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At least 955 records · Page 53

A moment-conserving discontinuous Galerkin representation of the relativistic Maxwellian distribution

Kinetic simulations of relativistic gases and plasmas are critical for understanding diverse astrophysical and terrestrial systems, but the accurate construction of the relativistic Maxwellian, the Maxwell–Jüttner distribution, on a discrete simulation grid is challenging. Difficulties arise from the finite velocity bounds of the domain, which may not capture the entire distribution function, as well as errors introduced by projecting the function onto a discrete grid. Here, we present a novel scheme for iteratively correcting the moments of the projected distribution applicable to all grid-based discretizations of the relativistic kinetic equation. In addition, we describe how to compute the needed nonlinear quantities, such as Lorentz boost factors, in a discontinuous Galerkin scheme through a combination of numerical quadrature and weak operations. The resulting method accurately captures the distribution function and ensures that the moments match the desired values to machine precision.

astrophysical plasmas↗

Advanced Pt-Based Core–Shell Electrocatalysts for Fuel Cell Cathodes

Proton-exchange membrane fuel cells (PEMFCs) are highly efficient energy storage and conversion devices. Thus, the platinum group metal (PGM)-based catalysts which are the dominant choice for the PEMFCs have received extensive interest during the past couple of decades. However, the drawbacks in the existing PGM-based catalysts (i.e., high cost, slow kinetics, poor stability, etc.) still limit their applications in fuel cells. The Pt-based core–shell catalysts potentially alleviate these issues through the low Pt loading with the associated low cost and the high corrosion resistance and further improve the oxygen reduction reaction’s (ORR’s) activity and stability. This Account focuses on the synthetic strategies, catalytic mechanisms, factors influencing enhanced ORR performance, and applications in PEMFCs for the Pt-based core–shell catalysts. We first highlight the synthetic strategies for Pt-based core–shell catalysts including the galvanic displacement of an underpotentially deposited non-noble metal monolayer, thermal annealing, and dealloying methods, which can be scaled-up to meet the requirements of fuel cell operations. Subsequently, catalytic mechanisms such as the self-healing mechanism in the Pt monolayer on Pd core catalysts, the pinning effect of nitrogen (N) dopants in N-doped PtNi core–shell catalysts, and the ligand effect of the ordered intermetallic structure in L10-Pt/CoPt core–shell catalysts and their synergistic effects in N-doped L10-PtNi catalysts are described in detail. Additionally, the core–shell structure in the Pt-based catalysts have two main effects for enhanced ORR performance: (i) the interaction between Pt shells and core substrates can tune the electronic state of the surface Pt, thus boosting the ORR activity and stability, and (ii) the outer Pt shell with modest thickness can enhance the oxidation and dissolution resistance of the core, resulting in improved durability. We then review the recent attempts to optimize the ORR performance of the Pt-based core–shell catalysts by considering the shape, composition, surface orientation, and shell thickness. The factors influencing the ORR performance can be grouped into two categories: the effect of the core and the effect of the shell. In the former, PtM core–shell catalysts which use different non-PGM element cores (M) are summarized, and in the latter, Pt-based core–shell catalysts with different shell structures and compositions are described. The modifications of the core and/or shell structure can not only optimize the intermediate-binding energetics on the Pt surface through tuning the strain of the surface Pt, which increases the intrinsic activity and stability, but also offer a significantly decreased catalyst cost. Finally, we discuss the membrane electrode assembly performance of Pt-based core–shell catalysts in fuel cell cathodes and evaluate their potential in real PEMFCs for light-duty and heavy-duty vehicle applications. Even though some challenges to the activity and lifetime in the fuel cells remain, the Pt-based core–shell catalysts are expected to be promising for many practical PEMFC applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Toward Machine Learning-Driven Mass Spectrometric Identification of Trichothecenes in the Absence of Standard Reference Materials

While a significant body of work exists on the detection of commonly known trichothecene toxins, biological, environmental, and other transformational processes can generate many under-characterized and unknown modified trichothecenes. Lacking both analytical reference standards and associated mass spectral databases, identification of these modified compounds reflects both a challenge and a critical gap from forensic and public health perspectives. Here we report here the application of machine learning (ML) techniques toward identification of discriminative fragment ions from mass spectrometric data that can be exploited to detect evidence of type A and B trichothecenes. The goal of this work is to establish a new method for the identification of unknown, though structurally similar trichothecenes, by leveraging objective ML techniques. Discriminative fragments derived from a series of gradient-boosted machine learners are then used to develop ML-driven precursor ion scan (PIS) methods on a triple quadrupole mass spectrometer (QQQ) for screening of “unknown unknown” trichothecenes. Specifically, we apply the PIS method to a laboratory-synthesized trichothecene, a first step in demonstrating the power of alternative, machine learning-driven mass spectrometric methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Long-Term Cyclability of NCM-811 at High Voltages in Lithium-Ion Batteries: an In-Depth Diagnostic Study

