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High Selectivity Benzoquinone Production from Oxidative Depolymerization of Poplar Lignin
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High Selectivity Benzoquinone Production from Oxidative Depolymerization of Poplar Lignin
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Unsupervised Clustering and Supervised Regression Learning to Select High Temperature Oxidation-Resistant Materials
High temperature oxidation and corrosion degradation mechanisms dictate the lifetime of materials critical to energy production. The combination of modeling and experimental approaches such as machine learning (ML) and data analytics, with sufficient experimental data, can accelerate the development of new materials while limiting its cost. In the present work, ML will be applied to two high temperature oxidation data libraries (Oak Ridge National Laboratory and National Air and Space Administration) that comprised of about 5000 mass change sample datasheets for a variety of materials and temperatures in dry air and air + 10 % H2O. A python code was developed to prepare the data for machine learning by collecting and formatting oxidation rate constants, alloy compositions and environment of exposure into a single data frame. Scikit-learn library and Statistics and Machine Learning Toolbox within MathWorks were then used to perform unsupervised clustering and supervised regression learning. The impact of dataset distribution on the performance of the developed ML models was evaluated. Potential strategies to improve the predictions and enhance extrapolative capability of the previously trained model were investigated.
Selective Chloride-Mediated Neat Ethanol Oxidation to 1,1-Diethoxyethane via an Electrochemically Generated Ethyl Hypochlorite Intermediate
Selective primary alcohol oxidation to form aldehydes products without overoxidation to carboxylic acids remains a key chemistry challenge. Using simple alkylammonium chloride as the electrolyte with a glassy carbon working electrode in neat ethanol solvent, 1,1-diethoxyethane (DEE) was prepared with >95% faradaic efficiency (FE). DEE serves as a storage platform protecting acetaldehyde from overoxidation and volatilization. UV–vis spectroscopy shows that the reaction proceeds through an ethyl hypochlorite intermediate as the sole chloride oxidation product, and that this intermediate decomposes unimolecularly (rate constant k = (6.896 ± 0.516) × 10 –4 s –1 ) to form HCl catalyst and acetaldehyde, which undergoes rapid nucleophilic attack by ethanol solvent to form the DEE product. As a result, this indirect oxidation mechanism enables ethanol oxidation at much less positive potentials due to the fast kinetics for chloride anion oxidation.
Crossing the Oxo‐Peroxo Wall for Selective Electrochemical Epoxidation
Electrochemical oxidation in water requires the formation of reactive oxygen species to be able to oxidize unsaturated hydrocarbons to epoxides, aldehydes, and ketones. These reactions, broadly classified as alternative oxidation reactions (AOR), directly compete with the prevalent oxygen evolution reaction (OER). In molecular catalysis, the Oxo-Wall dictates a transition from a stable oxo intermediate (OER active) to a meta-stable metal-oxo (OER inactive) generally occurs. In this work on heterogeneous catalysis, the same Oxo-Wall applies, however, a meta-stable oxo preferentially coordinates with lattice oxygen to form a more stable surface peroxo intermediate. A universal free energy onset of this process is identified at 3.39 eV under electrochemical activation in water and show that it is completely decoupled from the OER oxo species. Such decoupling gives rise to a new region of oxygen reactivity relevant for AOR where a selective oxidation of the unsaturated C-C bonds is predicted to occur instead of OER. A distinct AOR overpotential volcano is constructed and identify recently reported electrocatalysts, including palladium-platinum for propylene epoxidation and silver-nickel for ethylene epoxidation, along with others such as TiO 2 and CuO. Broader implications and limitations of electrochemical AOR are discussed, highlighting their potential to enable electrochemically enhanced thermal catalysis.
