Stabilization of a nanoporous NiCu dilute alloy catalyst for non-oxidative ethanol dehydrogenation
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99 GENERAL AND MISCELLANEOUS↗
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Carbon dioxide and nitrogen reduction reactions using 2D transition metal dichalcogenide (TMDC) and carbide/nitride (MXene) catalysts
Improving the carbon dioxide and nitrogen reduction reactions (CO 2 RR and NRR) can reduce anthropogenic greenhouse gas emissions while selectively producing chemicals needed for the fuel, plastic, and chemical industries. Efficient CO 2 RR can be used to replace fossil fuels as well as repurpose captured CO 2 , while new NRR pathways can be used to supplement or replace the energy intensive Haber–Bosch process for NH 3 generation with no CO 2 emissions. Therefore, this review article focuses on (photo)electrocatalytic and photocatalytic conversion of CO 2 and N 2 molecules into useful products, such as carbon monoxide, methanol, formic acid, and ammonia, using 2D transition metal dichalcogenides (TMDCs) and metal carbides/nitrides (MXenes). Additionally, these highly tunable 2D catalysts will be evaluated for their ability to selectively and efficiently undergo CO 2 RR and NRR by controlling defects, phases, edge sites, interfaces, and functional groups. We first address the CO 2 RR and NRR challenges, with a particular focus on theoretical mechanisms and minimum energy pathways. We follow this discussion with a detailed review of state-of-the-art 2D TMDC and MXene experimental catalysts for CO 2 RR and NRR (photo)electrocatalytic and photocatalytic reactions, and then address areas of opportunity for these catalytic reactions.
14 SOLAR ENERGY↗
Rxn Rover: automation of chemical reactions with user-friendly, modular software
Automation of chemical reactions through tools such as Rxn Rover in research and development is an enabling technology to reduce cost and waste management in technology transformations towards renewable feedstocks and energy in the chemical industry.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
ZeoNet: 3D convolutional neural networks for predicting adsorption in nanoporous zeolites
Zeolites are one of the most widely used materials in the chemical industry due to their nanometer-sized pores that can adsorb and react upon molecules selectively. With hundreds of known framework topologies and hundreds of thousands of computationally predicted structures, the ability to rapidly predict zeolite performance allows researchers to prioritize their efforts on the most promising structures for a given application. Although the accuracy of forcefield-based atomistic simulations has advanced significantly in the past two decades, these simulations can be computationally expensive, especially for long-chain, complex molecules. Here, we present ZeoNet, a representation learning framework using convolutional neural networks (ConvNets) and 3D volumetric representations for predicting adsorption in zeolites. ZeoNet was trained on the task of predicting Henry's constants for adsorption, k H , of n-octadecane in more than 330 000 known and predicted zeolite materials. Employing a 3D grid based on the distances to solvent-accessible surfaces, a volumetric representation that can be generated efficiently, the best-performing ZeoNet achieved a correlation coefficient r 2 = 0.977 and a mean-squared error MSE = 3.8 in ln k H , which corresponds to an error of 9.3 kJ mol -1 in adsorption free energy. In comparison, a model based on hand-designed geometric features has values of r 2 = 0.783 and MSE = 35.7. ZeoNet is also relatively efficient and can process ≈8 structures per second on an Nvidia RTX 2080TI GPU, orders of magnitude faster than forcefield-based simulations. A systematic analysis was conducted to investigate how the choice of ConvNet architectures, the linear dimension (L) and spatial resolution (Δd) of the distance grids, batch size, optimizer, and learning rate impact the model performance. We found that ConvNets based on the ResNet architecture offer the best tradeoff between expressiveness and efficiency. The performance for all models reaches a plateau at L = 30–45 Å and depends less sensitively on grid resolution, with a small benefit around Δd = 0.30–0.45 Å. Finally, saliency maps were visualized to identify which regions of the materials contributed the most to model predictions. It was found, interestingly, that the predictions are driven primarily by the accessible pore volume rather than the region occupied by the framework atoms.
