The comparative enzymology of lactic dehydro- genases. i- properties of the crystalline beef and chicken enzymes
Isolation, crystallization, and physicochemical properties of beef and chicken enzymes - lactic dehydrogenases
ENZYME↗
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Characteristics of graphene oxide-like materials prepared from different deashed-devolatilized coal chars and comparison with graphite-based graphene oxide, with or without the ultrasonication treatment
Deashed-devolatilized coal chars were prepared from anthracite, bituminous, subbituminous, and lignite coals, then oxidized to prepare graphene oxide (GO)-like materials that were extensively characterized and compared with a pristine graphite-based GO sample (CGO). Coal-based GO-like materials and CGO were then subjected to ultrasonication treatment for delamination, which also impacted their physicochemical properties. The surface oxygen contents of CGO and coal-based samples were ~32 % and ~26–35 %, respectively, that were reduced to ~27 % and ~21–24 %, respectively, after ultrasonication. The Fourier transform infrared and X-ray photoelectron spectroscopy spectra of all samples showed different profiles before and after the ultrasonication, suggesting a change in chemical functionalities. The Raman spectra of CGO and coal-based samples exhibited similar D and G bands with D/G intensity ratios (I D /I G ) of 0.93 and 0.88–0.92, respectively. The I D /I G ratio of CGO and most of the coal-based samples increased after the ultrasonication due to creation of more defects. The main X-ray diffraction (XRD) peak for CGO and coal-based samples were observed at ~11° and ~22–24°, respectively. Ultrasonication of the samples resulted in a reduction of crystallite size and number of layers for all CGO and coal-based samples, confirming both splitting and delamination of carbon layers. Additionally, different coal-based samples exhibited almost overlapping XRD profiles after the ultrasonication.
36 MATERIALS SCIENCE↗
A critical review on additive manufacturing of refractory alloys from a data analytics perspective- beyond nickel-based superalloys
Refractory alloys (RAs) are promising materials due to their exceptional physicochemical properties, but most research remains at the laboratory scale. For broader adoption, advancements in manufacturing are essential. Because their high stability makes conventional methods like machining and casting difficult, additive manufacturing (AM) is emerging as an effective approach for fabricating refractory alloy components. However, AM's repeated non-equilibrium thermal cycles introduce undesired features (e.g. defects, anisotropic microstructures, and residual stresses), which are magnified due to RAs’ unique properties. This paper comprehensively reviews the state-of-the-art methods of AM for refractory alloys. It explores data analytics techniques to establish design rules based on multi-fidelity experimental and computational methods. Furthermore, it investigates integrated, collaborative efforts to harmonise standalone databases, information, knowledge, and predictive models at multi-physics, multi-stage, and multi-scale. Unlike the existing literature that focuses primarily on material systems or process fundamentals, this work provides an integrated perspective on AM of refractory alloys from a data analytics standpoint, highlighting the roles of integrated computational materials engineering (ICME), verification, validation, and uncertainty quantification (VV&UQ), and digital twin-driven qualification in overcoming data scarcity and accelerating rapid qualification.
