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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Enhancing the Chemical Energy Flux in a High-Temperature Tubular Counterflow Solid Fuel Synthesis Reactor Using a Bypass

Redox reactions of metal oxides offer a path towards using intermittent renewable resources for high-density thermochemical energy storage. Thermochemical energy storage often involves the flow of a particulate media. We describe a novel method to increase the throughput in a gravity-driven high-temperature thermochemical storage reactor flowing pelletized MgMnO. The moving bed reactor operates under counter-flow conditions and encounters particle flowability problems at temperatures of 1500 °C leading to sintering of the bed. Inertial forces of a counter-flowing gas can overcome the gravitational forces on the particles and limit the chemical energy storage rate of the reactor. We found that the insertion of a gas bypass (a slotted tube) into the reactor results in a 100% increase of the flow rates and achieved a 50% higher chemical energy storage flux compared to the operation without a bypass tube while mitigating the effects of sintering on the particles. As a result, the higher solid flow rates require a longer heated zone to reach a comparable residence time and extent of reduction compared to the lower flow rates.

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

Advancing energy storage through solubility prediction: leveraging the potential of deep learning

Solubility prediction plays a crucial role in energy storage applications, such as redox flow batteries, because it directly affects the efficiency and reliability. Researchers have developed various methods that utilize quantum calculations and descriptors to predict the aqueous solubilities of organic molecules. Notably, machine learning models based on descriptors have shown promise for solubility prediction. As deep learning tools, graph neural networks (GNNs) have emerged to capture complex structure–property relationships for material property prediction. Specifically, MolGAT, a type of GNN model, was designed to incorporate n-dimensional edge attributes, enabling the modeling of intricacies in molecular graphs and enhancing the prediction capabilities. In a previous study, MolGAT successfully screened 23 467 promising redox-active molecules from a database of over 500 000 compounds, based on redox potential predictions. This study focused on applying the MolGAT model to predict the aqueous solubility (log S) of a broad range of organic compounds, including those previously screened for redox activity. The model was trained on a diverse sample of 8494 organic molecules from AqSolDB and benchmarked against literature data, demonstrating superior accuracy compared with other state of the art graph-based and descriptor-based models. Subsequently, the trained MolGAT model was employed to screen redox-active organic compounds identified in the first phase of high-throughput virtual screening, targeting favorable solubility in energy storage applications. The second round of screening, which considered solubility, yielded 12 332 promising redox-active and soluble organic molecules suitable for use in aqueous redox flow batteries. Thus, the two-phase high-throughput virtual screening approach utilizing MolGAT, specifically trained for redox potential and solubility, is an effective strategy for selecting suitable intrinsically soluble redox-active molecules from extensive databases, potentially advancing energy storage through reliable material development. This indicates that the model is reliable for predicting the solubility of various molecules and provides valuable insights for energy storage, pharmaceutical, environmental, and chemical applications.

25 ENERGY STORAGE↗
DOE OSTI2026

System-of-systems optimization of hydrogen infrastructure for heavy-duty freight corridors: The interstate 10 case study

Medium and heavy-duty freight transportation requires hydrogen energy infrastructure that is cost-effective, operationally reliable, spatially coherent, and resilient to demand variability along major corridors. This paper presents an integrated hydrogen corridor planning framework using Oak Ridge National Laboratory's OR-AGENT that couples freight-driven, route-resolved hydrogen demand modeling with optimized station siting, sizing, and station-level techno-economic analysis. The framework is demonstrated for the Interstate 10 freight corridor and the Houston-to-Los-Angeles region. Hydrogen demand is derived from high-resolution origin–destination freight data, duty-cycle characterization, and physics-based energy consumption modeling. Candidate refueling sites are selected from existing heavy-duty diesel fueling locations and optimized subject to onboard storage and station capacity constraints. Resulting station throughputs are evaluated using established techno-economic models for electrolytic hydrogen production and dispensing infrastructure. Results show that a regional, portfolio-level aggregation, average dispensed electrolytic hydrogen cost of $6.87–$7.26/kg is currently feasible, and is strongly influenced by demand density and utilization.

