained AI models can recognize and locate samples in various types of positions in a live video stream. In addition, we developed smart algorithms which used our models on multiple cameras to recognize where samples are with respect to multiple point of views, and then using user-inputted parameters, align them to perform complex measurements.

42 ENGINEERING↗

Performance Analysis Tool for HPC and Big Data Applications on Scientific Clusters

Big data is prevalent in HPC computing. Many HPC projects rely on complex workflows to analyze terabytes or petabytes of data. These workflows often require running over thousands of CPU cores and performing simultaneous data accesses, data movements, and computation. It is challenging to analyze the performance involving terabytes or petabytes of workflow data or measurement data of the executions, from complex workflows over a large number of nodes and multiple parallel task executions. To help identify performance bottlenecks or debug the performance issues in large-scale scientific applications and scientific clusters, we have developed a performance analysis framework, using state-ofthe- art open-source big data processing tools. Our tool can ingest system logs and application performance measurements to extract key performance features, and apply the most sophisticated statistical tools and data mining methods on the performance data. It utilizes an efficient data processing engine to allow users to interactively analyze a large amount of different types of logs and measurements. To illustrate the functionality of the big data analysis framework, we conduct case studies on the workflows from an astronomy project known as the Palomar Transient Factory (PTF) and the job logs from the genome analysis scientific cluster. Our study processed many terabytes of system logs and application performance measurements collected on the HPC systems at NERSC. The implementation of our tool is generic enough to be used for analyzing the performance of other HPC systems and Big Data workows.

97 MATHEMATICS AND COMPUTING↗

Hopes Beyond PET Recycling: Environmentally Clean and Engineeringly Applicable

Disposal of plastics in the environment has become a core of anxiety in developing countries, while in the developed countries the focus has additionally been placed on design and manufacture of emerging products from plastic wastes—a somewhat vague yet promising horizon. Central to environmental concerns are poly(ethylene terephthalate) (PET) wastes, mainly from post-consumer bottles. Because of a considerable drop in molecular weight in the course of recycling, recycled PETs are not suitable for engineering uses. An efficient yet reasonably green synthesis route is employed here to convert PET wastes into polyurethane, and then carbon nanotubes (CNTs) was added at different levels to obtain nanocomposites with high mechanical properties. The effects of isocyanate (NCO)/hydroxyl (OH) molar ratio and CNTs content on the morphology, physical and mechanical properties were discussed. Chemical/physical crosslink density was calculated from initial slope of stress–strain curves, Mooney–Rivlin plots, strain-hardening modulus, rubbery-plateau storage modulus and swelling data. High tensile strength (300 MPa) and breaking elongation (160%) of polyurethane/CNTs nanocomposites born from PET wastes seemed promising. Microscopic analyses by AFM, SEM, and TEM gave useful information about distribution of CNTs in polyurethane. Lastly, structural changes were correlated to mechanical properties improvement.

36 MATERIALS SCIENCE↗

A non-cooperative meta-modeling game for automated third-party calibrating, validating and falsifying constitutive laws with parallelized adversarial attacks

The evaluation of constitutive models, especially for high-risk and high-regret engineering applications, requires efficient and rigorous third-party calibration, validation and falsification. While there are numerous efforts to develop paradigms and standard procedures to validate models, difficulties may arise due to the sequential, manual, and often biased nature of the commonly adopted calibration and validation processes, thus slowing down data collections, hampering the progress towards discovering new physics, increasing expenses and possibly leading to misinterpretations of the credibility and application ranges of proposed models. This work attempts to introduce concepts from game theory and machine learning techniques to overcome many of these existing difficulties. Here, we introduce an automated meta-modeling game where two competing AI agents systematically generate experimental data to calibrate a given constitutive model and to explore its weakness such that the experiment design and model robustness can be improved through competitions. The two agents automatically search for the Nash equilibrium of the meta-modeling game in an adversarial reinforcement learning framework without human intervention. In particular, a protagonist agent seeks to find the more effective ways to generate data for model calibrations, while an adversary agent tries to find the most devastating test scenarios that expose the weaknesses of the constitutive model calibrated by the protagonist. By capturing all possible design options of the laboratory experiments into a single decision tree, we recast the design of experiments as a game of combinatorial moves that can be resolved through deep reinforcement learning by the two competing players. Our adversarial framework emulates idealized scientific collaborations and competitions among researchers to achieve a better understanding of the application range of the learned material laws and prevent misinterpretations caused by conventional AI-based third-party validation. Numerical examples are given to demonstrate the wide applicability of the proposed meta-modeling game with adversarial attacks on both human-crafted constitutive models and machine learning models.