Systematic characterization of selenium speciation in coal fly ash
Millions of tons of coal fly ashes (CFAs) are produced annually during coal combustion in the U.S., which are commonly beneficially used in the concrete industry or disposed of in ash ponds. CFAs contain trace amounts of a range of toxic heavy metals including selenium (Se). Because the toxicity of Se is dependent on its speciation, investigating Se speciation in CFAs as affected by coal source and combustion conditions can help understand the related environmental and human health impacts during disposal or beneficial reuse. In this study, a set of representative CFA samples were characterized for Se speciation using synchrotron X-ray absorption spectroscopy (XAS) and micro-X-ray fluorescence spectromicroscopy (μ-XRF/XAS). Se-containing particles were highly heterogeneous, and individual particles might contain multiple oxidation states including Se(0), Se(IV), and Se(VI). Principal component analysis was performed for sample characteristics including Al 2 O 3 , SiO 2 , CaO, FeO, loss on ignition, average particle size, Se concentration, and Se oxidation state. Selective catalytic reduction (SCR), which is used to limit nitrogen oxide (NO x ) emissions during coal combustion, was found to be associated with the presence of reduced Se oxidation states, with up to 90% Se(0) observed in samples with SCR. Alongside SCR, FeO content may also influence Se speciation.
Selective phosphate removal with manganese oxide composite anion exchange membranes in membrane capacitive deionization
The discharge of excessive phosphorous into water bodies can lead to serious eutrophication threatening aquatic ecosystem. Membrane capacitive deionization (MCDI) is an effective platform for deionizing aqueous streams; however, conventional MCDI is unable to selectively remove targeted ions from a liquid mixture. Here, in this work, we fabricated manganese oxide composite anion exchange membranes (AEMs) for MCDI to enhance phosphate removal selectivity from sodium chloride-sodium dihydrogen phosphate (10:1 M ratio) aqueous mixtures. We systematically investigated several critical factors, such as constant current or voltage operation, applied voltage amount, process stream pH, and manganese oxide (Mn 2 O 3 ) content in the AEM, on phosphate removal efficiency and phosphate selectivity. A trade-off was observed between phosphate removal and selectivity when increasing the cell voltage. Under the best conditions, a MCDI unit with a 20 wt% Mn 2 O 3 composite AEM and a bipolar membrane facilitated high phosphate removal efficiency of ≥ 31.8 % and a phosphate over chloride selectivity of 1.1 while showing stability for at least 30 cycles. To help understand how Mn 2 O 3 composite AEM boosts phosphate selectivity, static electronic structure calculations were performed, and they revelated that hydrogen phosphate absorption on Mn 2 O 3 composite AEM was 314 kcal/mol more exothermic than that on pristine AEM while chloride adsorption on Mn 2 O 3 composite AEM was 2.2 kcal/mol less exothermic than that on a pristine AEM. Overall, this work presents an effective strategy for selectively removing phosphate from model wastewater solutions and the mechanistic understanding that governs ion selectivity in composite ion-exchange membranes used in MCDI.
High-throughput oxidation screening and down-selection of refractory high entropy alloys in the Al-Cr-Mo-Nb-Ta-Ti system
Rapid experimentation and characterization are ever-present needs in the discovery of high entropy alloys. High entropy alloy systems are difficult to survey with systematic composition sweeps using traditional synthesis methods. The number of distinct compositions in even a four-element system is experimentally intractable. Exploration of these, and higher-element systems, necessitates thermodynamic prediction coupled with an automated sample creation method and a rapid screening methodology to effectively down-select alloys with targeted properties. As a result, a high-throughput method for evaluating the oxidation performance of refractory high entropy alloys was developed and tested. The six-element system of aluminum, chromium, molybdenum, niobium, tantalum, and titanium was evaluated for single phase stability and short-duration oxidation resistance. Target compositions were initially determined via thermodynamic predictions of single-phase stability across a wide temperature range. A twenty-five-sample build plate was produced using directed energy deposition additive manufacturing. After fabrication, the twenty-five 1 cm 3 samples were heat treated and characterized for composition and phase identification. The build plate was exposed to a high temperature oxidizing environment at 1000 °C for three hours. After oxidation, the composition, morphology, and chemistry of the oxides formed were characterized. Of the twenty-five samples produced, nine exhibited a favorable oxidation response, from which a single-phase BCC alloy at a composition of Al 13 Cr 7 Mo 19 Nb 18 Ta 26 Ti 17 was identified as the alloy with the most protective oxidation coating with a thin, adherent oxide scale. Finally, the complete experimental down-selection—from machine setup to final alloy identification—required approximately 45 labor hours, demonstrating a rapid validation for alloy discovery.