36 MATERIALS SCIENCE↗
Metal–support interactions in metal oxide-supported atomic, cluster, and nanoparticle catalysis
Supported metal catalysts are essential to a plethora of processes in the chemical industry. The overall performance of these catalysts depends strongly on the interaction of adsorbates at the atomic level, which can be manipulated and controlled by the different constituents of the active material (i.e., support and active metal). The description of catalyst activity and the relationship between active constituent and the support, or metal–support interactions (MSI), in heterogeneous (thermo)catalysts is a complex phenomenon with multivariate (dependent and independent) contributions that are difficult to disentangle, both experimentally and theoretically. So-called “strong metal–support interactions” have been reported for several decades and summarized in excellent review articles. However, in recent years, there has been a proliferation of new findings related to atomically dispersed metal sites, metal oxide defects, and, for example, the generation and evolution of MSI under reaction conditions, which has led to the designation of (sub)classifications of MSI deserving to be critically and systematically evaluated. These include dynamic restructuring under alternating redox and reaction conditions, adsorbate-induced MSI, and evidence of strong interactions in oxide-supported metal oxide catalysts. Here, we review recent literature on MSI in oxide-supported metal particles to provide an up-to-date understanding of the underlying physicochemical principles that dominate the observed effects in supported metal atomic, cluster, and nanoparticle catalysts. Critical evaluation of different subclassifications of MSI is provided, along with discussions on the formation mechanisms, theoretical and characterization advances, and tuning strategies to manipulate catalytic reaction performance. We also provide a perspective on the future of the field, and we discuss the analysis of different MSI effects on catalysis quantitatively.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
From lignin to market: a technical and economic perspective of reductive depolymerization approaches
Lignin has grown into one of the main candidates to replace fossil-based resources as it is the largest renewable source of aromatic building blocks. The complex structure of polymeric lignin, however, requires depolymerization to simpler building blocks for the chemical industry. One of the most promising depolymerization approaches is reductive depolymerization of which two process configurations are currently studied in pilot scale installations for upscaling to industrial scale: (i) reductive catalytic fractionation (RCF), and (ii) reductive catalytic depolymerization (RCD). Both technical and techno-economic aspects will be covered within this review, discussing the advantages and challenges of both approaches regarding processing, production costs, product output, and applications. In this regard, RCF benefits from its decreased energy and solvent consumption linked with being a one-step process and delivers a product with a high monomer content (∼25–45 wt%). RCD, on the other hand, has the advantage of continuous processing and reduced catalyst fouling and delivers a product that mainly consists of oligomers (<10 wt% monomers). The complete overview of both processes presented here addresses their potential, and can guide future researchers, policy makers and companies to make thoughtful decisions on lignin valorization.
09 BIOMASS FUELS↗
Catalytic properties of model supported nanoparticles
Nanoparticles of late transition metals are used as catalysts and electrocatalysts for industrial chemical reactions that produce fuels, convert chemical energy to electricity and clean up pollution associated with the generation and use of these fuels. For such applications, metals are usually bonded onto the surfaces of oxide or carbon support materials. To provide the energy needed for sustained economic development, we must develop new and improved solid catalysts and electrocatalysts for a variety of reactions that take better advantage of traditional and alternate energy sources (solar, wind, biomass, etc.) and avoid serious environmental problems. Additionally, since these metals are often expensive and in limited supply (e.g., Pt), we also need to develop catalysts that do not require precious metals. As a consequence, recent years have seen a dramatic increase in research aimed at providing the basic understanding needed to develop new and improved catalysts and electrocatalysts for a variety of reactions that involve nanoparticles of late transition metals supported on oxide or carbon materials. It is therefore timely to present a special issue that focusses on Catalytic Properties of Model Supported Nanoparticles. The collection highlights some of the most exciting new directions for research in this area, and provides in depth examples of some of the most cutting-edge methods, both experimental and theoretical, for studying this class of catalysts, which provide insights into structure, mechanisms, kinetics and energetics with unprecedented accuracy and detail. Finally, as Guest Editors, we present below a few of the many excellent papers published in the collection.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
The role of sub-surface hydrogen on CO 2 reduction and dynamics on Ni(110): An ab initio molecular dynamics study
The catalytic reduction of carbon dioxide is a crucial step in many chemical industrial reactions, such as methanol synthesis, the reverse water-gas shift reaction (rWGS) and formic acid synthesis. Here, we investigate the role of bulk hydrogen, where hydrogen atoms are found deep inside a metal surface as opposed to subsurface ones, upon CO 2 reduction over a Ni(110) surface using density functional theory (DFT) and ab initio molecular dynamics (AIMD) simulations. While it has previously been shown that subsurface hydrogen stabilizes CO 2 and can aid in overcoming reaction barriers, the role of bulk hydrogen is less studied and thus unknown with regard to CO 2 reduction. Furthermore, we find that the presence of bulk hydrogen can significantly alter the electronic structure of the Ni(110) surface, particularly the work function and d-band center, such that CO 2 adsorbs more strongly to the surface and is more easily reduced. Our results show an enhanced CO 2 dissociation in the presence of bulk hydrogen, shedding light on a hitherto underappreciated mechanistic pathway for CO 2 reduction on metal surfaces.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Functional enzyme–polymer complexes
Significance The use of biological enzyme catalysts could have huge ramifications for chemical industries. However, these enzymes are often inactive in nonbiological conditions, such as high temperatures, present in industrial settings. Here, we show that the enzyme PETase (polyethylene terephthalate [PET]), with potential application in plastic recycling, is stabilized at elevated temperature through complexation with random copolymers. We demonstrate this through simulations and experiments on different types of substrates. Our simulations also provide strategies for designing more enzymatically active complexes by altering polymer composition and enzyme charge distribution.
