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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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DOE OSTI2025

Upstreamness and downstreamness in input–output analysis from local and aggregate information

Abstract Ranking sectors and countries within global value chains is of paramount importance to estimate risks and forecast growth in large economies. However, this task is often non-trivial due to the lack of complete and accurate information on the flows of money and goods between sectors and countries, which are encoded in input–output (I–O) tables. In this work, we show that an accurate estimation of the role played by sectors and countries in supply chain networks can be achieved without full knowledge of the I–O tables, but only relying on local and aggregate information, e.g., the total intermediate demand per sector. Our method, based on a rank-1 approximation to the I–O table, shows consistently good performance in reconstructing rankings (i.e., upstreamness and downstreamness measures for countries and sectors) when tested on empirical data from the world input–output database. Moreover, we connect the accuracy of our approximate framework with the spectral properties of the I–O tables, which ordinarily exhibit relatively large spectral gaps. Our approach provides a fast and analytically tractable framework to rank constituents of a complex economy without the need of matrix inversions and the knowledge of finer intersectorial details.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Supramolecular Self-Assembled Multi-Electron-Acceptor Organic Molecule as High-Performance Cathode Material for Li-Ion Batteries

Organic electrode materials possess many advantages such as low toxicity, sustainability, and chemical/structural tunability toward high energy density. However, to compete with inorganic-based compounds, crucial aspects such as redox potential, capacity, cycling stability, and electronic conductivity need to be improved. Herein, a comprehensive strategy on the molecular design of small organic electron-acceptor-molecule—hexaazatrianthranylene (HATA) embedded quinone (HATAQ) is reported. By introducing conjugated quinone moieties into the electron-deficient hexaazatriphenylene-derivative core, HATAQ with highly extended π-conjugation can yield extra-high capacity for lithium storage, delivering a capacity of 426 mAh g -1 at 200 mA g -1 (0.4C). At an extremely high rate of 10 A g -1 (19C), a reversible capacity of 209 mAh g -1 corresponding to nearly 85% retention is obtained after 1000 cycles. A unique network of unconventional lock-and-key hydrogen bonds in the solid-state facilitates favorable supramolecular 2D layered arrangement, enhancing cycling stability. To the best of the authors’ knowledge, the capacity and rate capability of HATAQ are found to be the best ever reported for organic small-molecule-based cathodes. Finally, these results together with density functional theory studies provide proof-of-concept that the design strategy is promising for the development of organic electrodes with exceptionally high energy density, rate capability, and cycling stability.

25 ENERGY STORAGE↗
DOE OSTI2022

Degradation of Edge-Defined Film-Fed Silicon Glass–Glass Modules on Florida Rooftop After 22 Years

Thin silicon (< 100 μm) adoption can provide significantly lower cost. Kerfless technologies provide thin wafers while preventing material from being wasted. Understanding how this family of processing influences module reliability is important. A set of four edge-defined film-fed growth (EFG) silicon modules from a ten-module system in Florida is measured after 22 years of exposure. Power loss rates of 0.58–0.78% year -1 are measured for three modules, while a rate of 1.32% is measured for a module with severe delamination and corrosion. Short-circuit current degrades between 10.7% and 13.1% for all modules. Further, the losses are primarily optical and recombination based; however, series and shunt resistance effects play a non-negligible role. Pre-existing recombination losses exist but are exacerbated via degradation. Optical losses in short-circuit current are due in part to encapsulant yellowing. Electroluminescence (EL) images display the effects of processing on bulk quality by clearly showing lines of alternating brightness along the wafer length. To the authors’ knowledge, this is the first article displaying suns–V OC data, effective lifetime versus excess carrier density data, and module EL images for EFG silicon-based modules.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Application of Sequential Design of Experiments (SDoE) to Large Pilot-Scale Solvent-Based CO2 Capture Process at Technology Centre Mongstad (TCM)