Additive manufacturing↗
Identification and quantification of lignin monomers and oligomers from reductive catalytic fractionation of pine wood with GC × GC – FID/MS
Thorough lignin characterization is vital to understand the physicochemical properties of lignin and to evaluate lignocellulose biorefinery processes. In this study, an in-depth characterization of lignin oil, obtained from reductive catalytic fractionation (RCF) of pine wood, was performed with quantitative GC × GC – FID analysis and qualitative GC × GC – MS. By utilizing high-temperature resistant column sets in the GC × GC system and by applying a derivatization step, unambiguous detection of lignin monomers, dimers, and trimers is enabled. In addition to confirm the identity of eleven monomers, corresponding to 34 wt% of the RCF lignin oil, thirty-six dimers (16 wt%) and twenty-one trimers (7 wt%) were comprehensively identified by analysis of their mass spectra and quantified by a FID, encompassing the identity of an additional 23 wt% of the RCF lignin oil. The proposed structures reveal the interlinkages present in the dimeric and trimeric oligomers, containing β-5, β-1, β–β, 5–5, and a minor fraction of β-O-4 and 4-O-5 bonds. Furthermore, aliphatic end-units in the dimeric and trimeric molecules were identified, consisting of various substituents at the C4 position, that have been previously observed in the RCF-derived lignin monomers. To reduce complexity for analysis, the RCF oil was separated into six fractions, prior to analysis. The structural motifs (inter-unit linkages and end-units) that are found in the different fractions vary significantly, such that the lignin fractions extracted in more polar solvents contained higher molecular weight fragments and more hydroxyl containing structural motifs. Additionally, the identified structures of individual dimer and trimer molecules by GC × GC align well with and further complement the recent findings from 1 H– 13 C HSQC NMR spectroscopy, demonstrating complementarity between both 2D techniques to obtain a holistic view on both the molecular structures and the distribution of bonds and end-units in RCF oil. The combination of these two techniques provides a powerful tool for future RCF and other lignin depolymerization research.
09 BIOMASS FUELS↗
A Novel Sustainable Process for Multilayer Graphene Synthesis Using CO 2 from Ambient Air
Graphene produced by different methods can present varying physicochemical properties and quality, resulting in a wide range of applications. The implementation of a novel method to synthesize graphene requires characterizations to determine the relevant physicochemical and functional properties for its tailored application. We present a novel method for multilayer graphene synthesis using atmospheric carbon dioxide with characterization. Synthesis begins with carbon dioxide sequestered from air by monoethanolamine dissolution and released into an enclosed vessel. Magnesium is ignited in the presence of the concentrated carbon dioxide, resulting in the formation of graphene flakes. These flakes are separated and enhanced by washing with hydrochloric acid and exfoliation by ammonium sulfate, which is then cycled through a tumble blender and filtrated. Raman spectroscopic characterization, FTIR spectroscopic characterization, XPS spectroscopic characterization, SEM imaging, and TEM imaging indicated that the graphene has fifteen layers with some remnant oxygen-possessing and nitrogen-possessing functional groups. The multilayer graphene flake possessed particle sizes ranging from 2 µm to 80 µm in diameter. BET analysis measured the surface area of the multilayer graphene particles as 330 m 2 /g, and the pore size distribution indicated about 51% of the pores as having diameters from 0.8 nm to 5 nm. This study demonstrates a novel and scalable method to synthesize multilayer graphene using CO 2 from ambient air at 1 g/kWh electricity, potentially allowing for multilayer graphene production by the ton. The approach creates opportunities to synthesize multilayer graphene particles with defined properties through a careful control of the synthesis parameters for tailored applications.
36 MATERIALS SCIENCE↗
Amphiphilic components of the Murchison carbonaceous chondrite - Surface properties and membrane formation
The possibility that the amphiphilic compounds in carbonaceous meteorites whose physicochemical properties are presently studied may represent sources of lipidlike compounds which could have evolved as membrane components in primitive cells is investigated in samples of the Murchison CM2 chondrite. Surface properties and membrane formation are obtained for three fractions isolated by two-dimensional thin-layer chromatography. It is concluded that a small, undefined fraction of Murchison components exhibits amphiphilic properties which allow assembly into boundary membranes.
