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Mohan, Mood

Publications and source records attributed to Mohan, Mood.

11 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Physics-informed machine learning to predict solvatochromic parameters of designer solvents with case studies in CO 2 and lignin dissolution

The polarity of solvents plays a critical role in various research applications, particularly in their solubilities. Polarity is conveniently characterized by the Kamlet-Taft parameters that is, the hydrogen bonding acidity (α), the basicity (β), and the polarizability (π*). Obtaining Kamlet-Taft parameters is very important for designer solvents, namely ionic liquids (ILs) and deep eutectic solvents (DESs). However, given the unlimited theoretical number of combinations of ionic pairs in ILs and hydrogen-bond donor/acceptor pairs in DESs, experimental determination of their Kamlet-Taft parameters is impractical. To address this, the present study developed two different machine learning (ML) algorithms to predict Kamlet-Taft parameters for designer solvents using quantum chemically derived input features. The ML models developed in the present study showed accurate predictions with high R 2 and low RMSE values. Further, in the context of present interest in the circular bioeconomy, the relationship between the basicities and acidities of designer solvents and their ability to dissolve lignin and carbon dioxide (CO 2 ) is discussed. Our method thus guides the design of effective solvents with optimal Kamlet-Taft parameter values dissolving and converting biomass and CO 2 into valuable chemicals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Macroscale properties and atomic-scale mechanisms of ash removal in low-temperature hydrothermal carbonization

Biogenic ash is a significant impediment to the utilization of agricultural residues in biofuel production. Such challenge can be addressed by various treatments, as demonstrated in this study on the experimental and computational mechanisms involved in the hydrothermal treatment (HT) of wheat straw. A combination of classical (all-atom) molecular dynamics simulations of cellulose carrying silica and calcium species, along with first principles quantum chemical calculations, indicates the dissociation of inorganics from the cellulose with increased HT temperature. Here, this observation is confirmed by experimental evidence of effective ash removal by HT, showing at least 50% removal of sulfur, chlorine, potassium, and calcium, and 12.5% of silica, leading to a reduced total ash content (from 6.7% to 4.2%).Changes in structural features upon HT, such as surface cellular structure and porosity, were revealed, accompanied by an increased specific surface area (from 1.17 to 6.34 m 2 /g). Our simulations suggest that silica binds tightly to the hydrophobic face of cellulose at room temperature, but HT significantly reduces the binding free energy of association with both hydrophobic and hydrophilic surfaces. Most significantly, ash removal leads to an increased calorific value, rising from approximately 16 MJ/kg to about 19 MJ/kg, along with improved thermal behavior. The improved integration combustion index parameter S indicates that the combustion properties improve with ash removal efficiency. The proposed atomic-level mechanism for the observed removal of inorganics during mild HT underscores the potential of such treatment in producing energy-dense wheat straw, a widely available agricultural residue.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Molecular-level design of alternative media for energy-saving pilot-scale fibrillation of nanocellulose

The outstanding mechanical properties, light weight, and biodegradability of cellulose nanofibrils (CNFs) make them promising components of renewable and sustainable next-generation reinforced composite biomaterials and bioplastics. Manufacturing CNFs at a pilot scale requires disc-refining fibrillation of dilute cellulose fibers in aqueous pulp suspensions to shear the fibers apart into their nanodimensional forms, which is, however, an energy-intensive process. Here, in this study, we used atomistic molecular dynamics (MD) simulation to examine media that might facilitate the reduction of interactions between cellulose fibers, thereby reducing energy consumption in fibrillation. The most suitable medium found by the simulations was an aqueous solution with 0.007:0.012 wt.% NaOH:urea, and indeed this was found in pilot-scale experiments to reduce the fibrillation energy by ~21% on average relative to water alone. The NaOH:urea-mediated CNFs have similar crystallinity, morphology, and mechanical strength to those formed in water. The NaOH and urea act synergistically on CNFs to aid fibrillation but at different length scales. NaOH deprotonates hydroxyl groups leading to mesoscale electrostatic repulsion between fibrils, whereas urea forms hydrogen bonds with protonated hydroxyl groups thus disrupting interfibril hydrogen bonds. This suggests a general mechanism in which an aqueous medium that contains a strong base and a small organic molecule acting as a hydrogen-bond acceptor and/or donor may be effectively employed in materials processes where dispersion of deprotonable polymers is required. The study demonstrates how atomic-detail computer simulation can be integrated with pilot-scale experiments in the rational design of materials processes for the circular bioeconomy.