The United States Department of Energy’s Carbon Capture Simulation for Industry Impact (CCSI2) program has developed a framework for sequential design of experiments (SDoE) that aims to maximize knowledge gained from budget- and schedule-limited pilot scale testing. SDoE was applied to the planning and execution of campaigns for testing CO2 capture systems at pilot-scale in order to optimally allocate resources available for the testing. In this methodology, a stochastic process model is developed by quantifying the parametric uncertainty in submodels of interest; for a solvent-based CO2 capture system, these may include physical properties and equipment performance submodels (e.g., mass transfer, interfacial area). This uncertainty is propagated through the full process model, over variable operating conditions, for estimating the resulting uncertainty in key model outputs (e.g., percentage of CO2 capture, solvent regeneration energy requirement). In developing a data collection plan, the predicted output uncertainty is incorporated into an algorithm that seeks simultaneously to select process operating conditions for which the predicted uncertainty is relatively high and to ensure that the entire space of operation is well represented. This test plan is then used to guide operation of the pilot plant at varying steady-state conditions, with resulting process data incorporated into the existing model using Bayesian inference to refine parameter distributions. The updated stochastic model, with reduced parametric uncertainty from data collected, is then used to guide additional data collection, thus the sequential nature of the experimental design. The SDoE process was implemented at the pilot test unit (12 MWe in scale) at Norway’s Technology Centre Mongstad (TCM) in a summer 2018 test campaign with aqueous monoethanolamine (MEA). During the test campaign, the varied operating conditions included the flowrates of circulated solvent, flue gas, and reboiler steam and the CO2 concentration in the flue gas. The process data were used to update probability distributions of mass transfer and interfacial area parameters of a stochastic process model developed by the CCSI2 team. Two iterations of the SDoE process were executed, resulting in the uncertainty in model predicted CO2 capture percentage decreasing by an average of 58.0 ± 4.7% over the full input space of interest. This work demonstrates the potential of the SDoE process for model refinement through reduction in process model parametric uncertainty, and ultimately risk in scale-up, in CO2 capture technology performance.

carbon capture↗
DOE OSTI2021

Interpreting the Lipidome: Bioinformatic Approaches to Embrace the Complexity

Background Improvements in mass spectrometry (MS) technologies coupled with bioinformatics developments have allowed considerable advancement in the measurement and interpretation of lipidomics data in recent years. Since research areas employing lipidomics are rapidly increasing, there is a great need for bioinformatic tools that capture and utilize the complexity of the data. Currently, the diversity and complexity within the lipidome is often concealed by summing over or averaging individual lipids up to (sub)class-based descriptors, losing valuable information about biological function and interactions with other distinct lipids molecules, proteins and/or metabolites. Aim of review To address this gap in knowledge, novel bioinformatics methods are needed to improve identification, quantification, integration and interpretation of lipidomics data. The purpose of this mini-review is to summarize exemplary methods to explore the complexity of the lipidome. Key scientific concepts of review Here we describe six approaches that capture three core focus areas for lipidomics: (1) lipidome annotation including a resolvable database identifier, (2) interpretation via pathway- and enrichment-based methods, and (3) understanding complex interactions to emphasize specific steps in the analytical process and highlight challenges in analyses associated with the complexity of lipidome data.

Kyle, Jennifer E.↗
DOE OSTI2024

HDBind: encoding of molecular structure with hyperdimensional binary representations

Traditional methods for identifying “hit” molecules from a large collection of potential drug-like candidates rely on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug and its protein target. These approaches have a significant limitation in that they require exceptional computing capabilities for even relatively small collections of molecules. Increasingly large and complex state-of-the-art deep learning approaches have gained popularity with the promise to improve the productivity of drug design, notorious for its numerous failures. However, as deep learning models increase in their size and complexity, their acceleration at the hardware level becomes more challenging. Hyperdimensional Computing (HDC) has recently gained attention in the computer hardware community due to its algorithmic simplicity relative to deep learning approaches. The HDC learning paradigm, which represents data with high-dimension binary vectors, allows the use of low-precision binary vector arithmetic to create models of the data that can be learned without the need for the gradient-based optimization required in many conventional machine learning and deep learning methods. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated in a range of application areas (computer vision, bioinformatics, mass spectrometery, remote sensing, edge devices, etc.). To the best of our knowledge, our work is the first to consider HDC for the task of fast and efficient screening of modern drug-like compound libraries. We also propose the first HDC graph-based encoding methods for molecular data, demonstrating consistent and substantial improvement over previous work. We compare our approaches to alternative approaches on the well-studied MoleculeNet dataset and the recently proposed LIT-PCBA dataset derived from high quality PubChem assays. We demonstrate our methods on multiple target hardware platforms, including Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs), showing at least an order of magnitude improvement in energy efficiency versus even our smallest neural network baseline model with a single hidden layer. Our work thus motivates further investigation into molecular representation learning to develop ultra-efficient pre-screening tools. We make our code publicly available at https://github.com/LLNL/hdbind.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Machine learning-based discovery of molecular descriptors that control polymer gas permeation