97 MATHEMATICS AND COMPUTING↗

A wide range experimental and kinetic modeling study of the oxidation of 2,3-dimethyl-2-butene: Part 1

2,3-Dimethyl-2-butene (TME) is a potential fuel additive with high research octane number (RON) and octane sensitivity (S), which can improve internal combustion engine performance and efficiency. However, the combustion characteristics of TME have not been comprehensively investigated. Thus, it is essential to study the combustion characteristics of TME and construct a detailed chemical kinetic model to describe its combustion. In this paper, two high-pressure shock tubes and a constant-volume reactor are used to measure ignition delay times and laminar flame speeds of TME oxidation. Further, the ignition delay times were measured at equivalence ratios of 0.5, 1.0, and 2.0 in “air”, at pressures of 5 and 10 bar, in the temperature range of 950 – 1500 K. Flame speeds of the TME/ “air” mixtures were measured at atmospheric pressure, at a temperature of 325 K, for equivalence ratios ranging from 0.78 to 1.31. Two detailed kinetic mechanisms were constructed independently using different methodologies; the KAUST TME mechanism was constructed based on NUIGMech1.1, and the MIT TME mechanism was built using the Reaction Mechanism Generator (RMG). Both mechanisms were used to simulate the experimental results using Chemkin Pro. In the present work, reaction flux and sensitivity analyses were performed using the KAUST mechanism to determine the critical reactions controlling TME oxidation at the conditions studied.

33 ADVANCED PROPULSION SYSTEMS↗

Hierarchical microstructural parameters governing electrical and thermal conductivity in Al-based eutectic systems

Enhancing conductivity properties of aluminum alloys can drive superior performance and efficiency across diverse engineering applications, yet currently commercially available Al casting alloys are severely limited in electrical and thermal conductivity. There is a general understanding that the presence of any defects and alloying elements within Al will inevitably lower conductivity properties. However, there is a lack of comparative studies evaluating the intricate interplay between the microstructural parameters and conductivity properties across cast Al-based eutectic systems. Therefore, this study bridges this gap by conducting a comparative study across hypoeutectic alloy compositions consisting of 50 % primary Al and 50 % eutectic region to elucidate the key parameters that dictate conductivity properties. This research contributes to advancing the understanding of alloy design opportunities for high conductivity, high strength castable Al alloys.

36 MATERIALS SCIENCE↗

Machine-learning predictions of the shale wells’ performance

The ultra-low permeability nature of shale reservoirs leads to an extended linear flow and necessitates horizontal wells with multi-stage engineered fractures to efficiently extract hydrocarbons resources. These artificially-generated and naturally-occurring fractures form complex networks that create complex flow regimes which control oil production. These fractures are neither identical nor equally-spaced, which leads to a production profile with a masked onset of the boundary-dominated flow. The combination of the extended linear flow with the indeterminate onset of the boundary-dominated flow challenges the current deterministic analytic approaches to forecast the estimated ultimate recovery (EUR). In this work, we propose a novel machine-learning approach which overcomes these challenges and provides reliable EUR estimates based on field-wide analyses. We implement a novel unsupervised machine learning (ML) methodology, which allows for automatic identification of the optimal number of features (signals) present in the data based on non-negative matrix/tensor factorization coupled with k-means clustering incorporating regularization and physics constraints. In the presented analyses, the input data to the ML algorithm is the available (public) production history from the field collected at existing unconventional reservoirs. We validate our approach through hindcasting of the production data, where we achieved an excellent agreement. In addition, our approach is able to identify the poorly-performing wells, which could benefit from early refracing. Our approach provides fast and accurate estimations of the well performance without presumptions about the state of the well or the flow regime.

