Materials For Clean Energy Applications Research Articles

Recent Advances in the Preparation Methods of Magnesium-Based Hydrogen Storage Materials.

Magnesium-based hydrogen storage materials have garnered significant attention due to their high hydrogen storage capacity, abundance, and low cost. However, the slow kinetics and high desorption temperature of magnesium hydride hinder its practical application. Various preparation methods have been developed to improve the hydrogen storage properties of magnesium-based materials. This review comprehensively summarizes the recent advances in the preparation methods of magnesium-based hydrogen storage materials, including mechanical ball milling, methanol-wrapped chemical vapor deposition, plasma-assisted ball milling, organic ligand-assisted synthesis, and other emerging methods. The principles, processes, key parameters, and modification strategies of each method are discussed in detail, along with representative research cases. Furthermore, the advantages and disadvantages of different preparation methods are compared and evaluated, and their influence on hydrogen storage properties is analyzed. The practical application potential of these methods is also assessed, considering factors such as hydrogen storage performance, scalability, and cost-effectiveness. Finally, the existing challenges and future research directions in this field are outlined, emphasizing the need for further development of high-performance and cost-effective magnesium-based hydrogen storage materials for clean energy applications. This review provides valuable insights and references for researchers working on the development of advanced magnesium-based hydrogen storage technologies.

Advancing hydrogen storage predictions in metal-organic frameworks: A comparative study of LightGBM and random forest models with data enhancement

The escalating consumption of fossil fuels has given rise to a substantial upsurge in greenhouse gas concentrations and global temperatures, which, in turn, has triggered severe climate-related consequences. The critical imperative to reduce CO2 emissions and combat global warming has spurred extensive investigations into clean energy alternatives, with hydrogen emerging as a compelling zero-emission energy source. As a pivotal component of clean energy strategies, hydrogen requires designing compact, lightweight, and efficient storage systems. This study focuses on the development and evaluation of machine learning models for predicting the efficiency of Metal-Organic Frameworks (MOFs) in hydrogen storage, a key aspect of advancing clean energy technologies. MOFs, a class of nanoporous materials, show remarkable potential for hydrogen storage due to their high surface area and porosity. However, selecting the most suitable MOF for this application from a vast array of possible structures is a daunting task. In this context, machine learning algorithms offer an efficient alternative for predicting MOF suitability by considering their structural and chemical properties. We used ensemble learning methods, specifically Light Gradient Boosting Machine (LightGBM) and Random Forest (RF), to predict hydrogen uptake of MOFs based on a dataset of 219 experimentally tested samples. Two modeling scenarios were considered: one using the entire dataset, and the other involving strategic data pre-processing, including outlier removal and feature engineering. The results demonstrate that the measures taken to refine the dataset significantly enhance the predictive performance of the developed models, reducing prediction errors and improving overall goodness of fit. Specifically, the Mean Absolute Error (MAE) values for both the LightGBM and random forest models were reduced from 0.48 and 0.94, respectively, to 0.16 for both models, and the coefficients of determination (R2) increased substantially from 0.84 and 0.72 to 0.95, in both cases. Moreover, feature importance analysis unveiled that pressure-related features make the most significant contributions to the formation of tree ensembles during the model training process. A parametric sensitivity analysis was conducted revealing that H2 uptake in MOFs is most sensitive to changes in adsorption enthalpy, followed by surface area and temperature, while showing lower sensitivity to variations in pressure, consistent with established literature. These results underscore the pivotal role of data enhancement methods in refining machine learning models and can be instrumental in accelerating the development and optimization of MOF materials for clean energy applications.

Nanolaminated Ternary Transition Metal Carbide (MAX Phase)-Derived Core–Shell Structure Electrocatalysts for Hydrogen Evolution and Oxygen Evolution Reactions in Alkaline Electrolytes

The development of abundant, cheap, and highly active catalysts for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) is important for hydrogen production. Nanolaminate ternary transition metal carbides (MAX phases) and their derived two-dimensional transition metal carbides (MXenes) have attracted considerable interest for electrocatalyst applications. Herein, four new MAX@MXene core-shell structures (Ta2CoC@Ta2CTx, Ta2NiC@Ta2CTx, Nb2CoC@Nb2CTx, and Nb2NiC@Nb2CTx), in which the core region is Co/Ni-MAX phases while the edge region is MXenes, have been prepared. Under alkaline electrolyte conditions, the Ta2CoC@Ta2CTx core-shell structure showed an overpotential of 239 mV and excellent stability during the HER with MXenes as the active sites. For the OER, the Ta2CoC@Ta2CTx core-shell structure showed an overpotential of 373 mV and a small Tafel plot (56 mV dec-1), which maintained a bulk crystalline structure and generated Co-based oxyhydroxides that formed by surface reconstruction as active sites. Considering rich chemical compositions and structures of MAX phases, this work provides a new strategy for designing multifunctional electrocatalysts and also paves the way for further development of MAX phase-based materials for clean energy applications.

Part One - Advanced electronic materials for clean energy applications

Part One - Advanced electronic materials for clean energy applications

Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture.