While machine learning has found increasing use in predicting the properties of polymeric materials with only a knowledge of chain architecture, determining the molecular factors underpinning properties (“interpretable AI”) has remained less well explored. We show that encoding chain chemistry in commonly employed formats, e.g., binary-valued fingerprints, leads to uniqueness issues during the hashing process to save storage space. This is because the hashing algorithm can map several chemical moieties into the same bit. These issues carry over into the ML algorithms, especially for “inverse” design and interpretable AI, and cannot be avoided by changing the length of the fingerprint. Using MACCS key featurizations of monomer repeats resolves some of these issues, and we show that a few substructures consistently appear in top features for maximizing permeability across several gases and ML models. These are carbon–carbon double bonds (as in polyacetylenes) especially when they are associated with methyl groups (found in branching architectures). Here these results, derived from the limited data set of ~ 500 polymers with experimental gas permeation data, are in agreement with physical insight and thus provide a robust foundation which could further enable study of these material classes through detailed experiments and simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Guide de base pour l'energie solaire hors reseau en Haiti

This training lends insight into key information and concepts for off-grid solar in Haiti, building foundational knowledge and an ability to catalyze interest in off-grid solar for rural electrification in Haiti. This self-paced course is offered in both English and French and covers a variety of topics related to energy access in Haiti including off-grid solar products, market potential in Haiti, supply and demand side considerations, system design, installation and maintenance, off-grid solar business models, financial modeling, gender and energy access, productive use of energy, and climate adaptation. The training is intended for university students, government partners, NGOs, funders, development partners, professionals, and more. No prior knowledge of off-grid solar or Haiti is required to benefit from this training. See NREL/PR-7A40-88999 for the English translation of this document.

ENERGY PLANNING, POLICY, AND ECONOMY,SOLAR ENERGY↗
DOE OSTI2021

Intervening alcohol marketing to reduce harmful alcohol use and lessons learned from the theory of changes: Case studies in Thailand

Objectives Globally, the burden of disease caused by alcohol use has been steadily increasing, including in Thailand. In this study, we aim to test the effectiveness of Anderson et al.‘s suggested three approaches to change the collective social norms, which comprise of: (1) providing information and an understanding about alcohol use behaviour, its causes and distribution; (2) focusing strategies on groups rather than individuals; and (3) strengthening supportive laws, regulations and approaches. Study design We employed a mixed-methods approach. Evidence was gathered from literature review and in-depth interviews with key individuals who are responsible for community-based interventions to alcohol marketing strategies in Thailand. Methods We chose to focus on two case studies in Nan and Surin provinces, where hospital-based longitudinal data (8 years) were available. Changes in casualties related to the harmful use of alcohol, resulting from interactions between community-based interventions and alcohol marketing during the time of annual festivals were investigated. We employed the theory of change (ToC) defined by Vogel to guide the data collection and analysis. We reviewed literature from online databases and grey literature to generate causal-loop diagrams. Results We created a causal-loop diagram to describe the complexity of harmful alcohol use, its related factors, context, interventions and outcomes. Over the decade between 2006 and 2015, community-based strategies led to a substantial reduction of casualties (initially a 50% reduction, rising to an 80–90% reduction by the end of the study period) during the time of the festivals. Conclusions The reduction in injuries and fatalities could be a result of the concerted actions, including legal sanctions of alcohol beverage sales and advertisement, and public education to raise awareness and impart knowledge of the harmful use of alcohol. The actions were organised by a coalition of civil society, health professionals, public authorities and community leaders using hospital-based data on the adverse effects of harmful alcohol use to mobilise political support at the provincial level. The availability of long-term financial support as a catalytic source of funds and the presence of a comprehensive alcohol control act enabled framing and mobilisation of local resources and political support.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Graph identification of proteins in tomograms ( GRIP‐Tomo )