03 NATURAL GAS↗

Iterative genome editing of Escherichia coli for 3-hydroxypropionic acid production

Synthetic biology requires strategies for the targeted, efficient, and combinatorial engineering of biological sub-systems at the molecular level. Here, we report the use of the iterative CRISPR EnAbled Trackable genome Engineering (iCREATE) method for the rapid construction of combinatorially modified genomes. We coupled this genome engineering strategy with high-throughput phenotypic screening and selections to recursively engineer multiple traits in Escherichia coli for improved production of the platform chemical 3- hydroxypropionic acid (3HP). Particularly, we engineered i) central carbon metabolism, ii) 3HP synthesis, and (iii) 3HP tolerance through design, construction and testing of ~162,000 mutations across 115 genes spanning global regulators, transcription factors, and enzymes involved in 3HP synthesis and tolerance. The iCREATE process required ~1 month to perform 13 rounds of combinatorial genome modifications with targeted gene knockouts, expression modification by ribosomal binding site (RBS) engineering, and genome-level site-saturation mutagenesis. Specific mutants conferring increased 3HP titer, yield, and productivity were identified and then combined to produce 3HP at a yield and concentration ~60-fold higher than the wild-type strain.

3-hydroxypropionic acid↗

Initiation of Flash Boiling of Multicomponent Miscible Mixtures with Application to Transportation Fuels and Their Surrogates

This paper presents methodologies to predict thermodynamic conditions that initiate flash boiling by spontaneous nucleation of liquids consisting of hundreds of miscible liquids and their lower order surrogate mixtures. The methods are illustrated with a kerosene-based fuel and a seven-component surrogate for it. The predictions are compared to measurements of nucleation temperatures obtained from a pulse-heating technique that rapidly heats a microscale platinum film immersed in a pool of the test fluid. Nucleation temperatures are predicted using a generalized corresponding states principle (GCSP), and a modification of classical nucleation theory that considers the mixture as a pseudo single component fluid (PSCF). The intent is to offer a simple means to predict the initiation of flash boiling that can have important consequences for fuel efficiency in combustion engines. We show that contact angle has a strong effect such that predicted and measured spontaneous nucleation temperatures agree for a given heating rate only if contact angle is accounted for in the theory. For low heating rates (less than 2 × 10 7 K/s), predicted nucleation temperatures, assuming a spherical bubble, are 13% higher than measurements. The gap is closed to about 6% as the heating rate reaches 3 × 10 8 K/s (the highest that could be reached in the experiments) and to less than 1% when the measured nucleation temperature is extrapolated to an asymptotic (zero contact angle) limit. Here, the PSCF method (using mixture properties as mole fraction averages of mixture component properties evaluated at the same reduced temperatures as the mixtures) and the GSCP predict virtually identical nucleation temperatures, while the GCSP method does not require any mixture property values.

42 ENGINEERING↗

Understanding Trends in Autoignition of Biofuels: Homologous Series of Oxygenated C5 Molecules

Oxygenated biofuels provide a renewable, domestic source of energy that can enable adoption of advanced, high-efficiency internal combustion engines, such as those based on homogeneously charged compression ignition (HCCI). Of key importance to such engines is the cetane number (CN) of the fuel, which is determined by the autoignition of the fuel under compression at relatively low temperatures (550-800 K). For the plethora of oxygenated biofuels possible, it is desirable to know the ignition delay times and the CN of these fuels to help guide conversion strategies so as to focus efforts on the most desirable fuels. For alkanes, the chemical pathways leading to radical chain-branching reactions giving rise to low-temperature autoignition are well-known and are highly coincident with the buildup of reactive radicals such as OH. Key in the mechanisms leading to chain branching are the addition of molecular oxygen to alkyl radicals and the rearrangement and dissociation of the resulting peroxy radials. Prediction of the temperature and pressure dependence of reactions that lead to the buildup of reactive radicals requires a detailed understanding of the potential energy surfaces (PESs) of these reactions. In this study, we used quantum mechanical modeling to systematically compare the effects of oxygen functionalities on these PESs and associated kinetics so as to understand how they affect experimental trends in autoignition and CN. The molecules studied here include pentane, pentanol, pentanal, 2-heptanone, methylpentyl ether, methyl hexanoate, and pentyl acetate. All have a saturated five-carbon alkyl chain with an oxygen functional group attached to the terminal carbon atom. The results of our systematic comparison may be summarized as follows: (1) Oxygen functionalities activate C-H bonds by lowering the bond dissociation energy (BDE) relative to alkanes. (2) The R-OO bonds in peroxy radicals adjacent to carbonyl groups are weaker than corresponding alkyl systems, leading to dissociation of ROO radicals and reducing reactivity and hence CN. (3) Hydrogen atom transfer in peroxy radicals is important in autoignition, and low barriers for ethers and aldehydes lead to high CN. (4) Peroxy radicals formed from alcohols have low barriers to form aldehydes, which reduce the reactivity of the alkyl radical. In conclusion, these findings for the formation and reaction of alkyl radicals with molecular oxygen explain the trend in CN for these common biofuel functional groups.

