Intermediate product control in cascade reaction for one-pot production of ε-caprolactone by Escherichia coli.

ε-Caprolactone is an important non-toxic compound for polymer synthesis like polycaprolactone which has been widely used in drug delivery and degradable plastics. To meet the demand for a green economy, a bi-enzymatic cascade, consisting of an alcohol dehydrogenase (ADH) and a cyclohexanone monooxygenase (CHMO), was designed and introduced into Escherichia coli to synthesize ε-caprolactone from cyclohexanol with a self-sufficient NADPH-cofactor regeneration system. To further improve the catalytic efficiency, a carbonyl group-dependent colorimetric method using inexpensive 2,4-dinitrophenylhydrazine (DNPH) was developed for assay of cyclohexanone, an intermediate production of cascade reaction. It can be used to screen mutant strains with high catalytic efficiency from high-throughput library by detecting the absorbance value in microtiter plates (MTP) instead of gas chromatography (GC) analysis. Moreover, an RBS combinatorial library was constructed for balancing the expression of ADH and CHMO from two independent transcriptional units. After the high-throughput screening based on intermediate product control, an optimal variant with higher substrate tolerance and long-term stability was obtained from RBS combinatorial library. Through a fed-batch process, ε-caprolactone production reached 148.2 mM after 70 h of reaction under the optimized conditions, which was the highest yield achieved to date.

Discovery and Identification of a Novel Tag of HlyA60 for Protein Active Aggregate Formation in Escherichia coli.

The strategy of active aggregation tag fusion expression with target proteins can solve the problems of restricted expression, inefficient purification, and laborious immobilization faced in the production of recombinant proteins in Escherichia coli. We localized a novel active aggregation peptide HlyA60 from the hemolysin A secretion system, which can effectively induce aggregate formation with satisfactory protein activities in E. coli after fusion expression with the protein of interest. Based on structural prediction and surface properties, the process of active aggregation of HlyA60 through electrostatic interactions and hydrophobic interactions was analyzed. To investigate the potential application of HlyA60 as an efficient aggregation tag, it was fused with acetyl xylan esterase and lipase A, separately. The resulting fusion proteins demonstrated active aggregation rates of 97.6 and 66.7%, respectively, leading to 1.9-fold and 1.7-fold increases in bacterial density at the end of fermentation. The AXE-HlyA60 fusion protein, which exhibited superior performance, was subjected to purification and immobilization. It was able to achieve column-free purification with an impressive 98.8% recovery and in situ immobilization; the immobilization enabled 30 cycles of reactions to take place with 85% residual activity maintained. Our findings provide a novel tool for efficiently producing recombinant proteins in E. coli.

Green Production of a High-value Mosquito Insecticide of Nootkatone from Seaweed Hydrolysates.

Nootkatone is a type of valuable sesquiterpene that is widely used in food, cosmetics, fragrance, and other fields. The industry is faced with a major challenge due to the high expenses associated with plant-extracted nootkatone. We have developed a fermentation process for valencene production using seaweed hydrolysate as a carbon source via engineered Saccharomyces cerevisiae. Reduced-pressure distillation purified valencene was used as a substrate, and a yeast strain carrying HPO/AtCPR1 and ADH genes was constructed for whole-cell catalysis. After biotransformation at 25 °C for 3 h, a high yield of 73% for nootkatone production was obtained. Further, simple rotary evaporation was used to obtain nootkatone with a high purity of 97.4%. Mosquito-repellent testing showed that 1% nootkatone has a mosquito-repellent effect lasting up to 6 h, which is comparable to the 20% N,N-diethyl-meta-toluamide (DEET) effect. This study provided practical experience for developing third-generation biomass resources, generating new ideas for green manufacturing of valuable chemical products, and serving as a reference for creating efficient and eco-friendly mosquito repellents.

Systematic Optimization of HPO-CPR to Boost (+)-Nootkatone Synthesis in Engineered Saccharomyces cerevisiae.