Abstract In this study, we present a method of pattern mining based on network theory that enables the identification of protein structures or complexes from synthetic volume densities, without the knowledge of predefined templates or human biases for refinement. We hypothesized that the topological connectivity of protein structures is invariant, and they are distinctive for the purpose of protein identification from distorted data presented in volume densities. Three‐dimensional densities of a protein or a complex from simulated tomographic volumes were transformed into mathematical graphs as observables. We systematically introduced data distortion or defects such as missing fullness of data, the tumbling effect, and the missing wedge effect into the simulated volumes, and varied the distance cutoffs in pixels to capture the varying connectivity between the density cluster centroids in the presence of defects. A similarity score between the graphs from the simulated volumes and the graphs transformed from the physical protein structures in point data was calculated by comparing their network theory order parameters including node degrees, betweenness centrality, and graph densities. By capturing the essential topological features defining the heterogeneous morphologies of a network, we were able to accurately identify proteins and homo‐multimeric complexes from 10 topologically distinctive samples without realistic noise added. Our approach empowers future developments of tomogram processing by providing pattern mining with interpretability, to enable the classification of single‐domain protein native topologies as well as distinct single‐domain proteins from multimeric complexes within noisy volumes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

High-throughput measurements of CO 2 permeance and solubility in ionic liquid reveal a synergistic role of ionic interactions and void fractions

The factors that govern CO 2 solubility in ionic liquids (ILs) are of great interest for the development of new materials for CO 2 capture and utilization. The cationic functional group (i.e., imidazolium, pyrrolidinium, pyridinium, etc.), alkyl chain length of cation, degree of fluorination of anion, anion size, and the void fraction in IL are known to influence CO 2 solubility. However, a comprehensive explanation of how these factors collectively affect CO 2 solubility has not been developed yet. This knowledge gap is largely attributed to the lack of CO 2 solubility data for IL structures other than imidazolium based ILs. We report here an automated high-throughput (HT) setup for the measurement of CO 2 solubility in room-temperature ILs (RTILs) combining six different anions and nine different cations for a total of 19 different specific ranges of RTILs. The HT setup first dispenses up to 200 µL of RTILs in a 96-well microtiter plate and then utilizes a robotic arm to measure cyclic voltammogram (CV) in each well using maneuverable Ag electrodes. The Cottrell analysis of the CO 2 reduction CV peak provides a direct measurement of CO 2 permeance in RTILs, which yields Henry’s constant from the estimated diffusion coefficient of CO 2 . Henry’s constants thus obtained are in very good agreement with those reported earlier. The measured CO 2 permeance and Henry’s constant of all RTILs seem to follow a first-order dependence on void fraction and a second-order dependence on electrostatic interaction between anion and cation of IL, with some synergistic dependence on the product of a void fraction and electrostatic interaction, making them two important descriptors for the design of novel ILs.

CO2 Solubility↗
DOE OSTI2021

A new approach to study adsorption on shales and other microporous solids via the thermogravimetric analysis (TGA) technique

Measuring the adsorption of gases in microporous solids like the shales requires accurate knowledge of the solid’s skeletal volume. Helium (He) is commonly used to determine the sample’s skeletal volume based on the assumption that it does not adsorb in these porous media. The validity of such an assumption for microporous solids has been questioned in recent years, and in this study, we show that it is not applicable for the shale sample studied. We present a new method to measure the adsorption of shale-gas components in shales, which does not require the use of He to measure the solid’s skeletal volume. Since the proposed analysis method relies on the use of dynamic adsorption data, we also propose here a new zero-point correction method for the magnetic suspension balance, which is better suited for the analysis of such data. Here, we employ the new technique to the study of Argon adsorption in Marcellus Shales.