Sujan, Vivek [ORNL] (ORCID:0000000269882342)↗
DOE OSTI2021

Screening of alkali-assisted storage conditions to define the operational window of deacetylation within storage systems in the bioenergy supply chain

Overcoming biomass heterogeneity and associated recalcitrance to thermal, chemical, and enzymatic depolymerization is a necessary but challenging aspect of valorizing lignocellulosic biomass to fuels and chemicals. Here, this study explores how this recalcitrance can be reduced during the supply chain unit operation of storage, which is required for seasonally harvested agricultural residues to maintain constant throughput at a biorefinery. In this work, partial alkali pretreatment was performed just prior to corn stover entering storage, which had the benefit of providing a high pH environment entering storage such that soluble sugars were preserved and saponified acetyl groups in hemicellulose. This work investigated a range of viable conditions where saponification and preservation occur simultaneously by varying the moisture content (40% and 60%) and concentrations of sodium hydroxide (low and high) during aerobic and anaerobic storage. Anaerobic conditions preserved overall dry matter below 5% in the three scenarios evaluated, and the highest alkali loading solubilized up to 15% lignin, 18% xylan, and 50% of acetate, meanwhile doubling the extractable components. Scanning electron microscopy images highlighted potential physical impacts including cell-wall disruption near vascular bundles, pitting within parenchyma cells, and cell-wall distortion. Techno-economic assessment indicated that this storage approach and associated logistics system is economically competitive with a conventional approach using low-moisture bales.

09 BIOMASS FUELS↗
DOE OSTI2023

High Throughput Laser Processing for Enhanced Battery Performance and Manufacturing

For widespread adoption of electric vehicles, lithium-ion batteries (LiBs) need to achieve energy densities of >275 Wh/kg, cell costs less than $100/kWh, and charge to more than 80% capacity within 15 minutes. Recently, it has been shown that introducing a secondary pore network in thick battery electrodes enables higher extracted capacity without a severe reduction in charge/discharge rate performance. Secondary pore networks consisting of ordered arrays of microstructures reduce tortuosity of Li+-ion transport deep into the thick electrodes, diminishing detrimental concentration gradients within the cell. Ultrafast (femtosecond)-pulsed laser ablation is a promising method to introduce such micro pores or channels in thick battery electrodes as it allows for precise control of pattern geometries, results in minimal damage to the electrode, and can be introduced into existing roll-to-roll electrode manufacturing lines. We have explored laser patterning to create secondary pore networks in a variety of battery electrode materials for both anode and cathode applications and characterized the laser-material interaction parameters. In addition, advanced materials characterization techniques (SEM-EDS, XRD) were used to explore whether ultrafast laser ablation affects the remaining electrode materials' morphology and structure. Furthermore, we have translated lab-scale laser ablation to high-throughput roll-to-roll processing at industrially relevant scales. Finally, we worked with Argonne National Laboratory, developers of the battery cost-analysis package BatPac, to estimate the additional manufacturing costs (CapEx and OpEx) for incorporation of laser-ablation processing to current battery electrode manufacturing. We estimate that ultrafast laser processing will add only -$1kWh, which for considerable improvements in cell performance, creates a compelling case for industry adoption.

battery↗
DOE OSTI2022

Data reduction through optimized scalar quantization for more compact neural networks

Raw data generation for several existing and planned large physics experiments now exceeds TB/s rates, generating untenable data sets in very little time. Those data often demonstrate high dimensionality while containing limited information. Meanwhile, Machine Learning algorithms are now becoming an essential part of data processing and data analysis. Those algorithms can be used offline for post processing and post data analysis, or they can be used online for real time processing providing ultra low latency experiment monitoring. Both use cases would benefit from data throughput reduction while preserving relevant information: one by reducing the offline storage requirements by several orders of magnitude and the other by allowing ultra fast online inferencing with low complexity Machine Learning models. Moreover, reducing the data source throughput also reduces material cost, power and data management requirements. In this work we demonstrate optimized nonuniform scalar quantization for data source reduction. This data reduction allows lower dimensional representations while preserving the relevant information of the data, thus enabling high accuracy Tiny Machine Learning classifier models for online fast inferences. We demonstrate this approach with an initial proof of concept targeting the CookieBox, an array of electron spectrometers used for angular streaking, that was developed for LCLS-II as an online beam diagnostic tool. We used the Lloyd-Max algorithm with the CookieBox dataset to design an optimized nonuniform scalar quantizer. Optimized quantization lets us reduce input data volume by 69% with no significant impact on inference accuracy. When we tolerate a 2% loss on inference accuracy, we achieved 81% of input data reduction. Finally, the change from a 7-bit to a 3-bit input data quantization reduces our neural network size by 38%.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Higher Ion Selectivity with Lower Energy Usage Promoted by Electro-osmotic Flow in the Transport through Conical Nanopores