High-nickel layered oxides, such as LiNi 0.8 Co 0.1 Mn 0.1 O 2 (NCM-811), offer higher energy density than their low-nickel counterparts at a given voltage and are gaining major traction in automotive lithium-ion batteries for electric vehicles. Besides high-Ni content, higher charging voltages above 4.3 V vs Li + /Li boost the energy and represent another focus in battery development. Here, we investigate the long-term cyclability of NCM-811 in graphite pouch cells over 1000 deep cycles between 2.5–4.2, 2.5–4.4, and 2.5–4.5 V through a suite of sensitive characterization techniques. The NCM-811 full cells show severely deteriorated cyclability with higher charging voltages, from 78% at 4.2 V to 52 and 32% at 4.4 and 4.5 V, respectively. At 4.2 V, minor parasitic electrolyte oxidation as well as cathode Li/Ni mixing and cracking are revealed after cycling, while transition-metal dissolution and surface reconstruction into rock-salt NiO are virtually undetectable. At 4.4 and 4.5 V, transition-metal dissolution and crossover to the anode become much more pronounced and a primary contributor to the capacity fade, while significant surface NiO formation causes substantial voltage polarization, which is less noticeable at 4.2 V. Meanwhile, more severe electrolyte oxidation, Li/Ni mixing, and particle pulverization exacerbate the voltage and capacity fade. Finally, these results outline distinct challenges for stable high-Ni layered oxide cathodes in high-voltage Li-ion batteries.

25 ENERGY STORAGE↗

Evaluation of Nanostructured β-Mn 2 V 2 O 7 Thin Films as Photoanodes for Photoelectrochemical Water Oxidation

β-Mn 2 V 2 O 7 (β-MVO) was recently reported to be a promising candidate for photoelectrochemical (PEC) water splitting, with a suitable band gap and band edge positions and reasonable stability and photoactivity in preliminary tests in alkaline solution. Here, we present an in-depth evaluation of the PEC performance and stability of phase-pure nanostructured β-Mn 2 V 2 O 7 films made by calcination of a spin-cast molecular ink. We show that β-Mn 2 V 2 O 7 dissolves in pure water, corrodes in aqueous electrolytes at pH 7 and 9, and converts to amorphous manganese (hydr)oxides within minutes at pH 13. Our β-Mn 2 V 2 O 7 films yielded only miniscule photocurrents (~uA cm -2 ) for the oxidation of iodide, sulfite, or water and the reduction of iodate or water in borate- and phosphate-buffered electrolytes at pH 7 and 9, the oxidation of [Fe(CN) 6 ] 4- or water at pH 13, and the oxidation of bromide in acetonitrile, regardless of film calcination temperature and time, film thickness, and illumination geometry. Minimal photoactivity was observed even in electrolytes in which film degradation was insignificant over the duration of the PEC tests. Ultrafast transient absorption spectroscopy shows that the poor photoactivity is likely the result of fast hole trapping and recombination at the MVO surface that leaves few free charge carriers beyond the picosecond time scale. Given its poor charge transport/extraction and chemical stability, native nanostructured β-Mn 2 V 2 O 7 is ineffective for solar water splitting and future research on this material should focus on the development of superior syntheses and film morphologies, MVO alloys, heterostructures, and surface coatings to alleviate recombination and boost operational stability. This work underscores the importance of careful follow-up studies of materials identified as "hits" in combinatorial materials discovery campaigns.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Engineering of a Coupled Nanocomposite as a High-Performance Protonic Ceramic Fuel Cell Cathode

The lack of high-performance cathode catalysts is a salient issue that bedeviled the commercialization of protonic ceramic fuel cells (PCFCs). Here, in this work, we report a remarkable electrocatalytic activity and stability enhancement of cathode electrodes by engineering a coupled nanocomposite. The as-prepared Pr 0.3 (Ba 0.5 Sr 0.5 ) 0.7 Co 0.8 Fe 0.2 O 3−δ nanocomposite possesses a bulk cubic phase on which homogeneous and intimate orthorhombic PrCo 0.5 Fe 0.5 O 3−δ nanoparticles are uniformly decorated. X-ray diffraction and Raman spectroscopy reveal the excellent thermal stability of the nanocomposite. It achieves a high peak power density of 1.02 W cm –2 based on protonic electrolytes at 600 °C. No noticeable structural degradation is observed over ∼210 h at 550 °C according to scanning electron microscopy analysis. This work demonstrates an effective strategy to boost the performance of perovskite oxides for PCFCs via nanocomposite engineering. It may apply to other catalyst designs and discoveries, such as for batteries, electrolyzers, and membrane reactors.

