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A process for the production of malic acid in commercially significant quantities from the carbon compounds by genetically modified bacterial strains (GMBS; also referred to as biocatalysts or genetically modified microorganisms) is disclosed. Microorganisms suitable for the production of malic acid can be cultured in one or two-step processes as disclosed herein.
Research on renewable biofuels produced by microorganisms has enjoyed considerable advances in academic and industrial settings. As the renewable ethanol market approaches maturity, the demand is rising for the commercialization of more energy-dense fuel targets. Many strategies implemented in recent years have considerably increased the diversity and number of fuel targets that can be produced by microorganisms. Moreover, strain optimization for some of these fuel targets has ultimately led to their production at industrial scale. In this review, we discuss recent metabolic engineering approaches for augmenting biofuel production derived from alcohols, isoprenoids, and fatty acids in several microorganisms. In addition, we discuss successful commercialization ventures for each class of biofuel targets.
Instrumented indentation has been developed and widely utilized as one of the most versatile and practical means of extracting mechanical properties of materials. This method is particularly desirable for those applications where it is difficult to experimentally determine the mechanical properties using stress–strain data obtained from coupon specimens. Such applications include material processing and manufacturing of small and large engineering components and structures involving the following: three dimensional (3D) printing, thin-film and multilayered structures, and integrated manufacturing of materials for coupled mechanical and functional properties. Here, we utilize the latest developments in neural networks, including a multifidelity approach whereby deep-learning algorithms are trained to extract elastoplastic properties of metals an d alloys from instrumented indentation results using multiple datasets for desired levels of improved accuracy. We have established algorithms for solving inverse problems by recourse to single, dual, and multiple indentation and demonstrate that these algorithms significantly outperform traditional brute force computations and function-fitting methods. Moreover, we present several multifidelity approaches specifically for solving the inverse indentation problem which 1) significantly reduce the number of high-fidelity datasets required to achieve a given level of accuracy, 2) utilize known physical and scaling laws to improve training efficiency and accuracy, and 3) integrate simulation and experimental data for training disparate datasets to learn and minimize systematic errors. The predictive capabilities and advantages of these multifidelity methods have been assessed by direct comparisons with experimental results for indentation for different commercial alloys, including two wrought aluminum alloys and several 3D printed titanium alloys.
Instrumented indentation has been developed and widely utilized as one of the most versatile and practical means of extracting mechanical properties of materials. This method is particularly desirable for those applications where it is difficult to experimentally determine the mechanical properties using stress–strain data obtained from coupon specimens. Such applications include material processing and manufacturing of small and large engineering components and structures involving the following: three-dimensional (3D) printing, thin-film and multilayered structures, and integrated manufacturing of materials for coupled mechanical and functional properties. Here, we utilize the latest developments in neural networks, including a multifidelity approach whereby deep-learning algorithms are trained to extract elastoplastic properties of metals and alloys from instrumented indentation results using multiple datasets for desired levels of improved accuracy. We have established algorithms for solving inverse problems by recourse to single, dual, and multiple indentation and demonstrate that these algorithms significantly outperform traditional brute force computations and function-fitting methods. Moreover, we present several multifidelity approaches specifically for solving the inverse indentation problem which 1) significantly reduce the number of high-fidelity datasets required to achieve a given level of accuracy, 2) utilize known physical and scaling laws to improve training efficiency and accuracy, and 3) integrate simulation and experimental data for training disparate datasets to learn and minimize systematic errors. The predictive capabilities and advantages of these multifidelity methods have been assessed by direct comparisons with experimental results for indentation for different commercial alloys, including two wrought aluminum alloys and several 3D printed titanium alloys.
Altermagnetism refers to a wide class of magnetic orders featuring magnetic sublattices with opposite spins related by rotational symmetries, resulting in nontrivial spin splitting and magnetic multipoles. However, the direct observation of the altermagnetic transition remains elusive. Here, by combining theoretical analysis, electrical transport, X-ray, and optical spectroscopies, we establish a phase diagram in hybrid molecular beam epitaxy-grown RuO 2 /TiO 2 (110) films, mapping symmetries along with altermagnetic/electronic/structural phase transitions as functions of film thickness and temperature. This features an altermagnetic metallic polar phase in epitaxially strained 2 nm films, suggesting a potential link between polar metals and altermagnetic materials. Such a clear signature of a magnetic phase transition at ~500 K is observed exclusively in ultrathin strained films, unlike in bulk RuO 2 single crystals. These results highlight the power of epitaxial heterostructure engineering to induce altermagnetism in systems initially nonmagnetic, opening avenues for realizing emergent quantum phases with multifunctional properties.