The tradeoff between selectivity and throughput presents fundamental challenges to improve desalination and charge storage, salinity gradient-based energy harvesting, memory device/circuit development, and so forth. The well-known ion-current rectification and the recently resolved time-dependent transport hysteresis in conical nanopores or asymmetric nanointerfaces offer new opportunities for the selective transport of matter. This report shows that electro-osmotic flow (EOF) is an overlooked factor that increases ion selectivity while maintaining enhanced transport throughput in rectified nanoscale electrokinetic transport. The increased selectivity originates primarily from the suppression of anion flux by the fluid flow in the opposite direction under the applied electrical field. By solving the Poisson and Nernst–Planck (PNP) equations without and with coupled Navier–Stokes (PNP–NS) equations, the EOF effects on cation and anion transport are unequivocally revealed in asymmetric nanopipettes. The flux of cations and anions as well as the transference number and flow velocity are elucidated using the models and boundary conditions validated by previous experiments. A dimensionless parameter, radius over the Debye length, reveals optimal ion selectivity and energy cost at intermediate ion concentrations and nanopore sizes, up to hundreds of millimolars and tens of nanometers under time-dependent potential stimulus. Furthermore, the fundamental insights into EOF at nanointerfaces suggest new routes/strategies for better separation, analysis, and energy applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Automated, reliable, and efficient continental-scale replication of 7.3 petabytes of computational simulation data: A case study

We report on our experiences replicating 7.3 petabytes (PB) of Earth System Grid Federation (ESGF) computational simulation data from Lawrence Livermore National Laboratory (LLNL) in California to Argonne National Laboratory (ANL) in Illinois and Oak Ridge National Laboratory (ORNL) in Tennessee—a task motivated by a need for increased reliability, capacity, and performance. This task presented significant challenges: the need to move 29 million files twice under time pressure from aging storage hardware; a source file system bottleneck limiting throughput to 1.5 GB/s; frequent site maintenance windows; and the need for complete reliability at scale. We addressed these challenges using a simple replication tool that invoked Globus to transfer large bundles of files while tracking progress in a database, dynamically rerouting transfers to work around maintenance periods and file system limitations. Under the covers, Globus organized transfers to make efficient use of the high-speed Energy Sciences network (ESnet) and the data transfer nodes deployed at participating sites, and also addressed security, integrity checking, and recovery from a variety of transient failures. This success demonstrates the considerable benefits that can accrue from the adoption of performant data replication infrastructure. The replication tool is available at https://github.com/esgf2-us/data-replication-tools.

Globus↗
DOE OSTI2023

High-Throughput Electrochemical Characterization of Aqueous Organic Redox Flow Battery Active Material

The development of redox-active organics for flow batteries providing long discharge duration energy storage requires an accurate understanding of molecular lifetimes. Herein we report the development of a high-throughput setup for the cycling of redox flow batteries. Using common negolyte redox-active aqueous organics, we benchmark capacity fade rates and compare variations in measured cycling behavior of nominally identical volumetrically unbalanced compositionally symmetric cells. We propose figures of merit for consideration when cycling sets of identical cells, and compare three common electrochemical cycling protocols typically used in battery cycling: constant current, constant current followed by constant voltage, and constant voltage. Redox-active organics exhibiting either high or low capacity fade rates are employed in the cell cycling protocol comparison, with results analyzed from over 50 flow cells.