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

Thirdhand tobacco smoke exposure increases the genetic background-dependent risk of pan-tumor development in Collaborative Cross mice

Increasing evidence has shown that thirdhand smoke (THS) exposure is likely to induce adverse health effects. An important knowledge gap remains in our understanding of THS exposure related to cancer risk in the human population. Population-based animal models are useful and powerful in investigating the interplay between host genetics and THS exposure on cancer risk. Here, we used the Collaborative Cross (CC) mouse population-based model system, which recapitulates the genetic and phenotypic diversity observed in the human population, to assess cancer risk after a short period of exposure, between 4 and 9 weeks of age. Eight CC strains (CC001, CC019, CC026, CC036, CC037, CC041, CC042 and CC051) were included in our study. We quantified pan-tumor incidence, tumor burden per mouse, organ tumor spectrum and tumor-free survival until 18 months of age. At the population level, we observed a significantly increased pan-tumor incidence and tumor burden per mouse in THS-treated mice as compared to the control (p = 3.04E-06). Lung and liver tissues exhibited the largest risk of undergoing tumorigenesis after THS exposure. Tumor-free survival was significantly reduced in THS-treated mice compared to control (p = 0.044). At the individual strain level, we observed a large variation in tumor incidence across the 8 CC strains. CC036 and CC041 exhibited a significant increase in pan-tumor incidence (p = 0.0084 and p = 0.000066, respectively) after THS exposure compared to control. We conclude that early-life THS exposure increases tumor development in CC mice and that host genetic background plays an important role in individual susceptibility to THS-induced tumorigenesis. Genetic background is an important factor that should be taken into account when determining human cancer risk of THS exposure.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Carbon Coating Influence on the Formation of Percolating Electrode Networks for Silicon Anodes

Previous studies have demonstrated that chemical vapor deposition carbon coating on silicon (Si@C) can enhance the electrochemical performance of lithium-ion batteries with Si-based anodes. However, the underlying mechanisms contributing to this improvement have not been fully explored. Here, we address this knowledge gap by applying a suite of characterization methods to evaluate Si@C anodes prepared by reducing acetylene on ball-milled Si particles. Raman mapping measurements show that the C coating (<5 nm thick) enables a homogeneous Si and carbon distribution during the slurry casting process, thereby promoting Si utilization during cycling. The coating’s microstructure and morphology were evaluated using X-ray photoelectron spectroscopy (XPS), scanning transmission electron microscopy, and neutron reflectivity experiments. Electrochemical impedance spectroscopy measurements upon cycling indicate that carbon coating also reduces the overall resistance as benchmarked against bare Si anodes. Galvanostatic cycling in half-cell studies revealed higher initial Coulombic efficiency and specific capacities with increasing carbon coating time. However, solid electrolyte interphase (SEI) investigations using XPS showed that the coated and uncoated samples have very similar characteristics, suggesting that the SEI may only play a minor role in enhancing the performance of Si@C. Full-cell evaluation of the Si electrodes was consistent with half-cell results relating to performance and SEI properties, further supporting the conclusion that electronic and ionic percolation, enabled by effective electrode manufacturing, are the dominant factors contributing to the favorable performance of Si@C.

25 ENERGY STORAGE↗
DOE OSTI2019

Operational experience with the new CMS DAQ-Expert

The data acquisition (DAQ) system of the Compact Muon Solenoid (CMS) at CERN reads out the detector at the level-1 trigger accept rate of 100 kHz, assembles events with a bandwidth of 200 GB/s, provides these events to the high level-trigger running on a farm of about 30k cores and records the accepted events. Comprising custom-built and cutting edge commercial hardware and several 1000 instances of software applications, the DAQ system is complex in itself and failures cannot be completely excluded. Moreover, problems in the readout of the detectors,in the first level trigger system or in the high level trigger may provoke anomalous behaviour of the DAQ systemwhich sometimes cannot easily be differentiated from a problem in the DAQ system itself. In order to achieve high data taking efficiency with operators from the entire collaboration and without relying too heavily on the on-call experts, an expert system, the DAQ-Expert, has been developed that can pinpoint the source of most failures and give advice to the shift crew on how to recover in the quickest way. The DAQ-Expert constantly analyzes monitoring data from the DAQ system and the high level trigger by making use of logic modules written in Java that encapsulate the expert knowledge about potential operational problems. The results of the reasoning are presented to the operator in a web-based dashboard, may trigger sound alerts in the control room and are archived for post-mortem analysis - presented in a web-based timeline browser. We present the design of the DAQ-Expert and report on the operational experience since 2017, when it was first put into production.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Nuclear recoil response of liquid xenon and its impact on solar 8B neutrino and dark matter searches

