Enhanced CO2 Reactive Capture and Conversion Using Aminothiolate Ligand-Metal Interface.

Metallic catalyst modification by organic ligands is an emerging catalyst design in enhancing the activity and selectivity of electrocatalytic carbon dioxide (CO2) reactive capture and reduction to value-added fuels. However, a lack of fundamental science on how these ligand-metal interfaces interact with CO2 and key intermediates under working conditions has resulted in a trial-and-error approach for experimental designs. With the aid of density functional theory calculations, we provided a comprehensive mechanism study of CO2 reduction to multicarbon products over aminothiolate-coated copper (Cu) catalysts. Our results indicate that the CO2 reduction performance was closely related to the alkyl chain length, ligand coverage, ligand configuration, and Cu facet. The aminothiolate ligand-Cu interface significantly promoted initial CO2 activation and lowered the activation barrier of carbon-carbon coupling through the organic (nitrogen (N)) and inorganic (Cu) interfacial active sites. Experimentally, the selectivity and partial current density of the multicarbon products over aminothiolate-coated Cu increased by 1.5-fold and 2-fold, respectively, as compared to the pristine Cu at -1.16 VRHE, consistent with our theoretical findings. This work highlights the promising strategy of designing the ligand-metal interface for CO2 reactive capture and conversion to multicarbon products.

Enzyme selection, optimization, and production toward biodegradation of post-consumer poly(ethylene terephthalate) at scale.

Poly(ethylene terephthalate) (PET) is one of the world's most widely used polyester plastics. Due to its chemical stability, PET is extremely difficult to hydrolyze in a natural environment. Recent discoveries in new polyester hydrolases and breakthroughs in enzyme engineering strategies have inspired enormous research on biorecycling of PET. This study summarizes our research efforts toward large-scale, efficient, and economical biodegradation of post-consumer waste PET, including PET hydrolase selection and optimization, high-yield enzyme production, and high-capacity enzymatic degradation of post-consumer waste PET. First, genes encoding PETase and MHETase from Ideonella sakaiensis and the ICCG variant of leaf-branch compost cutinase (LCCICCG ) were codon-optimized and expressed in Escherichia coli BL21(DE3) for high-yield production. To further lower the enzyme production cost, a pelB leader sequence was fused to LCCICCG so that the enzyme can be secreted into the medium to facilitate recovery. To help bind the enzyme on the hydrophobic surface of PET, a substrate-binding module in a polyhydroxyalkanoate depolymerase from Alcaligenes faecalis (PBM) was fused to the C-terminus of LCCICCG . The resulting four different LCCICCG variants (LCC, PelB-LCC, LCC-PBM, and PelB-LCC-PBM), together with PETase and MHETase, were compared for PET degradation efficiency. A fed-batch fermentation process was developed to produce the target enzymes up to 1.2 g L-1 . Finally, the best enzyme, PelB-LCC, was selected and used for the efficient degradation of 200 g L-1 recycled PET in a well-controlled, stirred-tank reactor. The results will help develop an economical and scalable biorecycling process toward a circular PET economy.

Cellulose Fast Pyrolysis Activated by Intramolecular Hydrogen Bonds.

The conversion of inedible biomass by fast pyrolysis is a promising route for sustainable production of renewable fuels and value-added chemicals, but low selectivity toward desired products hampers its economic viability. Understanding the molecular-level reaction pathways of biomass fast pyrolysis could be the key to overcoming this challenge. However, the effects of intramolecular and interchain hydrogen bonds near the reaction center have not been thoroughly explored. In this work, the reaction pathways and kinetics of fast pyrolysis of cellulose, a major component of biomass, were investigated using the density functional theory. A new intramolecular hydroxyl-activated mechanism is presented for cellulose activation. Our calculations incorporating noncovalent interactions accurately captured the activation energy of 50.8 kcal mol-1, agreeable with the apparent activation energy measured experimentally. The findings of cellulose pyrolysis provide insights into the investigation of interactions during real-life biomass pyrolysis.

Understanding Consequences and Tradeoffs of Melt Processing as a Pretreatment for Enzymatic Depolymerization of Poly(ethylene terephthalate).