The Role of Co-ZSM-5 Catalysts in Aerobic Oxidation of Ethylbenzene
Various forms of cobalt catalysts have been studied extensively for selective aerobic oxidation of hydrocarbons. However, it remains unclear whether cobalt can directly activate molecular oxygen under mild reaction conditions. Here we investigated the catalytic roles of cobalt in ethylbenzene oxidation with and without a hydroperoxide initiator. The contribution of different cobalt species was studied by varying the metal loading on Co-impregnated ZSM-5 samples. Quantitative EPR was used to determine the impact of cobalt catalysts on the free radical concentrations. Furthermore, this work provided strong evidence that cobalt, in several different forms, catalyzes hydrocarbon oxidation by facilitating peroxy bond cleavage, instead of direct oxygen activation.
Spinel Cu–Mn–Cr Oxide Nanoparticle-Pigmented Solar Selective Coatings Maintaining >94% Efficiency at 750 °C
High-temperature concentrating solar power (CSP) system is capable of harvesting and storing solar energy as heat towards cost-effective dispatchable solar electricity. Solar selective coating is a critical component to boost its efficiency by maximizing solar absorptance and minimizing thermal emittance losses. However, maintaining a high solar-thermal conversion efficiency >90% for long-term operation at ≥750 ºC remains a significant challenge. Herein, we report spray-coated spinel Cu-Mn-Cr oxide nanoparticle-pigmented solar selective coatings on Inconel tube sections maintaining ≥94% efficiency at 750 ºC and ≥92.5% at 800 ºC under 1000x solar concentration after 60 simulated day-night thermal cycles in air, each cycle comprising 12h at 750 ºC/800 ºC and 12h cooling to 25 ºC. The solar spectral selectivity is intrinsic to the band-to-band and d-d transitions of non-stoichiometric spinel Cu-Mn-Cr oxide nanoparticles. Furthermore, this feature offers a large fabrication tolerance in nanoparticle volume fraction and coating thickness, facilitating low-cost and scalable spray-coated high-efficiency solar selective absorbers for high-temperature CSP systems.
Functional Data Analysis in Wearable Body Sensor Networks
Improving response time of indirect room-size calorimeters is still an outstanding problem in metabolic research. Accurate estimates of instantaneous rates of gaseous exchange require numerical differentiation of measured gaseousgas concentrations. We propose a new method to estimate the instantaneous gaseousgas exchange rates in indirect calorimetry. In contrast to the previously developed techniques, the method addresses the problem of differentiation of gaseous concentrations as an ill-posed problem. By applying the method of regularization, the problem of differentiation is converted into a well-posed problem resulting in smooth and consistent gaseous exchange rates. The validity of the method is tested on a large dataset of calorimeter experiments which included 313 human experiments along with 231 alcohol combustion experiments. It is demonstrated that the method is able to reliably differentiate between the “unphysiological” process of alcohol combustion and physiological variations produced by human metabolism. The method also allowed unraveling the previously unreported relative kinetics of O2 consumption and Respiratory Quotient (RQ) in humans. It was found that the kinetics of oxidative fuel selection lags behind the energy expenditure in humans exhibiting some sort of oxidative inertia. The time lag varies from 2-3 min up to 30 min, depending on particular individual. No such lag was found in alcohol combustion experiments. In addition to the relative kinetics of substrate oxidation, two statistical indexes reflecting variability of minute-by-minute RQ were estimated. The indexes were the RQ’s standard deviation and RQ’s first-order derivative. Both indexes showed statistically significant difference between human experiments and alcohol combustion experiments. We conclude that the proposed method can consistently extract physiologically-relevant information from noisy calorimetry data and the aforesaid information can provide additional insights into the mechanism of metabolic fuel selection in humans.