As an important and expensive natural sesquiterpene compound in grapefruit, the interest in (+)-nootkatone is stimulated by its strong grapefruit-like odor and physiological activities, which induce efforts for its microbial production. However, the low catalytic efficiency of the cytochrome P450-P450 reductase (HPO-CPR) system is the main challenge. We developed a high-throughput screening (HTS) method using the principle of the color reaction between carbonyl compounds and 2,4-dinitrophenylhydrazine (DNPH), which could rapidly screen the activity of candidate HPO mutants. After optimizing the pairing of HPO and CPR and through semirational design, the optimal mutant HPO_M18 with catalytic performance 2.54 times that of the initial was obtained. An encouraging (+)-nootkatone titer of 2.39 g/L was achieved through two-stage fed-batch fermentation after metabolic engineering and endoplasmic reticulum engineering, representing the highest titer reported to date. Our findings lay the foundation for the development of an economically viable bioprocess for (+)-nootkatone.

Probing the Synergistic Ratio of P450/CPR To Improve (+)-Nootkatone Production in Saccharomyces cerevisiae.

(+)-Nootkatone is an expensive sesquiterpene substance found in grapefruit peels and the heartwood of yellow cedar. It can be used as a food additive, perfume, and insect repellent; therefore, its highly efficient production is greatly needed. However, the low catalytic efficiency of the membrane-anchored cytochrome P450/P450 reductase system (HPO/AtCPR) is the main challenge and limits the production of (+)-nootkatone. We developed an effective high-throughput screening system based on cell wall destruction to probe the optimal ratio of HPO/AtCPR, which achieved a twofold elevation in (+)-valencene oxidation in Saccharomyces cerevisiae. An engineered strain PK2RI-AtC/Hm6A was constructed to realize de novo (+)-nootkatone production by a series of metabolic engineering strategies. In biphasic fed-batch fermentation, maximum titers of 3.73 and 1.02 g/L for (+)-valencene and (+)-nootkatone, respectively, were achieved. The dramatically improved performance of the constructed S. cerevisiae provides an excellent approach for economical production of (+)-nootkatone from glucose.

Orientation, pattern center refinement and deformation state extraction through global optimization algorithms.

Global optimization algorithms have been adopted to the simultaneously refinement of orientation and pattern center for electron backscatter diffraction patterns as well as deformation state extraction. The hyperparameter space and mutation schemes of differential evolution (DE) algorithm has been thoroughly investigated and showed to be a more efficient algorithm than the particle swarm optimization (PSO) algorithm. The optimal hyperparameters for DE generally depend on conditions such as the number of variables to be optimized and the size of bounded search space but reasonably close initial values for crossover probability is 0.9, mutation factor is 0.5, population size is ten times the number of variables, and number of iterations is at least 100. Validation on a set of simulated undeformed single crystal nickel patterns reveals a mean accuracy of ≈0.03° and ≈0.01% detector width across a large field of view. In addition, validation using noisy simulated deformed patterns with known deformation state and pattern center shows that the mean accuracy of shear strain and rotation components is ≈0.001 and for the normal strain ≈0.002.

Corona-Enabled Electrostatic Printing for Ultra-fast Manufacturing of Binder-Free Multifunctional E-Skins.

As essential components in intelligent systems, printed soft electronics (PSEs) are playing crucial roles in public health, national security, and economics. Innovations in printing technologies are required to promote the broad application of high-performance PSEs at a low cost. However, current printing techniques are still facing long-lasting challenges in addressing the conflict between printing speed and performance. To overcome this challenge, we developed a new corona-enabled electrostatic printing (CEP) technique for ultra-fast (milliseconds) roll-to-roll (R2R) manufacturing of binder-free multifunctional e-skins. The printing capability and controllability of CEP were investigated through parametric studies and microstructure observation. The electric field generation, material transfer, and particle amount and size selecting mechanisms were numerically and experimentally studied. CEP-printed graphene e-skins were demonstrated to possess an outstanding strain sensing performance. The binder-free feature of the CEP-assembled networks enables them to provide pressure sensitivity as low as 2.5 Pa and capability to detect acoustic signals of hundreds of hertz in frequency. Furthermore, the CEP technique was utilized to pattern different types of functional materials (e.g., graphene and thermochromic polymers) onto different substrates (e.g., tape and textile). Overall, this study demonstrated that CEP can be a novel contactless and ultra-fast manufacturing platform compatible with the R2R process for fabricating high-performance, scalable, and low-cost soft electronics.