Electrochemistry↗
DOE OSTI2025

Structure and Synthesizability of Iron–Sulfur Metal–Organic Frameworks

Sulfur-based metal–organic frameworks (MOFs) and coordination polymers (CPs) are an emerging class of hybrid materials that have received growing attention due to their magnetic, conductive, and catalytic properties with potential applications in electrocatalysis and energy storage. In this work, we report a high-throughput virtual screening protocol to predict the synthesizability of candidate metal–sulfur MOFs/CPs by computing the thermodynamically stable structures resulting from a particular combination of metal cluster, linker, cation, and synthetic conditions. Free energies are computed by using all-atom classical mechanical thermodynamic integration. Low-free-energy structures are refined using ab initio density functional theory, and pair distribution functions and powder X-ray diffraction patterns are calculated to complement and guide experimental structure determination. We validate the computational approach by retrospective predictions of the stable structure produced by experimental syntheses, and a subsequent screen predicts Fe 4 S 4 -BDT–TPP as a new thermodynamically stable one-dimensional (1D) CP comprising a redox-active Fe 4 S 4 cluster, a 1,4-benzenedithiolate (BDT) linker, and a tetraphenylphosphonium (TPP) countercation. Furthermore, this material is experimentally synthesized, and the 1D chain structure of the crystal is confirmed using microcrystal electron diffraction. The computational screening pipeline is generically transferable to neutral and ionic MOFs/CPs comprising arbitrary metal clusters, linkers, cations, and synthetic conditions, and we make it freely available as an open source tool to guide and accelerate the discovery and engineering of novel porous materials.

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

Uncovering novel liquid organic hydrogen carriers: a systematic exploration of chemical compound space using cheminformatics and quantum chemical methods

We present a comprehensive, in silico-based discovery approach to identifying novel liquid organic hydrogen carrier (LOHC) candidates using cheminformatics methods and quantum chemical calculations. We screened over 160 billion molecules from ZINC15 and GDB-17 chemical databases for structural similarity to known LOHCs and employed a data-driven selection criterion connecting molecular features with dehydrogenation enthalpy. This scoring criterion effectively predicts dehydrogenation enthalpies from SMILES strings, streamlining the LOHC screening process. After rigorous screening and down-selection, we compiled a database of 3000 dehydrogenation reactions for the most promising LOHC candidates, setting the stage for future selection based on kinetics and catalysis. This work demonstrates the significant impact of integrating quantum chemistry and cheminformatics in materials discovery, accelerating the selection process while reducing experimental efforts and time. By proposing new molecules as prospective LOHC candidates, our study provides a valuable resource for researchers and engineers in the development of advanced LOHC systems and showcases a successful approach for high-throughput discovery, contributing to more efficient and sustainable energy storage solutions.

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

Integrated Process Optimization for Biochemical Conversion

This research is motivated by the challenges faced during biomass processing in bioenergy plants. It has been observed that variations in biomass characteristics, such as moisture, ash, and carbohydrate contents cause variations in feeding of the system which led to underutilization of equipment and the reactor. The objective of this research is to ensure a continuous flow of biomass to the reactor in plants that use the biochemical conversion process to generate liquid fuels. The overall goal is to lower the cost of producing biofuels, which could lead to improving US’s energy independency and growing US’s rural economy. The research team developed analytical models, such as discrete element method (DEM) models and mathematical models. The DEM models are unit-level models that explicitly capture biomass characteristics and quantify the impacts of biomass characteristics on bulk material properties and the performance of specific equipment. The mathematical models are system-level models that capture the impacts of system infeed rate, equipment processing rate, storage location and capacity, and biomass characteristics on system throughput. The functional relations predicting the bulk material properties from DEM models are incorporated to the mathematical models. The models developed were validated and evaluated using data collected at Idaho National Laboratory’s biomass processing facility. Via these models, we identified process control strategies that ensure a continuous flow of biomass to the reactor, while meeting the requirements of biochemical conversion process. Our analysis indicates that sequencing of biomass bales based on moisture level, and carbohydrate contents could have a positive impact on reducing processing time and inventory level and increasing throughput rate. Short bale sequences that repeat frequently, seem to have the greatest impact on improving system’s performance. Based on our experiments, the total annual system operating costs reduced by 20-30%, and the maximum inventory level reduced by 3 to 4 times. The operating costs include the annual equipment amortization cost and processing cost. The implementation of the models developed requires the use of standardized bale format, Radio Frequency Identification technology, sensing and real time monitoring of material attributes, automated material handling equipment, and automated process control. The scope of the model proposed can be extended to include the whole supply chain. The supply chain models help identify how many bales of different biomass feedstock to purchase given biomass availability in the region, biomass price and quality, and the biomass processing capabilities of the biorefinery. Thus, the outcomes of supply chain models can be used to inform the design of long-term contracts among farmers and the biorefinery.