Deamer, D. W.↗
Synthesis, structure and canted antiferromagnetism of layered cobalt hydroxide sorbate
Layered hybrid organic–inorganic compounds of the transition metal hydroxides, in which part of the hydroxide ions are exchanged for organic ligands that act as spacers of the 2-dimensional inorganic layers, elicit new or enhanced properties. The interlayer ligands enable fine-tuning of the physicochemical properties of the metal hydroxide layers, including their magnetism. Specifically, within the Co(OH) 2 family of transition ion hydroxides, this approach has been leveraged to stabilize antiferromagnetic and ferrimagnetic ground states for pillared and layered complexes, respectively. Here, in this work, we further investigate the effects of changing dimensionality, local Co coordination, and interlayer spacing on the magnetic ground state of brucite-like β-Co(OH) layered complexes. We report the hydrothermal synthesis of a new crystalline material, Co(OH)(sorb), where the inorganic layers are spaced by monotopic, doubly unsaturated sorbate ligands (sorb = [C 6 H 7 O 2 ] – ). The material was structurally characterized using powder X-ray and neutron diffraction measurements and the crystal structure was solved ab initio from powder diffraction data and refined using the Rietveld method. The magnetic susceptibility and magnetization measurements reveal canted antiferromagnetic ordering with T N = 41.7 K. This report augments our understanding of tuning magnetism through dimensionality in layered complexes and represents a step forward in the design of new 2D layered hybrid compounds.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Quantum Chemistry-Driven Machine Learning Approach for the Prediction of the Surface Tension and Speed of Sound in Ionic Liquids
Ionic liquids (ILs) have unique solvent properties and have thus garnered significant interest. However, exhaustive experimental determination of the physicochemical properties of ILs is unrealistic due to the large structural diversity of anions and cations, their high cost, the requirements of elevated temperature and pressure, and the time required. To circumvent these experimental costs, computational approaches to accurately calculate these properties have emerged. Here in the present study, we present a demonstration of two machine learning (ML) models for the prediction of two critical IL physical properties, the surface tension and the speed of sound, across a wide range of temperatures and pressures. The models make use of molecular descriptors derived from the COSMO-RS, a quantum chemical-based model. The ML models show excellent agreement with experimental observations, with an R2 value of 0.96–0.99 and RMSE of 1.71 mN/m and 16.12 m/s for the surface tension and speed of sound, respectively. This work paves the way for the development of COSMO-RS-informed ML models for the prediction of IL properties which can help to further optimize and accelerate technology development for ILs.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Investigating the Effect of Water on the Mechanical Properties of Cellulose from Multiscale Molecular Dynamics Simulations
Classical molecular dynamics (MD) simulations provide insight into the structure and physicochemical properties of materials with atomic resolution. However, the length and time scales accessible to atomistic MD are orders of magnitude smaller than many relevant processes such as the response of a bulk material to experimentally accessible strain rates, which presents challenges when comparing models to experimental measurements. Bottom-up coarse-graining provides a means for systematically mapping atomistic information to lower resolution models to increase the length and time scales achievable by simulation. Cellulose is an abundant carbohydrate biopolymer with applications to many fields of research, such as materials science and renewable energy, due to its desirable mechanical properties and viability for conversion into biofuel. The effect of moisture content on the Young's modulus of cellulose is of special interest due to its native environment often being in the hydrated secondary plant cell wall and the grinding energy requirements for biomass feedstock preprocessing. The current work investigates the effects of water solvent on the Young's modulus of cellulose calculated from coarse-grained MD mechanical stress simulations. The coarse-grained model was parametrized from atomistic MD calculations of cellulose-cellulose potentials of mean force using umbrella sampling techniques under vacuum and solvated conditions. The Young's moduli of the coarse-grained cellulose assemblies parametrized from cellulose in vacuum or solvated in water were computed via mechanical stress simulations to highlight the importance of capturing solvent interactions for modeling the mechanical behavior of cellulose.