Functional Data Analysis in Wearable Body Sensor Networks
Improving response time of indirect room-size calorimeters is still an outstanding problem in metabolic research. Accurate estimates of instantaneous rates of gaseous exchange require numerical differentiation of measured gaseousgas concentrations. We propose a new method to estimate the instantaneous gaseousgas exchange rates in indirect calorimetry. In contrast to the previously developed techniques, the method addresses the problem of differentiation of gaseous concentrations as an ill-posed problem. By applying the method of regularization, the problem of differentiation is converted into a well-posed problem resulting in smooth and consistent gaseous exchange rates. The validity of the method is tested on a large dataset of calorimeter experiments which included 313 human experiments along with 231 alcohol combustion experiments. It is demonstrated that the method is able to reliably differentiate between the “unphysiological” process of alcohol combustion and physiological variations produced by human metabolism. The method also allowed unraveling the previously unreported relative kinetics of O2 consumption and Respiratory Quotient (RQ) in humans. It was found that the kinetics of oxidative fuel selection lags behind the energy expenditure in humans exhibiting some sort of oxidative inertia. The time lag varies from 2-3 min up to 30 min, depending on particular individual. No such lag was found in alcohol combustion experiments. In addition to the relative kinetics of substrate oxidation, two statistical indexes reflecting variability of minute-by-minute RQ were estimated. The indexes were the RQ’s standard deviation and RQ’s first-order derivative. Both indexes showed statistically significant difference between human experiments and alcohol combustion experiments. We conclude that the proposed method can consistently extract physiologically-relevant information from noisy calorimetry data and the aforesaid information can provide additional insights into the mechanism of metabolic fuel selection in humans.
In-situ localized growth of porous metal oxide films on microheater platform for low temperature gas detection
A solid-state, low power microheater sensor platform that is configurable with selected metal oxide films for particular gas sensing applications is described. The sensor platform is configured by selecting a chemiresistive or catalytic material that is suitable for detecting a desired gas and then forming a porous nanostructured film on the designated surfaces of the microheater platform. Also described are methods for creating a highly porous, nanostructured metal oxide film in a controlled location from a liquid precursor using a localized heat source. By fast annealing deposited liquid precursors with the microheater, a highly porous, nanocrystalline metal oxide film can be generated in-situ and locally on the sensor platform.
Acidity of surface-infiltrated binary oxides as a sensitive descriptor of oxygen exchange kinetics in mixed conducting oxides
Improving the kinetics of O 2 reduction on oxide surfaces is critical in many energy and fuel conversion technologies. Here we show that the acidity scale for binary oxides is a powerful descriptor for tuning and predicting oxygen surface exchange kinetics on mixed conducting oxides. By infiltrating a selection of binary oxides from strongly basic (Li 2 O) to strongly acidic (SiO 2 ) onto the surface of Pr 0.1 Ce 0.9 O 2-δ samples, it was possible to vary the chemical surface exchange coefficient k chem by 6 orders of magnitude, with basic oxides such as Li 2 O increasing k chem by nearly 1,000 times, with surface concentrations as low as 50 ppm impacting k chem . Strikingly, although the pre-exponential of k chem scales linearly with the acidity of the infiltrated binary oxide, there is nearly no change in the activation energy. Here, the origin of these dramatic changes is proposed to arise from the systematic increase in electron concentration at the Pr 0.1 Ce 0.9 O 2-δ surface with the decreasing acidity of the infiltrated binary oxide.