36 MATERIALS SCIENCE↗
DOE OSTI2024

High-Throughput Screening and Accurate Prediction of Ionic Liquid Viscosities Using Interpretable Machine Learning

Ionic liquids (ILs) are a novel group of green solvents with great promise for various industrial applications, including carbon capture and lignocellulosic biomass deconstruction. However, the use of ILs at the industrial scale remains challenging due to their high viscosities at ambient temperatures. To develop ILs with lower viscosities, a systematic study of their quantitative structure–property relationship (QSPR) is desirable. Here, we developed four machine learning (ML) models to predict viscosity at various temperature and pressure ranges, trained over a wide range of ILs consisting of various cationic and anionic families. ML methods including two-factor polynomial regression (two-factor PR), support vector regression (SVR), feed-forward neural networks (FFNN), and categorical boosting (CATBoost) were developed based on features that have proven useful in previous ML studies: COSMO-RS (conductor-like screening model for real solvents)-derived surface screening charge densities (sigma profiles). FFNN and CATBoost were the most accurate in predicting IL viscosities with lower average absolute relative deviation and higher R2 values on the test set. Tanimoto similarity scores were calculated to characterize the chemical space and structural similarity of the investigated ions. Furthermore, SHapley Additive exPlanation (SHAP) analysis was employed to interpret the ML results. Temperature, the polar area of ILs, and the nonpolar regions of ions are key features that influence the viscosity predictions. Importantly, the IL viscosity prediction here is the most accurate reported to date.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Inhibition of asphaltene aggregation using deep eutectic solvents: COSMO-RS calculations and experimental validation

Asphaltene precipitation/deposition adversely affects various oil and gas processes, such as oil recovery, transportation, and petroleum processing. The resulting increase in viscosity of crude oil decreases distillate yields creating unstable phase separation. Deep eutectic solvents (DESs) have recently gained interest as inhibitors of asphaltene aggregation. The goal of the present study is to derive the mechanism of inhibition of petroleum asphaltene aggregation by a novel screening of DESs. An archipelago-based chemical structure of asphaltene was adopted for performing quantum chemical calculations. The structure was used in the conductor-like screening model for real solvents (COSMO-RS) model to screen potential DESs for asphaltene precipitation. It was found that DESs containing thymol were the most promising of the 153 DES combinations screened. The COSMO-RS predictions were validated experimentally using solubility data of asphaltene in DESs. Among the studied DESs, thymol-diphenyl ether provided the highest solubility for asphaltene, which was further validated using the experimental and the COSMO-RS predicted data. In addition, to characterize the structural interactions between asphaltene and DESs, Fourier transform infrared (FTIR) spectroscopy and nuclear magnetic resonance (NMR) measurements were performed. It was found that strong interaction between asphaltene and the DESs is responsible for the higher asphaltene dispersion. Finally, the present approach opens pathways to rationally design and understand the impact of structural variation of DESs based on their interactions with asphaltene.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Multiscale investigation of the mechanism of biomass deconstruction in the dimethyl isosorbide/water Co-solvent pretreatment system

In the context of promoting a circular bioeconomy, the development of green and efficient lignocellulosic biomass pretreatment technologies so as to realize high value-added biomass utilization is of intense interest. We demonstrated the potential of the bio-based green solvent dimethyl isosorbide (DMI) for the fractionation of Eucalyptus biomass with excellent performance. Here, to investigate the mechanisms involved in biomass fractionation, microimaging and microspectroscopic techniques were employed together with molecular dynamics (MD) simulation and COSMO-RS quantum chemical calculations to derive multiscale information. Both the microstructure and regional chemistry of the cell wall vary significantly with the volume ratio of DMI/H 2 O. The strongest effects were found at DMI/H 2 O = 9:1 and showed visible cell wall tearing cracks and cell wall deformation and collapse as well as the lowest values of cell wall thickness and circularity. From the MD simulations, lignin exhibits collapsed-like structure in pure H 2 O with low solvent accessibility surface area (SASA) and radius of gyration (R g ). In contrast, lignin in DMI/H 2 O shows extended structure with high SASA and solvent interactions dominated by van der Waals forces, with maximal contact in the 9:1 (v/v) system. Further, the COSMO-RS calculated sigma (σ-) potential suggests the intermolecular interactions in DMI and DMI/H 2 O co-solvent are weak, leading to stronger interaction with lignin and correspondingly higher lignin dissolution. The radial distribution functions and σ-potential all show that again DMI/H 2 O at 9:1 is an optimal volume ratio for high lignin dissolution. Finally, this study provides a solvent-ratio dependent mechanism for the action of polar aprotic solvents in the deconstruction of biomass.