59 BASIC BIOLOGICAL SCIENCES↗
Metallic and porous Ti nanorod arrays for visible-IR light absorption and dendrite-free stable lithium-metal batteries
It is still a challenging task to prepare highly porous nanorod arrays of metals formed on substrates for optical and energy storage applications. Herein, we demonstrate the design and synthesis of black color, metallic and highly porous Ti nanorod arrays as novel current collectors for dendrite-free and highly stable Li-metal anodes. The high porosity of metallic nanorod arrays provides numerous heterogeneous nucleation sites and huge contact area and large space for the accommodation of Li metal. The conductive metallic Ti nanorod arrays enhance electrode integration. Effectively, it eliminates formation Li dendrites and demonstrates superior cycling stability over 300 cycles. Additionally, the unique porous structures of the nanorod arrays can decrease the amplitude of forced vibration in narrow space leading to light absorption. Interestingly, the metal is black instead of metallic color. The black metallic nanorod arrays can absorb more than 96% of both visible and infra-red lights. Furthermore, this black color metallic porous nanorod arrays may find additional applications in aerospace, energy, biomedical, defence, and chemical industries.
25 ENERGY STORAGE↗
Foundations of machine learning for low-temperature plasmas: methods and case studies
Abstract Machine learning (ML) and artificial intelligence have proven to be an invaluable tool in tackling a vast array of scientific, engineering, and societal problems. The main drivers behind the recent proliferation of ML in practically all aspects of science and technology can be attributed to: (a) improved data acquisition and inexpensive data storage; (b) exponential growth in computing power; and (c) availability of open-source software and resources that have made the use of state-of-the-art ML algorithms widely accessible. The impact of ML on the field of low-temperature plasmas (LTPs) could be particularly significant in the emerging applications that involve plasma treatment of complex interfaces in areas ranging from the manufacture of microelectronics and processing of quantum materials, to the LTP-driven electrification of the chemical industry, and to medicine and biotechnology. This is primarily due to the complex and poorly-understood nature of the plasma-surface interactions in these applications that pose unique challenges to the modeling, diagnostics, and predictive control of LTPs. As the use of ML is becoming more prevalent, it is increasingly paramount for the LTP community to be able to critically analyze and assess the concepts and techniques behind data-driven approaches. To this end, the goal of this paper is to provide a tutorial overview of some of the widely-used ML methods that can be useful, amongst others, for discovering and correlating patterns in the data that may be otherwise impractical to decipher by human intuition alone, for learning multivariable nonlinear data-driven prediction models that are capable of describing the complex behavior of plasma interacting with interfaces, and for guiding the design of experiments to explore the parameter space of plasma-assisted processes in a systematic and resource-efficient manner. We illustrate the utility of various supervised, unsupervised and active learning methods using LTP datasets consisting of commonly-available, information-rich measurements (e.g. optical emission spectra, current–voltage characteristics, scanning electron microscope images, infrared surface temperature measurements, Fourier transform infrared spectra). All the ML demonstrations presented in this paper are carried out using open-source software; the datasets and codes are made publicly available. The FAIR guiding principles for scientific data management and stewardship can accelerate the adoption and development of ML in the LTP community.
Physics↗
Engineering Escherichia coli to produce medium chain oleochemicals from C 2 substrates
Many strategies to create a circular bioeconomy have been proposed. To be successful, CO 2 must be reduced with renewable energy into chemical building blocks, from which the chemical industry can be supported. Circular strategies include leveraging photosynthesis to produce sugar and lipid intermediates or renewable electricity to produce hydrogen or other electron carriers to support CO 2 reduction. Acetogens can anaerobically reduce CO 2 with H 2 to produce mixtures of small organic molecules in gas fermentations. We previously demonstrated that acetate, a common product of gas fermentation, can be converted to the model oleochemical dodecanol in engineered Escherichia coli. Here, we explored the conversion of ethanol and mixtures of ethanol and acetate to the same model oleochemicals. Co-feeding ethanol can supply both carbon and additional reducing power relative to acetate alone. In this work, we engineered E. coli to catabolize ethanol and expressed two distinct ethanol metabolism pathways in different operons and combined them with improved engineered acetate activation. We evaluated the performance of these operons in dodecanol-producing strains when fed ethanol or acetate and found ethanol to be a better carbon source when judged by product titers. The engineered strains fed ethanol produced about two-fold more dodecanol than the strains fed acetate. This increase was in part, due to change in product distribution. Cells fed ethanol produced predominantly dodecanol, whereas cells fed acetate generated a mixture of dodecanol and dodecanoic acid. Dodecanol titers were further improved by employing feeding strategies in controlled bioreactors.