09 BIOMASS FUELS↗
DOE OSTI2022

Viability of S3 Object Storage for the ASC Program at Sandia

Recent efforts at Sandia such as DataSEA are creating search engines that enable analysts to query the institution’s massive archive of simulation and experiment data. The benefit of this work is that analysts will be able to retrieve all historical information about a system component that the institution has amassed over the years and make better-informed decisions in current work. As DataSEA gains momentum, it faces multiple technical challenges relating to capacity storage. From a raw capacity perspective, data producers will rapidly overwhelm the system with massive amounts of data. From an accessibility perspective, analysts will expect to be able to retrieve any portion of the bulk data, from any system on the enterprise network. Sandia’s Institutional Computing is mitigating storage problems at the enterprise level by procuring new capacity storage systems that can be accessed from anywhere on the enterprise network. These systems use the simple storage service, or S3, API for data transfers. While S3 uses objects instead of files, users can access it from their desktops or Sandia’s high-performance computing (HPC) platforms. S3 is particularly well suited for bulk storage in DataSEA, as datasets can be decomposed into object that can be referenced and retrieved individually, as needed by an analyst. In this report we describe our experiences working with S3 storage and provide information about how developers can leverage Sandia’s current systems. We present performance results from two sets of experiments. First, we measure S3 throughput when exchanging data between four different HPC platforms and two different enterprise S3 storage systems on the Sandia Restricted Network (SRN). Second, we measure the performance of S3 when communicating with a custom-built Ceph storage system that was constructed from HPC components. Overall, while S3 storage is significantly slower than traditional HPC storage, it provides significant accessibility benefits that will be valuable for archiving and exploiting historical data. There are multiple opportunities that arise from this work, including enhancing DataSEA to leverage S3 for bulk storage and adding native S3 support to Sandia’s IOSS library.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

Assessment of Spoilage Microbiota of Rainbow Trout (Oncorhynchus mykiss) during Storage by 16S rDNA Sequencing

Due to the high contents of protein and fat in rainbow trout, it is highly susceptible to spoilage, which limits the storage and transportation processes. Exploring the spoilage microbial community during rainbow trout storage is essential to develop an effective preservation method. Here, the changes in the total bacterial colony and total volatile base nitrogen (TVB-N) during the storage of rainbow trout were investigated. Storage at 0 °C can effectively slow down the spoilage process with bacterial counts and TVB-N contents decreased from 8.7 log CFU/g and 18.7 mg/100 g obtained at 4 °C to 5.6 log CFU/g and 14.5 mg/100 g, respectively. 16S rDNA high-throughput sequencing results showed that the diversity of microbial genera decreased during storage. Acinetobacter, Pseudomonas, and Shewanells gradually became the dominant spoilage genera with contents of 59.9%, 18.6%, and 1.7%, respectively, in the late stage of storage. The spoilage abilities of bacteria belonging to the Pseudomonas and Shewanells genera were analyzed. Shewanella sp. S5-52 showed the highest level of TVB-N content (100.6 mg/100 g) in sterile fish juice, indicating that it had a strong spoilage ability. This study confirmed the dominant spoilage bacterial genera and evaluated the spoilage abilities of isolated strains during the storage of rainbow trout, which laid the foundation for further investigation of the spoilage mechanism of rainbow trout and other aquatic products.

Du, Guangqing↗
DOE OSTI2025

What to Support When You’re Compressing

Over the last nearly 20 years, lossy compression has become an essential aspect of HPC applications’ data pipelines, allowing them to overcome limitations in storage capacity and bandwidth and, in some cases, increase computational throughput and capacity. However, with the adoption of lossy compression comes the requirement to assess and control the impact lossy compression has on scientific outcomes. In this work, we take a major step forward in describing the state of practice and by characterizing workloads. We examine applications’ needs and compressors’ capabilities across 9 different supercomputing application domains. We present 24 takeaways that provide best practices for applications, operational impacts for facilities achieving compressed data, and gaps in application needs not addressed by production compressors that point towards opportunities for future compression research.

Error-Bounded Lossy Compression↗
DOE OSTI2025

Machine learning-enabled discovery of ionic liquid–solvent electrolytes exhibiting high ionic conductivity