09 BIOMASS FUELS↗
DOE OSTI2024

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↗
DOE OSTI2023

Characterization of the acoustic cavitation in ionic liquids in a horn-type ultrasound reactor

Most ultrasound-based processes root in empirical approaches. Because nearly all advances have been conducted in aqueous systems, there exists a paucity of information on sonoprocessing in other solvents, particularly ionic liquids (ILs). In this work, we modelled an ultrasonic horn-type sonoreactor and investigated the effects of ultrasound power, sonotrode immersion depth, and solvent’s thermodynamic properties on acoustic cavitation in nine imidazolium-based and three pyrrolidinium-based ILs. The model accounts for bubbles, acoustic impedance mismatch at interfaces, and treats the ILs as incompressible, Newtonian, and saturated with argon. Following a statistical analysis of the simulation results, we determined that viscosity and ultrasound input power are the most significant variables affecting the intensity of the acoustic pressure field (P), the volume of cavitation zones (V), and the magnitude of the maximum acoustic streaming surface velocity (u). V and u increase with the increase of ultrasound input power and the decrease in viscosity, whereas the magnitude of negative P decreases as ultrasound power and viscosity increase. Probe immersion depth positively correlates with V, but its impact on P and u is insignificant. 1-alkyl-3-methylimidazolium-based ILs yielded the largest V and the fastest acoustic jets – 0.77cm 3 and 24.4ms -1 for 1-ethyl-3-methylimidazolium chloride at 60W. 1-methyl-3-(3-sulfopropyl)-imidazolium-based ILs generated the smallest V and lowest u – 0.17cm 3 and 1.7ms -1 for 1-methyl-3-(3-sulfopropyl)-imidazolium p-toluene sulfonate at 20W. Sonochemiluminescence experiments validated the model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Biomass pretreatment with distillable ionic liquids for an effective recycling and recovery approach

Ionic liquid (IL) pretreatment methods show incredible promise for the efficient conversion of lignocellulosic feedstocks to fuels and chemicals. Given their low vapor pressures, distillation-based methods of extracting ionic liquids out of biomass post-pretreatment have historically been ignored in favor of alternative methods. We demonstrate a process to distill four acetate-based ionic liquids ([EthA][OAc], [PropA][OAc], [MAEthA][OAc], and [DMAEthA][OAc]) at low pressure and high purity that overcome some disadvantages of “water washing” and “one pot” recovery methods. Out of four tested ILs, ethanolamine acetate ([EthA][OAc]) is shown to have the most agreeable conversion metrics for commercial bioconversion processes achieving 73.6% and 51.4% of theoretical glucose and xylose yields respectively and >85 % recovery rates. Our process metrics are factored into a techno-economic analysis where [EthA][OAc] distillation is compared to other recovery methods as well as ethanolamine pretreatment at both milliliter and liter scales. In conclusion, although our TEA shows [EthA][OAc] distillation underperforming against other processes, we show a step-by-step avenue to reduce sugar production cost below the wholesale dextrose price at scale.

09 BIOMASS FUELS↗
DOE OSTI2023

A hybrid chemical-biological approach can upcycle mixed plastic waste with reduced cost and carbon footprint

Derived from renewable feedstocks, such as biomass, polylactic acid (PLA) is considered a more environmentally friendly plastic than conventional petroleum-based polyethylene terephthalate (PET). However, PLA must still be recycled, and its growing popularity and mixture with PET plastics at the disposal stage poses a cross-contamination threat in existing recycling facilities and results in low-value and low-quality recycled products. Hybrid upcycling has been proposed as a promising sustainable solution for mixed plastic waste, but its techno-economic and life cycle environmental performance remain understudied. We propose a hybrid upcycling approach using a biocompatible ionic liquid (IL) to first chemically depolymerize plastics and then convert the depolymerized stream via biological upgrading with no extra separation. Here we show that over 95% of mixed PET/PLA was depolymerized into the respective monomers, which then served as the sole carbon source for the growth of Pseudomonas putida, enabling the conversion of the depolymerized plastics into biodegradable polyhydroxyalkanoates (PHAs). In comparison to conventional commercial PHAs, the estimated optimal production cost and carbon footprint are reduced by 62% and 29%, respectively.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