08 - HYDROGEN↗

Evaluating Material Design Principles for Calcium-Ion Mobility in Intercalation Cathodes

Multivalent-ion batteries offer an alternative to Li-based technologies, with the potential for greater sustainability, improved safety, and higher energy density, primarily due to their rechargeable system featuring a passivating metal anode. Although a system based on the Ca 2+ /Ca couple is particularly attractive given the low electrochemical plating potential of Ca 2+ , the remaining challenge for a viable rechargeable Ca battery is to identify Ca cathodes with fast ion transport. In this work, a high-throughput computational pipeline is adapted to (1) discover novel Ca cathodes in a largely unexplored space of empty intercalation hosts and (2) develop material design rules for Ca-ion mobility. One candidate from the screening, W 2 O 3 (PO 4 ) 2 , is confirmed to have a low Nudged Elastic Band (NEB) barrier of 168 meV within a one-dimensional (1D) ion percolation topology. This candidate is subsequently synthesized and electrochemically tested, achieving reversible Ca cycling with a capacity of 25 mA h/g. To further accelerate the screening for promising Ca intercalation electrodes, machine learning (ML) Random Forest (RF) and Extreme Gradient Boosting (XGB) classification models are created with local environment descriptors based on a large, structurally and chemically diverse dataset of minimum energy pathways, spanning over 5,000 density functional theory (DFT) site energy calculations. Accuracies of 92% are achieved, material design metrics are quantified, ML force-fields are leveraged in an accelerated iteration of the screening, and a total of 27 novel Ca cathode materials are highlighted for further investigation.

25 ENERGY STORAGE↗

Toward Long-Life, Ultrahigh-Nickel Layered Oxide Cathodes for Lithium-Ion Batteries: Optimizing the Interphase Chemistry with a Dual-Functional Polymer

Boosting the Ni content in LiMO 2 (M = Ni, Co, Mn, etc.) layered oxides is a promising way to establish high-energydensity, low-cost cathodes, but the poor cathode surface stability is a daunting challenge for their practical viability. Herein, by constructing a dual-functional binder framework with a conductive polymer - polyaniline (PANI), the ultrahigh-Ni layered oxide cathode (LiNi 0.94 Co 0.06 O 2 ) exhibits significantly improved cyclability, with a capacity retention greatly increased from 47% to 81% over 1,000 cycles in full cells. It is demonstrated that the acidic species (e.g. HF) in the electrolyte can be efficiently scavenged through a protonation process of PANI, hence the cathode surface reactivity is greatly suppressed and the rock-salt phase propagation into bulk structure is considerably alleviated. Furthermore, the PANI binder system effectively prevents both the cathode-electrolyte interphase (CEI) and anode-electrolyte interphase (AEI) from degrading to a thick “triple-layer” architecture upon extensive cycling, resulting in more robust, thinner CEI and AEI with regulated interphasial chemistry. Moreover, the delocalized π-conjugated electrons along the backbone of PANI facilitate fast electron transfer and promote rate capability even at low temperatures (-20 °C). Finally, this work sheds light on rational binder engineering for developing high-energy-density lithium-ion batteries with acceptable cycle life.

25 ENERGY STORAGE↗

Reducing Economy-Wide Greenhouse Gas Emissions with Electrofuels and Biofuels as the Grid Decarbonizes

Biofuels and electrofuels have the potential to complement electrification in speeding greenhouse gas emissions reductions, especially in hard-to-decarbonize sectors. Concentrated waste CO 2 streams that can be used as a feedstock for electrofuels, however, may become less available as the share of renewable electricity increases and industries undergo decarbonization. Here, we conduct an analysis with detailed treatment of biofuels and electrofuels to probe their role in decarbonizing multiple industrial sectors and transportation. We examine how the changing availability of CO 2 could affect electrofuel production and the potential role of direct air capture in stabilizing the supply of CO 2 . The results indicate that biofuels could fulfill 12% of the total final energy demand across all U.S. sectors in 2050. Using seven industrial source points of CO 2 available in 2050, 15,388 PJ of electrofuels could be produced, which amounts to 25% of the total final energy demand. This result holds even upon decarbonization that requires direct air capture to boost the CO 2 supply. Biofuels and e-fuels have the potential to reduce economy-wide GHG emissions by 7 and 21% beyond electrification alone. However, electricity consumption and land use grow markedly with decarbonization at scale.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Unsteady- and Steady-State Relative Permeability Study with X-ray and Acoustic Monitoring for CO 2 Storage in Deep Saline Aquifers