Reducing metal/graphene contact resistance via N, N-dimethylacetamide-assisted clean fabrication process.

Contact resistance (RC) is of great importance for radio frequency (RF) applications of graphene, especially graphene field effect transistors (FETs) with short channel. FETs and transmission line model test structures based on chemical vapor deposition grown graphene are fabricated. The effects of employing traditional lithography solvent (Acetone) and strong solvents for photo resist, such as N, N-Dimethylacetamide (ZDMAC) and N-Methyl pyrrolidone (NMP), are systematically investigated. It was found that ZDMAC and NMP have more proficiency than acetone to remove the photo-resist residues and contaminations attached on graphene surface, enabling clean surface of graphene. However, strong solvents are found to destroy the lattice structure of graphene channel and induce defects in graphene lattice. Clean surface contributes to a significant reduction in theRCbetween graphene channel and metal electrode, and the defects introduced on graphene surface underneath metal electrodes also contribute the reduction ofRC. But defects and deformation of lattice will increase the resistance in graphene channel and lead to the compromise of device performance. To address this problem, a mix wet-chemical approach employing both acetone and ZDMAC was developed in our study to realize a 19.07% reduction ofRC, without an unacceptable mass production of defects.

Stable p-type chemical doping of graphene with reduced contact resistance by single-layer perfluorinated polymeric sulfonic acid.

Recently, graphene has led to unprecedented progress in device performance at the atom limit. A high performance of field-effect transistors requires a low graphene-metal contact resistance. However, the chemical doping methods used to tailor or improve the properties of graphene are sensitive to ambient conditions. Here, we fabricate a single-layer perfluorinated polymeric sulfonic acid (PFSA), also known as Nafion, between the graphene and the substrate as a p-type dopant. The PFSA doping method, without inducing any additional structural defects, reduces the contact resistance of graphene by ∼28.8%, which has a significant impact on practical applications. This reduction can be maintained for at least 67 days due to the extreme stability of PFSA. Effective, uniform and stable, the PFSA doping method provides an efficient way to reduce the contact resistance of graphene applications.

Amorphization in extreme deformation of the CrMnFeCoNi high-entropy alloy.

Ever-harsher service conditions in the future will call for materials with increasing ability to undergo deformation without sustaining damage while retaining high strength. Prime candidates for these conditions are certain high-entropy alloys (HEAs), which have extraordinary work-hardening ability and toughness. By subjecting the equiatomic CrMnFeCoNi HEA to severe plastic deformation through swaging followed by either quasi-static compression or dynamic deformation in shear, we observe a dense structure comprising stacking faults, twins, transformation from the face-centered cubic to the hexagonal close-packed structure, and, of particular note, amorphization. The coordinated propagation of stacking faults and twins along {111} planes generates high-deformation regions, which can reorganize into hexagonal packets; when the defect density in these regions reaches a critical level, they generate islands of amorphous material. These regions can have outstanding mechanical properties, which provide additional strengthening and/or toughening mechanisms to enhance the capability of these alloys to withstand extreme loading conditions.

EBSD pattern simulations for an interaction volume containing lattice defects.

A quantitative understanding of the effect of the spatial distribution and density of lattice defects on the electron backscatter diffraction patterns requires careful consideration of the electron-matter interaction volume and the traction free boundary condition on the deformation field for near-surface defects. In this work, we couple a depth-specific dynamical electron scattering simulation with an approximate crystal deformation model to generate a single diffraction pattern from an interaction volume containing lattice defects. Two case studies are considered, namely a single edge dislocation and a low angle grain boundary. Their displacement fields, derived from the three-dimensional Yoffe-Shaibani-Hazzeldine's dislocation field model, are fed into the simulation and the resulting diffraction patterns are cross-validated using the HR-EBSD technique. In addition, diffraction contrast associated with defect deformation field is investigated with the virtual beam technique. Pattern diffuseness is quantitatively analyzed in the frequency domain as a function of dislocation density.

Phase Mapping in EBSD Using Convolutional Neural Networks.