Melt extrusion pretreatment of poly(ethylene terephthalate) (PET) prior to enzymatic depolymerization with an unpurified leaf branch compost cutinase enzyme cocktail is explored to ascertain the efficiency gained by different processing methods on the enzymatic depolymerization of PET. Specific surface area (SSA) is investigated as a key factor in reducing depolymerization time. Higher SSA substrates (>5.6 mm2  mg-1 ) show higher depolymerization rates (≈0.88 g L-1 terephthalic acid [TPA] per day) and no induction phase, while lower SSA substrates (≈4.3, 4.4, and 5.6 mm2  mg-1 ) show, after an initial induction phase, similar depolymerization rates (≈0.46, 0.45, and 0.44 g L-1 TPA per day) despite increases in SSA of up to 30%. The mechanism of enzymatic depolymerization manifests in the appearance of anisotropic pitting. Longer incubation time used to overcome the induction phase in low SSA substrates allows for nearly full recovery of monomeric products, but manual pregrinding of extruded PET sharply increases SSA, depolymerization rate, and substrate crystallinity which may decrease the maximum recycled yield of the product materials. An estimate of the energy cost of increasing SSA is made and its effects on material properties are discussed. This work highlights key material structure and pretreatment aspects influencing the enzymatic recycling of PET.

Microbial synthesis of wax esters.

Microbial synthesis of wax esters (WE) from low-cost renewable and sustainable feedstocks is a promising path to achieve cost-effectiveness in biomanufacturing. WE are industrially high-value molecules, which are widely used for applications in chemical, pharmaceutical, and food industries. Since the natural WE resources are limited, the WE production mostly rely on chemical synthesis from rather expensive starting materials, and therefore solution are sought from development of efficient microbial cell factories. Here we report to engineer the yeast Yarrowia lipolytica and bacterium Escherichia coli to produce WE at the highest level up to date. First, the key genes encoding fatty acyl-CoA reductases and wax ester synthase from different sources were investigated, and the expression system for two different Y. lipolytica hosts were compared and optimized for enhanced WE production and the strain stability. To improve the metabolic pathway efficiency, different carbon sources including glucose, free fatty acid, soybean oil, and waste cooking oil (WCO) were compared, and the corresponding pathway engineering strategies were optimized. It was found that using a lipid substrate such as WCO to replace glucose led to a 60-fold increase in WE production. The engineered yeast was able to produce 7.6 g/L WE with a yield of 0.31 (g/g) from WCO within 120 h and the produced WE contributed to 57% of the yeast DCW. After that, E. coli BL21(DE3), with a faster growth rate than the yeast, was engineered to significantly improve the WE production rate. Optimization of the expression system and the substrate feeding strategies led to production of 3.7-4.0 g/L WE within 40 h in a 1-L bioreactor. The predominant intracellular WE produced by both Y. lipolytica and E. coli in the presence of hydrophobic substrates as sole carbon sources were C36, C34 and C32, in an order of decreasing abundance and with a large proportion being unsaturated. This work paved the way for the biomanufacturing of WE at a large scale.

Biomanufacturing of value-added products from oils or fats: A case study on cellular and fermentation engineering of Yarrowia lipolytica.

The United States produces more than 10 million tons of waste oils and fats each year. This paper aims to establish a new biomanufacturing platform that converts waste oils or fats into a series of value-added products. Our research employs the oleaginous yeast Yarrowia lipolytica as a case study for citric acid (CA) production from waste oils. First, we conducted the computational fluid dynamics (CFD) simulation of the bioreactor system and identified that the extracellular mixing and mass transfer is the first limiting factor of an oil fermentation process due to the insolubility of oil in water. Based on the CFD simulation results, the bioreactor design and operating conditions were optimized and successfully enhanced oil uptake and bioconversion in fed-batch fermentation experiments. After that, we investigated the impacts of cell morphology on oil uptake, intracellular lipid accumulation, and CA formation by overexpressing and deleting the MHY1 gene in the wild type Y. lipolytica ATCC20362. Fairly good linear correlations (R2 > 0.82) were achieved between cell morphology and productivities of biomass, lipid, and CA. Finally, fermentation kinetics with both glucose and oil substrates were compared and the oil fermentation process was carefully evaluated. Our study suggests that waste oils or fats can be economical feedstocks for biomanufacturing of many high-value products.

Effect of Metal Substrate on Electrocatalytic Property of Palladium Nanowire Array for High Performance Ethanol Electro-Oxidation.