Ionic liquids (ILs), which are a class of materials with versatile nature and growing popularity, are facing impediments toward widespread usage as electrolytes due to various factors such as low ionic conductivity, high viscosity, high market price etc. One of the ways these limitations can be addressed is by mixing ILs with a molecular solvent. In a combinatorial sense, there exists an immense number of specific IL–solvent combinations. An exhaustive experimental or even simulation-based investigation of the chemical space spanned by such combinations can be extremely time-consuming, expensive, and nearly impossible. An alternative approach is to employ machine learning-based models developed from available databases. Although there exists prior literature that integrates machine learning to investigate mixtures of specific solvents with ILs, these models lack generalization necessitating development of a large number of ML models to handle various solvents. To remedy this shortcoming, as a part of designing green electrolytes with high ionic conductivity that can have potential applications in next-generation batteries and solar cells, this work aims to develop a unified machine learning model to predict ionic conductivity of any IL–solvent mixture system. In this regard, three models, namely, Random Forest, extreme gradient boosting (XGBoost), and artificial neural network (ANN) were formulated using the NIST ILThermo database. The dataset contained 549 unique ionic liquids from 16 cation families and 81 unique solvents, representing a total of 23 712 datapoints. SHAPLEY additive explanation (SHAP) method was used to assess the impact of various features on model prediction and their significance was compared with literature to gain physical insight about the model behavior. Finally, using the developed models, approximately 2.5 million IL–solvent mixtures at five different compositions were screened at room temperature. The high-throughput screening yielded nearly 19 000 IL–solvent mixtures for which ionic conductivity was found to exceed the ionic conductivity of conventional Li-ion battery electrolyte.

25 ENERGY STORAGE↗
DOE OSTI2023

Theory-Enabled High-Throughput Screening of Ion Dissociation Explains Conductivity Enhancements in Diluted Ionic Liquid Mixtures

The growing demand for room-temperature ionic liquids (RTILs) for energy applications necessitates the development of an efficient screening platform. Here, in this study, we successfully developed a fully automated high-throughput RTIL screening platform specifically designed for assessing ionic conductivity. By utilizing the 96 wells of a microtiter plate as individual electrolysis cells, we measured the ionic conductivity of 22 different RTILs, encompassing various combinations of cations and anions, and benchmarked the values with existing literature. We also employed the screening platform to investigate the conductivities of RTIL mixtures with a nonaqueous solvent, ethylene glycol (EG). Specific combinations of RTILs with EG result in approximately 200% enhancement in the conductivity values compared to the pure RTILs. To understand the underlying mechanisms responsible for this enhancement, we developed a theoretical framework for ionic conductivity that considers factors such as the degree of dissociation, viscous forces, and molal volume of the RTIL-EG mixtures. The optimized electrolyte mixture was then employed in the migration-assisted moisture gradient (MAMG) CO 2 capture process to study the effects of improved ionic conductivity on the energy efficiency of the process. Notably, the enhanced conductivity of the RTIL-EG mixture led to a nearly 50% reduction in energy consumption for capturing CO 2 . These outcomes highlight the effectiveness of our strategy in screening RTILs and improving existing processes. Moreover, this fully automated high-throughput setup, combined with the developed theoretical framework, provides a comprehensive platform for screening and studying RTIL mixtures with different solvents, enabling their application in various fields.

25 ENERGY STORAGE↗
DOE OSTI2023

Unraveling H2 chemisorption and physisorption on metal decorated graphene using quantum Monte Carlo

Molecular hydrogen has the potential to significantly reduce the use of carbon dioxide emitting energy processes. However, hydrogen gas storage is a major bottleneck for its large-scale use as current storage methods are energy intensive. Among different storage methods, physisorbing molecular hydrogen at ambient pressure and temperatures is a promising alternative—particularly in light of the advancements in tunable lightweight nanomaterials and high throughput screening methods. Nonetheless, understanding hydrogen adsorption in well-defined nanomaterials remains experimentally challenging and reference information is scarce despite the proliferation of works predicting hydrogen adsorption. We focus on Li, Na, Ca, and K, decorated graphene sheets as substrates for molecular hydrogen adsorption, and compute the most accurate adsorption energies available to date using quantum diffusion Monte Carlo (DMC). Building on our previous insights at the density functional theory (DFT) level, we find that a weak covalent chemisorption of molecular hydrogen, known as Kubas interaction, is feasible on Ca decorated graphene according to DMC, in agreement with DFT. This finding is in contrast to previous DMC predictions of the 4H2/Ca+ gas cluster (without graphene) where chemisorption is not favored. However, we find that the adsorption energy of hydrogen on metal decorated graphene according to a widely used DFT method is not fully consistent with DMC. The reference adsorption energies reported herein can be used to find better work-horse methods for application in large-scale modeling of hydrogen adsorption. Furthermore, the implications of this work affect strategies for finding suitable hydrogen storage materials and high-throughput methods.

Chemistry↗