Escherichia coli↗
A glimpse into the fungal metabolomic abyss: Novel network analysis reveals relationships between exogenous compounds and their outputs
Fungal specialized metabolites are a major source of beneficial compounds that are routinely isolated, characterized, and manufactured as pharmaceuticals, agrochemical agents, and industrial chemicals. The production of these metabolites is encoded by biosynthetic gene clusters that are often silent under standard growth conditions. There are limited resources for characterizing the direct link between abiotic stimuli and metabolite production. Herein, we introduce a network analysis-based, data-driven algorithm comprising two routes to characterize the production of specialized fungal metabolites triggered by different exogenous compounds: the direct route and the auxiliary route. Both routes elucidate the influence of treatments on the production of specialized metabolites from experimental data. The direct route determines known and putative metabolites induced by treatments and provides additional insight over traditional comparison methods. The auxiliary route is specific for discovering unknown analytes, and further identification can be curated through online bioinformatic resources. We validated our algorithm by applying chitooligosaccharides and lipids at two different temperatures to the fungal pathogen Aspergillus fumigatus. After liquid chromatography–mass spectrometry quantification of significantly produced analytes, we used network centrality measures to rank the treatments’ ability to elucidate these analytes and confirmed their identity through fragmentation patterns or in silico spiking with commercially available standards. Later, we examined the transcriptional regulation of these metabolites through real-time quantitative polymerase chain reaction. Our data-driven techniques can complement existing metabolomic network analysis by providing an approach to track the influence of any exogenous stimuli on metabolite production. Our experimental-based algorithm can overcome the bottlenecks in elucidating novel fungal compounds used in drug discovery.
59 BASIC BIOLOGICAL SCIENCES↗
Structural analysis of wild-type and Val120Thr mutant Candida boidinii formate dehydrogenase by X-ray crystallography
Candida boidinii NAD + -dependent formate dehydrogenase (CbFDH) has gained significant attention for its potential application in the production of biofuels and various industrial chemicals from inorganic carbon dioxide. The present study reports the atomic X-ray crystal structures of wild-type CbFDH at cryogenic and ambient temperatures, as well as that of the Val120Thr mutant at cryogenic temperature, determined at the Turkish Light Source ‘Turkish DeLight’. The structures reveal new hydrogen bonds between Thr120 and water molecules in the active site of the mutant CbFDH, suggesting increased stability of the active site and more efficient electron transfer during the reaction. Further experimental data is needed to test these hypotheses. Collectively, these findings provide invaluable insights into future protein-engineering efforts that could potentially enhance the efficiency and effectiveness of CbFDH.
59 BASIC BIOLOGICAL SCIENCES↗
ALLOY SELECTION AND C-276 CODE DESIGN VALUE EXTENSION FOR ADVANCED MOLTEN SALT TECHNOLOGY TEST FACILITIES EXPERIMENTATION
Molten halide salts are being considered as working fluids for nuclear and concentrated solar power applications. High temperature molten fluoride and chloride salts are known to preferentially attack and deplete Cr in alloys, which leads to the use of high-Ni lower-Cr alloys in test facilities for advanced Molten Salt Technology. Alloys 600 and C-276 are two commercially available Ni alloys that have moderate Cr contents and are qualified to the maximum temperature of 649°C and 677°C (1,200°F and 1,251°F), respectively, in the Boiler and Pressure Vessel Code. Both alloys have good corrosion resistance to acids, are resistant to stress-corrosion cracking, and have long track records of use in the chemical industry. Comparatively, Alloy C-276 has a similar Cr content but much higher allowable stresses for temperatures greater than 600°C (1,112°F). Therefore, it has been considered as a structural material for test facilities that require operations at 700°C (1,292°F) or greater to develop high-temperature Molten Salt Technology. To meet the requirements, the current code design values were extended based on some experimental data. Analysis showed that above current Codified maximum temperature, strength of the alloy is mainly controlled by creep rupture life under the average stress, although the Sc creep rate criterion is close to the Favg.Savg rupture criterion. This paper presents the intended test facilities and the design requirements, alloy selection considerations, literature review, data analysis, and proposed allowable stress extension based on some creep test data for C-276 at temperatures greater than 677°C (1,251°F). Further research activities are also briefly mentioned.