In this research, a high performance, ionomer-free electrocatalyst based on vertically aligned palladium (Pd) nanowire array was developed as an anode electrode toward ethanol oxidation reaction (EOR) in an alkaline environment. Using a one-step electrodeposition method, the Pd nanowires with controlled length were obtained by varying the electrodeposition current density and the synthesis time. Scanning electron microcopy (SEM), energy dispersive X-ray spectroscopy (EDS), and X-ray powder diffraction (XRD) were employed to characterize the morphology, chemical composition, and crystal structure of the Pd nanowires. The length effects of the nanowires, in the range of 0.8-4.5 µm, and various metal substrates, such as Ag, Cu, Ni, and Ti, were investigated for their electrochemical activities. The results demonstrated that Ag was the most active substrate to facilitate the ethanol oxidation reaction of the Pd nanowire array (NWA) electrocatalyst, which could be related to its good electrical conductivity. The stability test of the Pd NWA/Ag over time for EOR was also carried out, and the catalytic activity was recovered after the electrode was replaced with a new ethanol solution. Electrochemical impedance spectroscopy (EIS) measurements were performed to provide insights in the electron transfer resistance between the electrode and analyte. Gas chromatography and UV-vis spectroscopy were employed to measure the concentration of chemical species, which helped elucidate the overall reaction mechanism on the electrode surfaces.

An algorithm to estimate aircraft cruise black carbon emissions for use in developing a cruise emissions inventory.

UNLABELLED: To provide accurate input parameters to the large-scale global climate simulation models, an algorithm was developed to estimate the black carbon (BC) mass emission index for engines in the commercial fleet at cruise. Using a high-dimensional model representation (HDMR) global sensitivity analysis, relevant engine specification/operation parameters were ranked, and the most important parameters were selected. Simple algebraic formulas were then constructed based on those important parameters. The algorithm takes the cruise power (alternatively, fuel flow rate), altitude, and Mach number as inputs, and calculates BC emission index for a given engine/airframe combination using the engine property parameters, such as the smoke number, available in the International Civil Aviation Organization (ICAO) engine certification databank. The algorithm can be interfaced with state-of-the-art aircraft emissions inventory development tools, and will greatly improve the global climate simulations that currently use a single fleet average value for all airplanes. IMPLICATIONS: An algorithm to estimate the cruise condition black carbon emission index for commercial aircraft engines was developed. Using the ICAO certification data, the algorithm can evaluate the black carbon emission at given cruise altitude and speed.

Measurement of naphthalene uptake by combustion soot particles.

In this study, we designed and constructed an experimental laboratory apparatus to measure the uptake of volatile organic compounds (VOCs) by soot particles. Results for the uptake of naphthalene (C10H8) by soot particles typical of those found in the exhaust of an aircraft engine are reported in this paper. The naphthalene concentration in the gas phase and naphthalene attached to the particles were measured simultaneously by a heated flame ionization detector (HFID) and a time-of-flight aerosol mass spectrometer (ToF AMS), respectively. The uptake coefficient for naphthalene on soot of (1.11 ± 0.06) × 10(-5) at 293 K was determined by fitting the HFID and AMS measurements of gaseous and particulate naphthalene to a kinetic model of uptake. When the gaseous concentration of naphthalene is kept below the saturation limit during these experiments, the uptake of naphthalene can be considered the dry mass accommodation coefficient.

Encoding of polycyclic Si-containing molecules for determining species uniqueness in automated mechanism generation.

Automated mechanism generation is an attractive way to understand the fundamental kinetics of complex reaction systems such as silicon hydride clustering chemistry. It relies on being able to tell molecules apart as they are generated. The graph theoretic foundation allows molecules to be identified using unique notations created from their connectivity. To apply this technique to silicon hydride clustering chemistry, a molecule canonicalization and encoding algorithm was developed to handle complex polycyclic, nonplanar species. The algorithm combines the concepts of extended connectivity and the idea of breaking ties to encode highly symmetric molecules. The connected components in the molecules are encoded separately and reassembled using a depth-first search method to obtain the correct string codes. A revised cycle-finding algorithm was also developed to properly select the cycles used for ring corrections when thermodynamic properties were calculated using group additivity. In this algorithm, the molecules are expressed explicitly as trees, and all linearly independent cycles of every size in the molecule are found. The cycles are then sorted according to their size and functionality, and the cycles with higher priorities will be used to include ring corrections. Applying this algorithm, more appropriate cycle selection and more accurate estimation of thermochemical properties of the molecules can be obtained.