BASIC BIOLOGICAL SCIENCES,RADIATION PROTECTION AND↗
Investigation of the effects of ternary deep eutectic solvent composition on pretreatment of sorghum stover
Biomass‐derived deep eutectic solvents (DESs) have been introduced as promising pretreatment and fractionation solvents because of their mild processing conditions, easy synthesis, and green solvent components from biomass. In recent DES studies, solvent‐based third constituents like water, ethanol, and others improve the processibility of typical binary DESs. However, the impacts of these components are not well understood. Here, two solvent‐based constituents, including water and ethylene glycol, were applied to 3,4‐dihydroxybenzoic acid (DHBA)‐based DES system for improving the conversion efficiency of cellulose‐rich fraction and the properties of lignin fraction. Chemical composition, enzymatic digestibility, degree of polymerization of cellulose and physicochemical properties of lignin were used to evaluate the impact of each third constituent on biomass processing. Ternary ChCl‐DHBA DESs exhibited better performances in delignification, fermentable sugar production, and preservation of β‐O‐4 ether linkage in lignin compared with binary ChCl‐DHBA DES.
09 BIOMASS FUELS↗
Probing degradation at solid-state battery interfaces using machine-learning interatomic potential
Solid-state batteries featuring fast ion-conducting solid electrolytes are promising next-generation energy storage technologies, yet challenges remain for practical deployment due to electro-chemo-mechanical instabilities at solid-solid interfaces. These interfaces, which include homogeneous/internal interfaces such as grain boundaries (GBs) and heterogeneous/external interfaces between solid-electrolyte and electrode materials, can impede Li-ion transport, deteriorate performance, and eventually lead to cell failure. Here, in this study, we leverage large-scale molecular simulations, enabled by validated machine-learning interatomic potentials, to directly probe the onset of interfacial degradation at the garnet Li 7 La 3 Zr 2 O 12 (LLZO) solid-electrolyte/LiCoO 2 (LCO) cathode interface. By surveying different interfacial geometries and compositions, it is found that Li-deficient interfaces can lead to severe interfacial disordering with cation mixing and Co interdiffusion from LCO into LLZO. By contrast, Li-sufficient interfaces are less disordered, although elemental segregation with local ordering is observed. As a consequence of Co interdiffusion, Co-rich regions are formed at the GBs of LLZO due to cation segregation and trapping effects. This behavior is independent of the GB tilting axis, degree of disorder at the GBs, and Co concentration, which implies Co clustering at GBs is a general phenomenon in polycrystalline LLZO and can dictate its overall transport and mechanical properties. Our findings elucidate the underlying fundamental mechanisms that give rise to experimentally observed physicochemical properties and provide guidelines for interface design that can mitigate interfacial degradation and improve cycling performance.
25 ENERGY STORAGE↗
Structural Anomalies and Electronic Properties of an Ionic Liquid under Nanoscale Confinement
Ionic liquids (ILs) promise far greater electrochemical performance compared to aqueous systems, yet key physicochemical properties governing their assembly at interfaces within commonly used graphitic nanopores remain poorly understood. Here, we combine synchrotron X-ray scattering with first-principles molecular dynamics simulations to unravel key structural characteristics of 1-alkyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([TFSI] – ) ionic liquids confined in carbon slit pores. X-ray scattering reveals selective pore filling due to size exclusion, while filled pores exhibit disruption in the IL intermolecular structure, the extent of which increases for narrower slit pores. First-principles simulations corroborate this finding and quantitatively describe how perturbations in the local IL structure, particularly the hydrogen-bond network, depend strongly on the degree of confinement. Despite significant deviations in structure under confinement, electrochemical stability remains intact, which is important for energy storage based on nanoporous carbon electrodes (e.g., supercapacitors).
36 MATERIALS SCIENCE↗
Global Backscatter Experiment (GLOBE) Results: Aerosol Backscatter Global Distribution and Wavelength Dependence
The GLObal Backscatter Experiment (GLOBE) was initiated by NASA in 1986 as an interagency and international research effort to characterize tropospheric backscatter properties. The primary objective of the program is to develop realistic aerosol backscatter inputs for design and simulation studies for NASA's prospective Laser Atmospheric Wind Sounder (LAWS). To achieve this, GLOBE incorporates several different types of aerosol sensors, which operate from a variety of sensor platforms, covering a wide range of spatial and temporal scales, and measure a diverse set of aerosol physical, chemical, and optical properties. The results of this analysis have provided important new information on the life cycles and physicochemical properties of global scale tropospheric aerosol systems. In addition, GLOBE analytical methods will be useful for the Earth Observing System (EOS) and other studies that involve the assimilation of large, complex atmospheric aerosol databases.