In this study, we link the multiphase flow measurements with controls of sedimentary structures (e.g., heterogeneity and anisotropy) on relative permeability to variations in ultrasonic velocities for two deep saline aquifer formations (Entrada and Bluff Sandstones) in the San Juan Basin of the Southwestern USA. The rock specimens were extracted from outcrop sites near Durango, CO, USA. They have distinct differences in grain size, cementation composition, and individual chemical amounts, despite both formations being eolian sandstones. We performed a series of unsteady- and steady-state CO 2 -brine relative permeability experiments under capillary-controlled displacement rates. Unsteady-state experiments were conducted at 71 °C and 9.65 MPa; steady-state experiments were conducted at 85 °C and 22.8 MPa and 89 °C and 24.1 MPa for the Bluff and Entrada Sandstones, respectively. During the unsteady experiments, X-ray computed tomography was used to visualize multiphase flow in porous media and quantify saturations during brine drainage under various flow rates. Scan images and saturation profiles indicate that the CO 2 distribution in the pore volume was strongly impacted by the presence of high-angle cross-laminations, heterogeneous rock structure, and direction of bedding orientation. Those factors contribute to dramatic and quick initial breakthroughs and affect the overall saturation dynamics. Steady-state relative permeability tests were conducted at net flow rates of 1 mL/min for both brine drainage and imbibition. During the experimental steps, the CO 2 fractional flow was increased and decreased for both drainage and imbibition scenarios to mimic the front when CO 2 contacts brine and behind the front when brine enters space previously occupied by CO 2 . It was found that compressional velocity decreased, while shear waves slightly increased as brine saturation decreased. The hysteresis effects for the relative permeability and acoustic velocities were distinct. Furthermore, the CO 2 /brine front stability is quantified by applying a mobility ratio approach to spot saturations at which the boundary line between fluids becomes uneven. The results presented in this work can potentially boost the quality and precision of forecasts for the CO 2 storage projects in which the vertical and horizontal core-scale heterogeneity and anisotropy impact the plume migration within host reservoirs.

carbon dioxide (CO2)↗

Transformation of Highly Stable Pt Single Sites on Defect Engineered Ceria into Robust Pt Clusters for Vehicle Emission Control

Engineering surface defects on metal oxide supports could help promote the dispersion of active sites and catalytic performance of supported catalysts. Herein, a strategy of ZrO 2 doping was proposed to create rich surface defects on CeO 2 (CZO) and, with these defects, to improve Pt dispersion and enhance its affinity as single sites to the CZO support (Pt/CZO). The strongly anchored Pt single sites on CZO support were initially not efficient for catalytic oxidation of CO/C 3 H 6 . However, after a simple activation by H 2 reduction, the catalytic oxidation performance over Pt/CZO catalyst was significantly boosted and better than Pt/CeO 2 . Pt/CZO catalyst also exhibited much higher thermal stability. The structural evolution of Pt active sites by H 2 treatment was systematically investigated on aged Pt/CZO and Pt/CeO 2 catalysts. With H 2 reduction, ionic Pt single sites were transformed into active Pt clusters. Much smaller Pt clusters were created on CZO (ca. 1.2 nm) than on CeO 2 (ca. 1.8 nm) due to stronger Pt-CeO 2 interaction on aged Pt/CZO. Consequently, more exposed active Pt sites were obtained on the smaller clusters surrounded by more oxygen defects and Ce 3+ species, which directly translated to the higher catalytic oxidation performance of activated Pt/CZO catalyst in vehicle emission control applications.