Controlling Exsolution Dynamics in High‐Entropy Oxides for Highly Active and Selective Acetylene Semi‐Hydrogenation
Exsolution-derived catalysts feature robust metal–support interactions that enhance catalytic performance; yet achieving precise control over exsolution dynamics in multicomponent oxides remains challenging. In this study, we demonstrate that exsolution behavior in high-entropy oxides (HEOs) can be rationally tuned through coupled lattice- and valence-engineering to create a highly active and selective catalyst for acetylene semi-hydrogenation. Incorporation of Li + into a rock salt-structured HEO (LiNiMgCuZnCoO x and LiHEO) induces local lattice distortion, generates oxygen vacancies, and partially oxidizes Co sites from Co 2+ to Co 3+ , collectively modulating local charge redistribution. This strategy enables facilitated Cu nanoparticle exsolution and alters the exsolution sequence from Cu 0 > Ni 0 > Co 0 in pristine HEO to Cu 0 > Co 0 > Ni 0 in the LiHEO. The resulting catalyst via controlled exsolution exhibits superior activity and ethylene selectivity, outperforming state-of-the-art transition metal systems. This work establishes entropy-enabled lattice and valence engineering as a facile route to programmable exsolution for enhanced catalysis.
High-efficiency, air-stable manganese–iron oxide nanoparticle-pigmented solar selective absorber coatings toward concentrating solar power systems operating at 750 °C
Solar selective absorber coating with long-term thermal stability at high temperatures ≥750 °C in air is an important component to reduce the levelized cost of energy (LCOE) of concentrating solar power (CSP) systems toward 50% power efficiency and dispatchable solar electricity. Conventionally, solar spectral selectivity requires multilayer-interference coatings implemented by stringently controlled vacuum deposition, and these coatings degrade significantly at >700 °C in air. Herein, we established a quantitative design approach and demonstrated a proof-of-concept air-stable, manganese–iron oxide nanoparticle (NP)-pigmented solar selective coatings with a high solar absorptance of ~93%, a relatively low thermal emittance of ~52%, and an optical-to-thermal energy conversion efficiency >89% under 1,000× solar concentration at 750 °C toward Generation 3 CSP systems. These coatings demonstrate spectral selectivity using cost-effective spray coating approach, a notable improvement over conventional vacuum-deposited, multilayer solar selective coatings for low-cost, high-efficiency solar thermal receivers. In contrast to the thermal degradation of spectrally non-selective benchmark Pyromark 2500 coatings at 750 °C in air, the solar absorptance of the MnFe 2 O 4 -pigmented coatings on stainless steel 310 (SS 310) substrates is increased to ~92.9% and the optical-to-thermal energy conversion efficiency is improved to 89.7% after serving at 750 °C in air for 700 h. X-ray diffraction results reveal that this improvement is due to the transformation of MnFe 2 O 4 NPs into more thermodynamically stable, non-stoichiometric manganese-rich manganese ferrite and iron-rich manganese–iron oxide phases after 500 h aging at 750 °C. For >1,000 h-endurance testing at 750 °C in air and the subsequent 19 day-night thermal cycling between 750 °C (12 h/cycle) and 25 °C (12 h/cycle) on SS 310 substrates, the thermal degradation is mainly due to the CrO x microflake formation from SS 310 substrates rather than the coatings, which can be suppressed by preoxidizing the surface of SS 310. With lower emittance matrix material and further optimization of pigment NP stoichiometry, concentration, and coating thickness, it is promising to achieve an optimized thermal efficiency ≥92.5% with long-term thermal stability at 750 °C for Generation 3 CSP systems.
Highly Selective Carbon-Supported Boron for Oxidative Dehydrogenation of Propane
Bulk boron materials, such as hexagonal boron nitride (h-BN), are highly selective catalysts for the oxidative dehydrogenation of propane (ODHP). Previous attempts to improve the productivity of these systems involved the immobilization of boron on silica and resulted in less selective catalysts. Here, we report that acid-treated, activated carbon-supported boron prepared via incipient wetness impregnation with boric acid (B/OAC) exhibits equal propylene selectivity and improved productivity (kg propylene kg cat -1 hr -1 ) as compared to h-BN. Characterization of the fresh and spent catalysts with infrared, Raman, X-ray photoelectron, and solid-state NMR spectroscopies reveals the presence of oxidized/hydrolyzed boron that is clustered on the surface of the support.