09 BIOMASS FUELS↗

Dynamics of Charged Species in Ionic-Neutral Block Copolymer and Surfactant Complexes [Structural Relaxation and Dynamics of Ionic-Neutral Block Copolymer Surfactant Complexes]

Structure–property relationships of ionic block copolymer (BCP) surfactant complexes are critical toward the progress of favorable engineering design of efficient charge-transport materials. In this paper, molecular dynamics simulations are used to understand the dynamics of charged-neutral BCP and surfactant complexes. The dynamics are examined for two different systems: charged-neutral double-hydrophilic and hydrophobic–hydrophilic block copolymers with oppositely charged surfactant moieties. The dynamics of the surfactant head, tails, and charges are studied for five different BCP volume fractions. We observe that the dynamics of the different species solely depend on the balance between electrostatic and entropic interactions between the charged species and the neutral monomers. The favorable hydrophobic–hydrophobic interactions and the unfavorable hydrophobic–hydrophilic interactions determine the mobilities of the monomers. The dynamical properties of the charge species influence complex formation. Structural relaxations exhibit length-scale dependent behavior, with slower relaxation at the radius of gyration length-scale and faster relaxation at the segmental length-scale, consistent with previous results. The dynamical analysis correlates ion-exchange kinetics to the self-assembly behavior of the complexes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

High-Throughput Characterization and Optimization of Polyamide Hydrolase Activity Using Open Port Sampling Interface Mass Spectrometry

Enzymatic biodegradation of polymers, such as polyamides (PA), has the potential to cost-effectively reduce plastic waste, but enhancements in degradation efficiency are needed. Engineering enzymes through directed evolution is one pathway toward identification of critical domains needed for improving activity. However, screening such enzymatic libraries (100s-to-1000s of samples) is time-consuming. Here we demonstrate the use of robotic autosampler (PAL) and immediate drop on demand technology (I.DOT) liquid handling systems coupled with open-port sampling interface-mass spectrometry (OPSI-MS) to screen for PA6 and PA66 hydrolysis by 6-aminohexanoate-oligomer endo-hydrolase (nylon hydrolase, NylC) in a high-throughput (8–20 s/sample) manner. The OPSI-MS technique required minimal sample preparation and was amenable to 96-well plate formats for automated processing. Enzymatic hydrolysis of PA characteristically produced soluble linear oligomer products that could be identified by OPSI-MS. Incubation temperatures and times were optimized for PA6 (65 °C, 24 h) and PA66 (75 °C, 24 h) over 108 experiments. In addition, the I.DOT/OPSI-MS quantified production of PA6 linear dimer (8.3 ± 1.6 μg/mL) and PA66 linear monomer (13.5 ± 1.5 μg/mL) by NylC with a lower limit of detection of 0.029 and 0.032 μg/mL, respectively. For PA6 and PA66, linear oligomer production corresponded to 0.096 ± 0.018% and 0.204 ± 0.028% conversion of dry pellet mass, respectively. The developed methodology is expected to be utilized to assess enzymatic hydrolysis of engineered enzyme libraries, comprising hundreds to thousands of individual samples.

47 OTHER INSTRUMENTATION↗

Greenhouse gas consequences of the China dual credit policy

For over ten years, China has been the largest vehicle market in the world. In order to address energy security and air quality concerns, China issued the Dual Credit policy to improve vehicle efficiency and accelerate New Energy Vehicle adoption. In this paper, a market-penetration model is combined with a vehicle fleet model to assess implications on greenhouse gas (GHG) emissions and energy demand. Here we use this integrated modeling framework to study several scenarios, including hypothetical policy tweaks, oil price, battery cost and charging infrastructure for the Chinese passenger vehicle fleet. The model shows that the total GHGs of the Chinese passenger vehicle fleet are expected to peak in 2032 under the Dual Credit policy. A significant reduction in GHG emissions is possible if more efficient internal combustion engines continue to be part of the technology mix in the short term with more New Energy Vehicle penetration in the long term.

54 ENVIRONMENTAL SCIENCES↗