During the lifetime of a structure, concrete and mortar may be exposed to highly dynamic loadings, such as impact or explosion. The dynamic fracture at high loading rates needs to be well understood to allow an accurate modeling of this kind of event. In this work, a pulsed-power generator has been employed to conduct spalling tests on mortar samples at strain-rates ranging from 2 × 10{sup 4} to 4 × 10{sup 4} s{sup −1}. The ramp loading allowed identifying the strain-rate anytime during the test. A power law has been proposed to fit properly the rate-sensitivity of tensile strength of this cementitious material over a wide range of strain-rate. Moreover, a specimen has been recovered damaged but unbroken. Micro-computed tomography has been employed to study the characteristics of the damage pattern provoked by the dynamic tensile loading.
Ceramic thermal and environmental barrier coatings will be more aggressively designed to protect gas turbine engine hot section SiC/SiC Ceramic Matrix Composite (CMC) components in order to meet future engine higher fuel efficiency and lower emission goals. A coating system consisting of a zirconia-based oxide topcoat (thermal barrier) and a mullite/BSAS silicate inner coat (environmental barrier) is often considered a model system for the CMC applications. However, the coating sintering, and thermal expansion mismatch between the zirconia oxide layer and the silicate environmental barrier/CMC substrate will be of major concern at high temperature and under thermal cycling conditions. In this study, the sintering behavior of plasma-sprayed freestanding zirconia-yttria-based thermal barrier coatings and mullite (and/or barium-strontium-aluminosilicate, i.e., BSAS) environmental barrier coatings was determined using a dilatometer in the temperature range of 1200-1500 C. The effects of test temperature on the coating sintering kinetics were systematically investigated. The plasma-sprayed zirconia-8wt.%yttria and mullite (BSAS) two-layer composite coating systems were also prepared to quantitatively evaluate the interface strain tolerance of the coating system under thermal cycling conditions based on the dilatomentry. The cyclic response of the coating strain tolerance behavior and interface degradation as a function of cycle number will also be discussed.
Biosensors can be used to screen or select for small molecule production in engineered microbes. However, mutations to the biosensor that interfere with accurate signal transduction are common, producing an excess of false positives. Strategies have been developed to avoid this limitation by physically separating the production pathway and biosensor, but these approaches have only been applied to screens, not selections. In this work, we have developed a novel biosensor-mediated selection strategy using competition between cocultured bacteria. When applied to the biosynthesis of cis,cis-muconate, we show that this strategy yields a selective advantage to producer strains that outweighs the costs of production. By encapsulating the competitive cocultures into microfluidic droplets, we successfully enriched the muconate-producing strains from a large population of control nonproducers. Finally, facile selections for small molecule production will increase testing throughput for engineered microbes and allow for the rapid optimization of novel metabolic pathways.
Elastic plastic stress strain states in cooled turbine blade airfoils were calculated by three methods for the initial takeoff transient of an advanced technology aircraft engine. The three analytical methods compared were a three dimensional elastic plastic, finite element analysis, a three dimensional, elastic, finite element analysis, and a one dimensional, elastic plastic, beam theory analysis. Structural analyses were performed for eight cases involving different combinations of mechanical and thermal loading on impingement cooled airfoils with and without leading edge film cooling holes. The von Mises effective total strains at maximum takeoff computed from the elastic and elastic plastic finite element analyses agreed with 9 percent for rotating airfoils and 28 percent for stationary airfoils with the elastic results on the conservative side.
Microalgae are photosynthetic microorganisms that can use CO2 and sunlight to generate the complex biomolecules necessary for their survival. These biomolecules include energy-rich lipid compounds that can be converted using existing refinery equipment into valuable bio-derived fuels, including jet fuel for military and commercial use. Through a dedicated and thorough collaborative research,development and deployment program, the team of the National Renewable Energy Laboratory (NREL) and Chevron will identify a suitable algae strain that will surpass the per-acre biomass productivity of terrestrial plant crops.