Bowdle, David A.↗
Surface functionality and formation mechanisms of carbon and graphene quantum dots
Graphene quantum dots (GQDs) and Carbon dots (C-dots) have been widely studied in recent years due to their structural and optoelectrical properties. These properties have prompted the exploration of the role of these carbon-based materials in many potential applications. This includes solar cells, photodetectors, bioimaging, sensors, batteries and drug delivery. These properties and applications of GQDs and C-dots are highly dependent on their size, shape and surface functionality. In this work, GQDs and C-dots mixtures have been synthesized by an inexpensive wet chemical method by varying the synthesis temperature from 85°, 100° to 115 °C. The surface functionalities of the synthesized carbon-based materials were investigated by several analytical methods. We discovered a higher degree of oxidation at higher temperatures. The mechanism of formation of different sized GQDs and C-dots with different functionalities have been explained with the help of XPS and NEXAFS analysis. The influence of size and surface functionalities on the optical properties of these nanomaterials is analyzed by UV–Vis and PL spectroscopic techniques. Furthermore, this study demonstrates that physicochemical properties of GQDs and C-dots can be controlled by changing the synthesis temperature.
36 MATERIALS SCIENCE↗
Molecular property prediction for very large databases with natural language processing: a case study in ionic liquid design
The prospect of using artificial intelligence (AI) to accurately screen very large databases of compounds for multiple properties has yet to be realized. Here, we explore this possibility using ionic liquids (ILs) which offer unique physicochemical properties and excellent tunability, making them highly versatile solvents for various research applications. Screening millions of potential ILs for the best perfomance for use in specific tasks with experimental methods alone however, is impractical. Further, traditional’ physics-based computational chemistry is hindered by high computational cost. To address this challenge, we leverage a natural language processing (NLP)-based molecular embedding technique with advanced machine learning (ML) models to predict seven key IL properties: viscosity, density, ionic conductivity, surface tension, melting temperature, toxicity, and water solubility. Comprehensive datasets for these properties are obtained, then NLP featurization with Mol2vec is compared with other featurization techniques such as 2D Morgan fingerprints, and 3D quantum chemistry-derived sigma profiles. NLP-based featurization exhibited the best predictive performance, achieving the highest R 2 and lowest RMSE values for all the studied IL properties. Further, we present case studies of how ILs might be screened using combined property criteria for practical cases – lignocellulosic biomass processing, CO 2 capture, and optimal electrolytes for batteries – screening a novel database of ∼10.6 million generated feasible ILs. The results introduce NLP as a powerful tool for engineering many designer solvents with desirable properties for task specific applications.
Mohan, Mood [Oak Ridge National Laboratory (ORNL),↗
Comparative Studies of the Structural and Transport Properties of Molten Salt FLiNaK Using the Machine-Learned Neural Network and Reparametrized Classical Forcefields
Despite surging interest in molten salt reactors and thermal storage systems, knowledge of the physicochemical properties of molten salts are still inadequate due to demanding experiments that require high temperature, impurity control, and corrosion mitigation. Therefore, the ability to predict these properties for molten salts from first-principles computations is urgently needed. Herein, we developed and compared a machine-learned neural network force field (NNFF) and a reparametrized rigid ion model (RIM) for a prototypical molten salt LiF–NaF–KF (FLiNaK). We found that NNFF was able to reproduce both the structural and transport properties of the molten salt with first-principles accuracy and classical-MD computational efficiency. Furthermore, the correlation between the local atomic structures and the dynamics was identified by comparing with RIMs, suggesting the significance of polarization of anions implicitly embedded in the NNFF. Furthermore, this work demonstrated a computational framework that can facilitate the screening of molten salts with different chemical compositions, impurities, and additives, and at different thermodynamic conditions suitable for the next-generation nuclear reactors and thermal energy storage facilities.