The emergence of commercial electron backscatter diffraction (EBSD) equipment ushered in an era of information rich maps produced by determining the orientation of user-selected crystal structures. Since then, a technological revolution has occurred in the quality, rate detection, and analysis of these diffractions patterns. The next revolution in EBSD is the ability to directly utilize the information rich diffraction patterns in a high-throughput manner. Aided by machine learning techniques, this new methodology is, as demonstrated herein, capable of accurately separating phases in a material by crystal symmetry, chemistry, and even lattice parameters with fewer human decisions. This work is the first demonstration of such capabilities and addresses many of the major challenges faced in modern EBSD. Diffraction patterns are collected from a variety of samples, and a convolutional neural network, a type of machine learning algorithm, is trained to autonomously recognize the subtle differences in the diffraction patterns and output phase maps of the material. This study offers a path to machine learning coupled phase mapping as databases of EBSD patterns encompass an increasing number of the possible space groups, chemistry changes, and lattice parameter variations.

Deep Neural Network Enabled Space Group Identification in EBSD.

Electron backscatter diffraction (EBSD) is one of the primary tools in materials development and analysis. The technique can perform simultaneous analyses at multiple length scales, providing local sub-micron information mapped globally to centimeter scale. Recently, a series of technological revolutions simultaneously increased diffraction pattern quality and collection rate. After collection, current EBSD pattern indexing techniques (whether Hough-based or dictionary pattern matching based) are capable of reliably differentiating between a "user selected" set of phases, if those phases contain sufficiently different crystal structures. EBSD is currently less well suited for the problem of phase identification where the phases in the sample are unknown. A pattern analysis technique capable of phase identification, utilizing the information-rich diffraction patterns potentially coupled with other data, such as EDS-derived chemistry, would enable EBSD to become a high-throughput technique replacing many slower (X-ray diffraction) or more expensive (neutron diffraction) methods. We utilize a machine learning technique to develop a general methodology for the space group classification of diffraction patterns; this is demonstrated within the $\lpar 4/m\comma \;\bar{3}\comma \;\;2/m\rpar$ point group. We evaluate the machine learning algorithm's performance in real-world situations using materials outside the training set, simultaneously elucidating the role of atomic scattering factors, orientation, and pattern quality on classification accuracy.

Crystal symmetry determination in electron diffraction using machine learning.

Electron backscatter diffraction (EBSD) is one of the primary tools for crystal structure determination. However, this method requires human input to select potential phases for Hough-based or dictionary pattern matching and is not well suited for phase identification. Automated phase identification is the first step in making EBSD into a high-throughput technique. We used a machine learning-based approach and developed a general methodology for rapid and autonomous identification of the crystal symmetry from EBSD patterns. We evaluated our algorithm with diffraction patterns from materials outside the training set. The neural network assigned importance to the same symmetry features that a crystallographer would use for structure identification.

Novel remapping approach for HR-EBSD based on demons registration.

In this study, the possibility of utilizing a computer vision algorithm, i.e., demons registration, to accurately remap electron backscatter diffraction patterns for high resolution electron backscatter diffraction (HR-EBSD) applications is presented. First, the angular resolution of demons registration is demonstrated to be lower than the conventional cross-correlation based method, particularly at misorientation angles >0.157 rad. In addition, GPU acceleration has been applied to significantly boost the speed of iterative registration between a pair of patterns with 0.175 rad misorientation to under 1 s. Second, demons registration is implemented as a first-pass remapping, followed by a second pass cross-correlation method, which results in angular resolution of ~0.5 × 10-4 rad, a phantom stress value of ~35 MPa and phantom strain of ~2 × 10-4, on dynamically simulated patterns, without the need of implementing robust fitting or iterative remapping. Lastly, the new remapping method is applied to a large experimental dataset collected from an as-built additively-manufactured Inconel 625 cube, which shows significant residual stresses built-up near the large columnar grain region and regularly arranged GND structures.

High production of valencene in Saccharomyces cerevisiae through metabolic engineering.