Understanding the molecular basis of regulated nitrogen (N 2 ) fixation is essential for engineering N 2 -fixing bacteria that fulfill the demand of crop plants for fixed nitrogen, reducing our reliance on synthetic nitrogen fertilizers. In Azotobacter vinelandii and many other members of Proteobacteria, the two-component system comprising the anti-activator protein (NifL) and the Nif-specific transcriptional activator (NifA)controls the expression of nif genes, encoding the nitrogen fixation machinery. The NifL-NifA system evolved the ability to integrate several environmental cues, such as oxygen, nitrogen, and carbon availability. The nitrogen fixation machinery is thereby only activated under strictly favorable conditions, enabling diazotrophs to thrive in competitive environments. While genetic and biochemical studies have enlightened our understanding of how NifL represses NifA, the molecular basis of NifA sequestration by NifL depends on structural information on their interaction. Here, we present mechanistic insights into how nitrogen fixation is regulated by combining biochemical and genetic approaches with a low-resolution cryo-electron microscopy (cryo-EM) map of the oxidized NifL-NifA complex. Our findings define the interaction surface between NifL and NifA and reveal how this interaction can be manipulated to generate bacterial strains with increased nitrogen fixation rates able to secrete surplus nitrogen outside the cell, a crucial step in engineering improved nitrogen delivery to crop plants.
We report a fiber-optic sensor configuration with a cascaded fiber Bragg grating (FBG) and a silicon Fabry-Perot interferometer (FPI) for simultaneous measurement of temperature and strain. The sensor is composed of a 5 mm FBG on a single mode fiber and a 100μm thick silicon FPI attached to the tip of the optical fiber. The FBG is surface mounted on the host structure, and the FPI tip is suspended. Due to the stress-free, cantilever configuration, the silicon FPI is insensitive to strain, but sensitive to temperature with a sensitivity much higher than the FBG due to the large thermo-optic coefficient of silicon. The sensor is tested from room temperature to 100°C with varying strain up to ∼150με. The silicon FPI provides high temperature sensitivity of 89 pm/°C unaffected by strain. Since the FBG is attached to the host structure, it is affected by both thermal and mechanical strain; the sensitivity of these was experimentally obtained 32 pm/°C and 1.09 pm / με, respectively. Interrogated with a broadband light source and a high-speed spectrometer, the sensor shows temperature and strain resolutions of 1.9 × 10 −3 °C and 0.042 με, respectively. Here, due to the small size, enhanced sensitivity, and high resolution, this cascaded FBG-FPI sensor can be used in applications where accurate measurement of temperature and strain is required.
An electrochemically driveable actuator according to one embodiment includes a nanoporous carbon aerogel composition capable of exhibiting charge-induced reversible strain when wetted by an electrolyte and a voltage is applied thereto. An electrochemically driven actuator according to another embodiment includes a nanoporous carbon aerogel composition wetted by an electrolyte; and a mechanism for causing charge-induced reversible strain of the composition. A method for electrochemically actuating an object according to one embodiment includes causing charge-induced reversible strain of a nanoporous carbon aerogel composition wetted with an electrolyte to actuate the object by the strain.
The ability to fine tune gene expression has created the field of metabolic pathway optimization and balancing where a variety of factors affecting flux balance are carefully modulated to improve product titers, yields and productivity. Using a library of isopropyl β-D- 1 -thiogalactopyranoside (IPTG)-inducible mutant T 7 promoters of varied strength a combinatorial method was developed for transcriptional balancing of the violacein pathway. Violacein biosynthesis involves a complex five-gene pathway that is an excellent model for exploratory metabolic engineering efforts into pathway regulation and control due to many colorful intermediates and side products allowing for easy analysis and strain comparison. Upon screening approximately 4% of the total initial library, several high-titer mutants were discovered that resulted in up to a 63-fold improvement over the control strain. With further fermentation optimization, titers were improved to 1829 ± 46 mg/L; a 2.6-fold improvement in titer and a 30-fold improvement in productivity from previous literature reports.