36 MATERIALS SCIENCE↗

Hydroxyls on CeO 2 Support Promoting CuO/CeO 2 Catalyst for Efficient CO Oxidation and NO Reduction by CO

Transition metal catalysts, such as copper oxide, are more attractive alternatives to noble metal catalysts for emission control due to their higher abundance, lower cost, and excellent catalytic activity. Here, in this study, we report the preparation and application of a novel CuO/CeO 2 catalyst using a hydroxyl-rich Ce(OH) x support for CO oxidation and NO reduction by CO. Compared to the catalyst prepared from a regular CeO 2 support, the new CuO/CeO 2 catalyst prepared from the OH-rich Ce(OH) x (CuO/CeO 2 –OH) showed significantly higher catalytic activity under different testing conditions. The effect of OH species in the CeO 2 support on the catalytic performance and physicochemical properties of the CuO/CeO 2 catalyst was characterized in detail. It is demonstrated that the abundant OH species enhanced the CuO x dispersion on CeO 2 , increased the CuO x –CeO 2 interfaces and surface defects, promoted the oxygen activation and mobility, and boosted the NO adsorption and dissociation on CuO/CeO 2 –OH, thus contributing to its superior catalytic activity for both CO oxidation and NO reduction by CO. These results suggest that the OH-rich Ce(OH) x is a superior support for the preparation of highly efficient metal catalysts for different applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Serendipita indica Drives Sulfur-Related Microbiota in Enhancing Growth of Hyperaccumulator Sedum alfredii and Facilitating Soil Cadmium Remediation

Endophytic fungus Serendipita indica can bolster plant growth and confer protection against various biotic and abiotic stresses. However, S. indica -reshaped rhizosphere microecology interactions and root-soil interface processes in situ at the submicrometer scale remain poorly understood. Here, we combined amplicon sequencing and high-resolution nano X-ray fluorescence (nano-XRF) imaging of the root-soil interface to reveal cadmium (Cd) rhizosphere processes. S. indica can successfully colonize the roots of Sedum alfredii Hance, which induces a remarkable increase in shoot biomass by 211.32% and Cd accumulation by 235.72%. Nano-XRF images showed that S. indica colonization altered the Cd distribution in the rhizosphere and facilitated the proximity of more Cd and sulfur (S) to enter the roots and transport to the shoot. Furthermore, the rhizosphere-enriched microbiota demonstrated a more stable network structure after the S. indica inoculation. Keystone species were strongly associated with growth promotion and Cd absorption. For example, Comamonadaceae are closely related to the organic acid cycle and S bioavailability, which could facilitate Cd and S accumulation in plants. Meanwhile, Sphingomonadaceae could release auxin and boost plant biomass. In summary, we construct a mutualism system for beneficial fungi and hyperaccumulation plants, which facilitates high-efficient remediation of Cd-contaminated soils by restructuring the rhizosphere microbiota.

60 APPLIED LIFE SCIENCES↗

Operation and Optimization of Microwave-Heated Continuous-Flow Microfluidics

Microwave (MW) technology can be powerful for electrification and process intensification but limited fundamental understanding of scalability and design principles hinders its effective use. In this work, we build a continuous-flow microreactor inside a commercial single-mode MW applicator and the corresponding computational fluid dynamics model to simulate the temperature profile. The model is in good agreement with experiments for various microreactor dimensions and operating conditions. The model indicates that MW heating is greatly influenced by reactor geometry as well as the operating parameters. We observe a strong correlation between parameters and develop a gradient boost regression tree model to predict the outlet temperature accurately. This model is then applied to optimize the dimensions and operating conditions to maximize the outlet temperature and energy efficiency, resulting in a Pareto optimal. We demonstrate computationally and experimentally that it is possible to surpass the Pareto optimal and achieve an energy efficiency of ~90% or greater at temperatures relevant for liquid-phase chemistry via salting of the solvent. The present methodology can be applied to other complex MW reactors. Lastly, the combined numerical and experimental approach provides insights into and a framework for scale-up and optimization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Improving Data and Prediction Quality of High-Throughput Perovskite Synthesis with Model Fusion

Combinatorial fusion analysis (CFA) is an approach for combining multiple scoring systems using the rank-score characteristic function and cognitive diversity measure. One example is to combine diverse machine learning models to achieve better prediction quality. In this work, we apply CFA to the synthesis of metal halide perovskites containing organic ammonium cations via inverse temperature crystallization. Using a data set generated by high-throughput experimentation, four individual models (support vector machines, random forests, weighted logistic classifier, and gradient boosted trees) were developed. We characterize each of these scoring systems and explore 66 possible combinations of the models. When measured by the precision on predicting crystal formation, the majority of the combination models improves the individual model results. The best combination models outperform the best individual models by 3.9 percentage points in precision. In addition to improving prediction quality, we demonstrate how the fusion models can be used to identify mislabeled input data and address issues of data quality. In particular, we identify example cases where all single models and all fusion models do not give the correct prediction. Experimental replication of these syntheses reveals that these compositions are sensitive to modest temperature variations across the different locations of the heating element that can hinder or enhance the crystallization process. In summary, we demonstrate that model fusion using CFA can not only identify a previously unconsidered influence on reaction outcome but also be used as a form of quality control for high-throughput experimentation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Optimal Orbital Selection for Full Configuration Interaction (OptOrbFCI): Pursuing the Basis Set Limit under a Budget