Knowledge of the ionization and scintillation responses of liquid xenon (LXe) to nuclear recoils is crucial for LXe-based dark matter experiments. Current calibrations carry large uncertainties in the low-energy region below ∼3 keV nr where signals from dark matter particles of <10 GeV/c 2 masses are expected. The coherent elastic neutrino-nucleus scattering (CE ν NS) by solar 8 B neutrinos also results in a continuum of nuclear recoil events below 3.0 keV nr (99% of events), which further complicates low-mass dark matter searches in LXe experiments. In this paper, we describe a method to quantify the uncertainties of low-energy LXe responses using published calibration data, followed by case studies to evaluate the impact of yield uncertainties on 8 B searches and low-mass dark matter sensitivity in a typical ton-scale LXe experiment. We conclude that naively omitting yield uncertainties leads to overly optimistic limits by factor ∼2 for a 6 GeV WIMP mass. Future nuclear recoil light yield calibrations could allow experiments to recover this sensitivity and also improve the accuracy of solar 8 B flux measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Effect of Lateral Conduction in a Rectangular Fuel Plate and Optimum Stripe Widths for the OPAL Reactor

The impetus for this work is the Proliferation Resistance Optimization (PRO-X) program, which was created by the National Nuclear Security Administration (NNSA) of the US Department of Energy (DOE) to provide a framework for developing reactor designs that minimize the production of special nuclear materials while maximizing performance for peaceful uses. The Argentine company INVAP and Argonne National Laboratory collaborated to share knowledge and expertise regarding the thermal analysis of research reactors. Working together, they designed a series of test problems based on the Open Pool Australian Light Water (OPAL) reactor, whose fuel is in the form of flat plates cooled by water flowing through narrow rectangular channels. ANL used their PLTEMP/ANL code. INVAP used their TERMIC code (and its new multi-plate version, TERMIC-MP). Then, INVAP and ANL compared the two sets of results. 1-D models were used during the collaboration since for research reactor analysis, largely 1-D thermal-hydraulics models are commonly used because they are simple to apply and technically defendable. The collaboration between the two organizations aims for improvements in thermal-hydraulic models. Improvements to methods and models can reduce modeling uncertainties and excessive conservatism, allowing greater reactor performance without reducing predicted safety margins. Therefore, these improvements potentially expand the design space, making additional designs feasible, which, in turn, may achieve better fuel utilization and proliferation resistance in the designs. This report provides justification for the lateral node size (stripe width) chosen for the 1-D computer models during the collaboration. The lateral heat conduction in OPAL fuel plates is evaluated using the two-dimensional conduction capability of PLTEMP/ANL to determine a representative hot-stripe width that can be conservatively used for one-dimensional steady-state thermal-hydraulic analysis. Basically, a representative hot-stripe width needs to be selected so that the actual heat flux averaged over this stripe width is greater than or equal to the maximum heat flux obtained by the two-dimensional analysis. Detailed results in this report show that a hot-stripe width of 8.125 mm can be conservatively used for the one-dimensional thermal-hydraulics analysis of all plates. Therefore, eight equal stripes (8.125 mm each) are recommended to be modeled in the MCNP and PLTEMP/ANL full core models of the OPAL reactor.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Beyond Pristine Metal–Organic Frameworks: Preparation of Hollow MOFs and Their Composites for Catalysis, Sensing, and Adsorption Removal Applications

Metal–organic frameworks (MOFs) have been broadly applied to numerous domains with a substantial surface area, tunable pore size, and multiple unsaturated metal sites. Recently, hollow MOFs have greatly attracted the scientific community due to their internal cavities and gradient pore structures. Hollow MOFs have a higher tunability, faster mass-transfer rates, and more accessible active sites when compared to traditional, solid MOFs. Hollow MOFs are also considered to be candidates for some functional material carriers. For example, composite materials such as hollow MOFs and metal nanoparticles, metal oxides, and enzymes have been prepared. These composite materials integrate the characteristics of hollow MOFs with functional materials and are broadly used in many aspects. This review describes the preparation strategies of hollow MOFs and their composites as well as their applications in organic catalysis, electrochemical sensing, and adsorption separation. Finally, we hope that this review provides meaningful knowledge about hollow-MOF composites and their derivatives and offers many valuable references to develop hollow-MOF-based applied materials.

36 MATERIALS SCIENCE↗