Methyl methacrylate (MMA) is an important petrochemical with many applications. However, its manufacture has a large environmental footprint. Combined biological and chemical synthesis (semisynthesis) may be a promising alternative to reduce both cost and environmental impact, but strains that can produce the MMA precursor (citramalate) at low pH are required. A non-conventional yeast, Issatchenkia orientalis , may prove ideal, as it can survive extremely low pH. Here, we demonstrate the engineering of I. orientalis for citramalate production. Using sequence similarity network analysis and subsequent DNA synthesis, we selected a more active citramalate synthase gene (cimA) variant for expression in I. orientalis . We then adapted a piggyBac transposon system for I. orientalis that allowed us to simultaneously explore the effects of different cimA gene copy numbers and integration locations. A batch fermentation showed the genome-integrated-cimA strains produced 2.0 g/L citramalate in 48 h and a yield of up to 7% mol citramalate/mol consumed glucose. These results demonstrate the potential of I. orientalis as a chassis for citramalate production.
Methyl methacrylate (MMA) is an important petrochemical with many applications. However, its manufacture has a large environmental footprint. Combined biological and chemical synthesis (semisynthesis) may be a promising alternative to reduce both cost and environmental impact, but strains that can produce the MMA precursor (citramalate) at low pH are required. A non-conventional yeast, Issatchenkia orientalis, may prove ideal, as it can survive extremely low pH. Here, we demonstrate the engineering of I. orientalis for citramalate pro- duction. Using sequence similarity network analysis and subsequent DNA synthesis, we selected a more active citramalate synthase gene (cimA) variant for expression in I. orientalis. We then adapted a piggyBac transposon system for I. orientalis that allowed us to simultaneously explore the effects of different cimA gene copy numbers and integration locations. A batch fermentation showed the genome-integrated-cimA strains produced 2.0 g/L citramalate in 48 h and a yield of up to 7% mol citramalate/mol consumed glucose. These results demonstrate the potential of I. orientalis as a chassis for citramalate production.
Antiferromagnets (AFMs) have recently gathered a large amount of attention as a potential replacement for ferromagnets (FMs) in spintronic devices due to their lack of stray magnetic fields, invisibility to external magnetic probes, and faster magneti- zation dynamics. Their development into a practical technology, however, has been hampered by the small number of materials where the antiferromagnetic state can be both controlled and read out. We show here, that by relaxing the strict criterion on pure antiferromagnetism, we can engineer a new class of magnetic materials that 1 overcome these limitations. This is accomplished by stabilizing a non-collinear mag- netic phase in LaNiO 3 /La 2/3 Sr 1/3 MnO 3 superlattices. This state can be continuously tuned between AFM and FM coupling through varying either superlattice spacing, strain, applied magnetic field, or temperature. By using this new “knob” to tune magnetic ordering, we take a nanoscale materials-by-design approach to engineering ferromagnetic-like controllability into antiferromagnetic synthetic magnetic structures. This approach can be used to trade-off between the favorable and unfavorable proper- ties of FMs and AFMs when designing realistic resistive antiferromagnetic memories. We demonstrate a memory device in one such superlattice, where the magnetic state of the non-collinear antiferromagnet is reversibly switched between different orientations using a small magnetic field and read out in real time with anisotropic magnetoresistance measurements.
Recent breakthroughs in advanced manufacturing capabilities have made it possible to design and print sophisticated topologies of cellular structures using diverse engineering materials such as metals, polymers, and ceramics. In these architectured materials, it is often desirable to tailor the mechanical properties by altering the unit cell topology. This necessitates an in-depth understanding of how the topology of the unit cell structure affects the macroscopic behavior of the material in both the linear and the non-linear regimes encountered under large compression. Here, we have developed a machine learning (ML) approach capable of accelerating the prediction of the stress–strain response of a polymer-based cellular structure under uniaxial confined compression. As part of generating the training data for ML, 60,000 mesostructures were generated using a relatively novel approach based on cellular automata, and their corresponding stress–strain responses were obtained from the finite element simulations. Principal component analysis (PCA) was used to reduce the dimensionality of the stress–strain curves. With only 20 principal components, PCA captured 99.89% of the variance in the stress–strain curves while reducing the dimensionality by 5X. ML using supervised autoencoder was able to successfully speed up the prediction of the non-linear stress–strain response of a unit cell by up to 4600X. The proposed method can serve as an efficient data generation tool and a rapid means for predicting the structure–property relationship through accelerated forward modeling of cellular materials under compaction, in cases where the macroscopic stress–strain response is governed by the unit-cell topology.