Full configuration interaction (FCI) solvers are limited to small basis sets due to their expensive computational costs. An optimal orbital selection for FCI (OptOrbFCI) is proposed to boost the power of existing FCI solvers to pursue the basis set limit under a computational budget. The optimization problem coincides with that of the complete active space SCF method (CASSCF), while OptOrbFCI is algorithmically quite different. OptOrbFCI effectively finds an optimal rotation matrix via solving a constrained optimization problem directly to compress the orbitals of large basis sets to one with a manageable size, conducts FCI calculations only on rotated orbital sets, and produces a variational ground-state energy and its wave function. Coupled with coordinate descent full configuration interaction (CDFCI), we demonstrate the efficiency and accuracy of the method on the carbon dimer and nitrogen dimer under basis sets up to cc-pV5Z. We also benchmark the binding curve of the nitrogen dimer under the cc-pVQZ basis set with 28 selected orbitals, which provide consistently lower ground-state energies than the FCI results under the cc-pVDZ basis set. Furthermore, the dissociation energy in this case is found to be of higher accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Extracting Inelastic Scattering Cross Sections for Finite and Aperiodic Materials from Electronic Dynamics Simulations

Explicit time-dependent electronic structure theory methods are increasingly prevalent in the areas of condensed matter physics and quantum chemistry, with the broad-band optical absorptivity of molecular and small condensed-phase systems nowadays routinely studied with such approaches. Here, in this paper, it is demonstrated that electronic dynamics simulations can similarly be employed to study cross sections for the scattering-induced electronic excitations probed in nonresonant inelastic X-ray scattering and momentum-resolved electron energy loss spectroscopies. A method is put forth for evaluating the electronic dynamic structure factor, which involves the application of a momentum boost-type perturbation and transformation of the resulting reciprocal space density fluctuations into the frequency domain. Good agreement is first demonstrated between the dynamic structure factor extracted from these electronic dynamics simulations and the corresponding transition matrix elements from linear response theory. The method is then applied to some extended (quasi)one-dimensional systems, for which the wave vector becomes a good quantum number in the thermodynamic limit. Finally, the dispersion of many-body excitations in a series of hydrogen-terminated graphene flakes (and twisted bilayers thereof) is investigated to highlight the utility of the presented approach for capturing morphology-dependent effects in the inelastic scattering cross sections of nanostructured and/or noncrystalline materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Accurate Machine Learning for Predicting the Viscosities of Deep Eutectic Solvents

Deep eutectic solvents (DESs) are emerging as environmentally friendly designer solvents for mass transport and heat transfer processes in industrial applications; however, the lack of accurate tools to predict and thus control their viscosities under both a range of environmental factors and formulations hinders their general application. While DESs may serve as designer solvents, with nearly unlimited combinations, this unfortunately makes it experimentally infeasible to comprehensively measure the viscosities of all DESs of potential industrial interest. To assist in the design of DESs, we have developed several new machine learning (ML) models that accurately and rapidly predict the viscosities of a diverse group of DESs at different temperatures and molar ratios using, to date, one of the most comprehensive data sets containing the properties of over 670 DESs over a wide range of temperatures (278.15–385.25 K). Three ML models, including support vector regression (SVR), feed forward neural networks (FFNNs), and categorical boosting (CatBoost), were developed to predict DES viscosity as a function of temperature and molar ratio and contrasted with multilinear and two-factor polynomial regression baselines. Further, quantum chemistry-based, COSMO-RS-derived sigma profile (σ-profile) features were used as inputs for the ML models. The CatBoost model is excellent at externally predicting DES viscosity, as indicated by high R 2 (0.99) and low root-mean-square-error (RMSE) and average absolute relative deviations (AARD) (5.22%) values for the testing data sets, and 98% of the data points lie within the 15% of AARD deviations. Furthermore, SHapley additive explanation (SHAP) analysis was employed to interpret the ML results and rationalize the viscosity predictions. The result is an ML approach that accurately predicts viscosity and will aid in accelerating the design of appropriate DESs for industrial applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