Ren, Weiju↗
Developmental changes in lignin composition are driven by both monolignol supply and laccase specificity
The factors controlling lignin composition remain unclear. Catechyl (C)–lignin is a homopolymer of caffeyl alcohol with unique properties as a biomaterial and precursor of industrial chemicals. The lignin synthesized in the seed coat of Cleome hassleriana switches from guaiacyl (G)– to C-lignin at around 12 to 14 days after pollination (DAP), associated with a rerouting of the monolignol pathway. Lack of synthesis of caffeyl alcohol limits C-lignin formation before around 12 DAP, but coniferyl alcohol is still synthesized and highly accumulated after 14 DAP. We propose a model in which, during C-lignin biosynthesis, caffeyl alcohol noncompetitively inhibits oxidation of coniferyl alcohol by cell wall laccases, a process that might limit movement of coniferyl alcohol to the apoplast. Developmental changes in both substrate availability and laccase specificity together account for the metabolic fates of G- and C-monolignols in the Cleome seed coat.
09 BIOMASS FUELS↗
Countering the future chemical weapons threat
After decades of difficult negotiations, the Chemical Weapons Convention (CWC) was adopted in 1993 and entered into force on 29 April 1997, banning the development, production, stockpiling, transfer, and use of chemical weapons (CW). As the CWC celebrates the 25th anniversary of its entry into force, it can document considerable success, much of it attributed to the CWC implementing body—the Organisation for the Prohibition of Chemical Weapons (OPCW). However, facing a volatile international security environment and an everchanging chemical industry, the OPCW must transform to meet its mission and remain an exemplar for multilateralism. As the next CWC review conference approaches in 2023, a next-generation OPCW 2.0 can be effective and credible only if it reinforces international norms against CW, anticipates future challenges posed by advancements in science and technology (S&T), incorporates more qualitative elements into the verification and compliance system, and keeps pace with technological change.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Xylanolytic metabolism is regulated by coordination of transcription factors XynR and XylR in extremely thermophilic Caldicellulosiruptorales
ABSTRACT Global transcription factors (TFs) control metabolic processes in bacteria to efficiently utilize available carbon. The orderCaldicellulosiruptoraleshas drawn interest due to the ability of its members to degrade components of lignocellulosic biomass. Regulatory reconstruction ofAnaerocellum (f. Caldicellulosiruptor) besciiidentified two major global transcription factors for xylan utilization, XynR and XylR, and the corresponding putative transcription factor binding sites. Recombinant versions of XynR (LacI family) and XylR (ROK family) were subjected to fluorescence polarization (FP) and biolayer interferometry (BLI) analysis to confirm the predicted binding sites. Four XynR sites and two XylR sites were validated, accounting for 20 of 26 genes regulated by XynR and six of seven genes regulated by XylR. Bioinformatic analysis of the individual genes controlled by the two regulators showed an inter-dependent scheme for xylan conversion; the transport of xylooligosaccharides (XOS) is dependent on XylR, while enzymes responsible for hydrolysis are controlled by both regulators. For xylose catabolism by the xylose isomerase-xylulose kinase pathway, regulation is also split, with XylR controlling xylose isomerase and XynR controlling xylokinase. The XynR/XylR regulator pair withinA. besciiis conserved in all sequenced species ofCaldicellulosiruptorales, suggesting similarities in regulating linear xylan conversion. In other xylanolytic thermophiles, XylR homologs control xylan degradation, compared to just 6 out of 26 genes forA. bescii. These results show that two separate regulatory schemes (dual repression) are coordinated byA. besciito effectively regulate the hemicellulose inventory and xylan catabolism. IMPORTANCE To take full advantage of extreme thermophiles as platform metabolic engineering microorganisms, the tools for genetic manipulation must be further developed, and strategies that exploit a better understanding of metabolic regulation need to be discerned.Anaerocellum bescii, the most studied of the extremely thermophilic fermentative anaerobic bacteria that can utilize microcrystalline cellulose, can degrade microcrystalline cellulose and hemicellulose and has been metabolically engineered to convert the resulting sugars to products such as ethanol and acetone. For xylan, in particular, two major global transcription factors (TFs), XynR and XylR, play a role in sugar metabolism, although their predicted regulatory interdependence from bioinformatics analysis has not been elucidated experimentally. Here, fluorescence polarization (FP) and biolayer interferometry (BLI) were used to explore this issue to support metabolic engineering efforts aimed at improving carbohydrate processing to industrial chemicals.
Biotechnology & Applied Microbiology↗