BACKGROUND: The biological synthesis of high value compounds in industry through metabolically engineered microorganism factories has received increasing attention in recent years. Valencene is a high value ingredient in the flavor and fragrance industry, but the low concentration in nature and high cost of extraction limits its application. Saccharomyces cerevisiae, generally recognized as safe, is one of the most commonly used gene expression hosts. Construction of S. cerevisiae cell factory to achieve high production of valencene will be attractive. RESULTS: Valencene was successfully biosynthesized after introducing valencene synthase into S. cerevisiae BJ5464. A significant increase in valencene yield was observed after down-regulation or knock-out of squalene synthesis and other inhibiting factors (such as erg9, rox1) in mevalonate (MVA) pathway using a recyclable CRISPR/Cas9 system constructed in this study through the introduction of Cre/loxP. To increase the supplement of the precursor farnesyl pyrophosphate (FPP), all the genes of FPP upstream in MVA pathway were overexpressed in yeast genome. Furthermore, valencene expression cassettes containing different promoters and terminators were compared, and PHXT7-VS-TTPI1 was found to have excellent performance in valencene production. Finally, after fed-batch fermentation in 3 L bioreactor, valencene production titer reached 539.3 mg/L with about 160-fold improvement compared to the initial titer, which is the highest reported valencene yield. CONCLUSIONS: This study achieved high production of valencene in S. cerevisiae through metabolic engineering and optimization of expression cassette, providing good example of microbial overproduction of valuable chemical products. The construction of recyclable plasmid was useful for multiple gene editing as well.

Automated Reconstruction of Spherical Kikuchi Maps.

An automated approach to fully reconstruct spherical Kikuchi maps from experimentally collected electron backscatter diffraction patterns and overlay each pattern onto its corresponding position on a simulated Kikuchi sphere is presented in this study. This work demonstrates the feasibility of warping any Kikuchi pattern onto its corresponding location of a simulated Kikuchi sphere and reconstructing a spherical Kikuchi map of a known phase based on any set of experimental patterns. This method consists of the following steps after pattern collection: (1) pattern selection based on multiple threshold values; (2) extraction of multiple scan parameters and phase information; (3) generation of a kinematically simulated Kikuchi sphere as the "skeleton" of the spherical Kikuchi map; and (4) overlaying the inverse gnomonic projection of multiple selected patterns after appropriate pattern center calibration and refinement. The proposed method is the first automated approach to reconstructing spherical Kikuchi maps from experimental Kikuchi patterns. It potentially enables more accurate orientation calculation, new pattern center refinement methods, improved dictionary-based pattern matching, and phase identification in the future.

Stepwise engineering of Saccharomyces cerevisiae to produce (+)-valencene and its related sesquiterpenes.

(+)-Valencene and (+)-nootkatone are high value-added sesquiterpenoids found in grapefruit. The synthesis of (+)-nootkatone by chemical oxidation from (+)-valencene cannot meet the increasing demand in natural aromatics markets. Development of a viable bioprocess using microorganisms is attractive. According to the yields of ß-nootkatol and (+)-nootkatone by strains harboring different expression cassettes in the resting cell assay, premnaspirodiene oxygenase from Hyoscyamus muticus (HPO), cytochrome P450 reductase from Arabidopsis thaliana (AtCPR) and alcohol dehydrogenase (ADH1) from Saccharomyces cerevisiae were finally selected and overexpressed in CEN·PK2-1Ca, yielding ß-nootkatol and (+)-nootkatone with 170.5 and 45.6 mg L-1 ethyl acetate, respectively. A combinational engineering strategy including promoter change, regulator ROX1 knockout, squalene pathway inhibition, and tHMGR overexpression was performed to achieve de novo (+)-valencene production. Subsequent culture investigations found that galactose as the induced carbon source and a lower temperature (25 °C) were beneficial to target accumulation. Also, replacing the inducible promoters (GAL1) of HPO and AtCPR with constitutive promoters (HXT7 and CYC1) dramatically increased the ß-nootkatol accumulation from 108.2 to 327.8 mg L-1 ethyl acetate in resting-cell experiments using (+)-valencene as a substrate. Finally, the total terpenoid titer of the engineered strain of PK2-25 using glucose as a carbon source was improved to 157.8 mg L-1 cell culture, which was 56 times the initial value. We present a new candidate for production of (+)-valencene and its related sesquiterpenoids with attraction for industry.