36 MATERIALS SCIENCE↗
Preparation of illite coated geomaterial microfluidic surfaces: Effect of salinity and heat treatment
Unique characteristics of clay (e.g., high surface area, deficiency in positive charges) make any clay abundant shales a good source of adsorbed gas. However, the presence of clay in porous media can also affect shale rock's producibility. The clay-water interaction can cause significant swelling and fines migration in the formation and impair the overall hydrocarbon recovery from shale. Geomaterial micromodels, developed by functionalizing traditional glass or PDMS surfaces with geomaterials (e.g., calcite, Quartz, clay), can represent the physicochemical properties of the natural porous media and have been used during the last few years to understand and evaluate the solid-fluid physicochemical interactions. Here, this study focuses on developing a clay-coated geomaterial surface and investigates the effect of base fluid's salinity on clay adsorption to the glass surface. Glass capillary tubes and straight channel borosilicate glass micromodels are coated with Illite clay to represent the pore-scale clay chemistry of Caney Shale, a Mississippian unconventional play in Southern Oklahoma, USA. 10 wt% of Illite clay slurries made with brines of four different salinities are used to coat the glass-capillary tubes to understand the effect of salinity in clay adsorption on the glass surface and overall coating quality. To achieve a stable coating and evaluate the impact of heat treatment on coating's stability, straight channel glass flow cells coated with Illite clay are heat-treated at low (25 °C) and high temperature (125 °C), and the results are compared with an untreated coated surface. Flooding tests were carried out with brines of different salinities on the coated surface to evaluate the stability of the coating. The experimental results indicated a strong relationship between the brine's salinity and the adsorption of clay particles on the glass surface. An increase in brine concentration resulted in improved adsorption of clay particles on the glass surface. Experiments involving heat treating the glass surface following the coating demonstrated significant improvement in the stability of the coating.
02 PETROLEUM↗
Chemical and Structural Changes in Corn Stover After Ensiling: Influence on Bioconversion
Production of biofuels, bioproducts, and bioenergy requires a well-characterized, stable, and reasonably uniform biomass supply and well-established supply chains for shipping biomass from farm fields to biorefineries, while achieving year-round production targets. Preserving and stabilizing biomass feedstock during storage is a necessity for cost-effective and sustainable biofuel production. Ensiling is a common storage method used to preserve and even improve forage quality; however, the impact of ensiling on biomass physical and chemical properties that influence bioconversion processes has been variable. Our objective in this work was to determine the effects of ensiling on lignocellulosic feedstock physicochemical properties and how that influences bioconversion requirements. We observed statistically significant decreases (p < 0.05) in the content of two major structural carbohydrates (glucan and xylan) of 5 and 8%, respectively, between the ensiled and non-ensiled materials. We were unable to detect differences in sugar yields from structural carbohydrates after pretreatment and enzymatic hydrolysis of the ensiled materials compared to non-ensiled controls. Based on this work, we conclude that ensiling the corn stover did not change the bioconversion requirements compared to the control samples and incurred losses of structural carbohydrates. At the light microscopy level, ensiled corn stover exhibited little structural change or relocation of cell wall components as detected by immunocytochemistry. However, more subtle structural changes were revealed by electron microscopy, as ensiled cell walls exhibit ultrastructural characteristics such as wall delimitation intermediate between non-ensiled and dilute-acid-pretreated cell walls. These findings suggest that alternative methods of conversion, such as deacetylation and mechanical refining, could take advantage of lamellar defects and may be more effective than dilute acid or hot water pretreatment for biomass conversion of ensiled materials.
09 BIOMASS FUELS↗