Addressing climate change challenges by reducing greenhouse gas levels requires innovative adsorbent materials for clean energy applications. Recent progress in machine learning has stimulated technological breakthroughs in the discovery, design, and deployment of materials with potential for high-performance and low-cost clean energy applications. This review summarizes basic machine learning methods-data collection, featurization, model generation, and model evaluation-and reviews their use in the development of robust adsorbent materials. Key case studies are provided where these methods are used to accelerate adsorbent materials design and discovery, optimize synthesis conditions, and understand complex feature-property relationships. The review provides a concise resource for researchers wishing to use machine learning methods to rapidly develop effective adsorbent materials with a positive impact on the environment.

Computational screening of metal-organic frameworks with open copper sites for hydrogen purification

With the increasing demand for environmental protection worldwide, metal-organic frameworks (MOFs) have been pivotal in the clean energy domain. Due to the high surface areas, large porosities and structural tunability, they are promising for the adsorption separation of H2/CH4 mixtures. High-throughput computational screening was adopted to identify the optimal adsorbents for hydrogen purification from 502 MOFs with open copper sites. Firstly, the adsorption performance of H2/CH4 mixture in 440 MOFs, which exhibit non-zero surface area and over -3.8 Å largest cavity diameter (LCD), was calculated using grand canonical Monte Carlo (GCMC) simulations at 300 K and various pressures. Secondly, we identified the top 9 high-performance MOFs by evaluating the ranking of candidate adsorbent performance according to a combination metric of adsorption performance score (APS, the product of adsorption capacity of CH4 and selectivity of CH4 over H2) and percent regenerability (R%). PCN-39 and MOF-505 exhibit high APS of 101 mol kg−1 and 67.9 mol kg−1, respectively, promising for hydrogen purification. Subsequently, the breakthrough curves of H2/CH4 mixture through the fixed bed packed with some optimal MOFs were predicted to evaluate their effects in practical hydrogen purification. UMODEH08 or UMOBEF04 exhibits the long dimensionless residence time over 30 of CH4 for the H2/CH4 separation. Finally, we also explored the behaviors of the radial distribution functions (RDF) and adsorption equilibrium configurations to further demonstrate how the selected MOFs differentiate CH4 from H2. The investigation on all these observations at molecular level will pave the way for the development of new materials for clean energy applications.

Self-driving laboratory for accelerated discovery of thin-film materials.

Discovering and optimizing commercially viable materials for clean energy applications typically takes more than a decade. Self-driving laboratories that iteratively design, execute, and learn from materials science experiments in a fully autonomous loop present an opportunity to accelerate this research process. We report here a modular robotic platform driven by a model-based optimization algorithm capable of autonomously optimizing the optical and electronic properties of thin-film materials by modifying the film composition and processing conditions. We demonstrate the power of this platform by using it to maximize the hole mobility of organic hole transport materials commonly used in perovskite solar cells and consumer electronics. This demonstration highlights the possibilities of using autonomous laboratories to discover organic and inorganic materials relevant to materials sciences and clean energy technologies.

Designing for Manufacturing Scalability in Clean Energy Research

Kevin Huang is a postdoctoral associate in the Department of Materials Science and Engineering at the Massachusetts Institute of Technology (MIT). He studies methods for assessing and enhancing the manufacturing scalability of laboratory-derived clean energy technologies. He has a PhD from MIT, an MS from the University of Illinois, Urbana-Champaign, and a BS from Cornell University, all in materials science and engineering. Liang Li is a graduate student in the Technology and Policy Program at the Massachusetts Institute of Technology. Her current research is focused on the manufacturing scalability of materials for clean energy applications. She holds a bachelor's degree in chemical engineering from the National University of Singapore. Elsa Olivetti is the Atlantic Richfield Associate Professor of Energy Studies in the Department of Materials Science and Engineering at the Massachusetts Institute of Technology. Her research focuses on improving the environmental and economic sustainability of materials using methods informed by materials economics, machine learning, experimental inquiry, and techno-economic analysis. Dr. Olivetti received her BS degree in engineering science from the University of Virginia. Her PhD in materials science and engineering from MIT focused on the development of cathode materials for lithium ion batteries.

Direct carbonization of Zn/Co zeolitic imidazolate frameworks for efficient adsorption of Rhodamine B

Using zeolitic imidazolate frameworks (ZIFs) as precursors to yield highly nanoporous carbon materials for clean energy applications have received great attention in recent years, because of its diverse structure and dual characteristics from carbon and metals. To further explore the property and application of ZIFs-derived carbons, herein, bimetallic Zn/Co ZIFs were synthesized and used as self-sacrificial templates and carbon sources for the preparation of porous carbons. By direct carbonization at 900 °C, hierarchical porous carbon with magnetic properties was obtained in which highly dispersed Co nanoparticles were found. The as-prepared carbons were further used as adsorbents for the removal of Rhodamine B (RhB) from water. For comparison, ZIF-8- and ZIF-67-derived carbons were also synthesized, characterized and tested for RhB adsorption. The results show that Zn/Co ZIF-derived carbon exhibited the highest RhB adsorption capacity compared to ZIF-8- and ZIF-67-derived carbons, and it could be easily separated from water by an external magnetic field. The adsorption isotherm data conform to the Langmuir model, and the adsorption kinetic data obey a pseudo-second-order model. Moreover, Zn/Co ZIF-derived carbons can be regenerated by ethanol washing and reused at least 6 times without a significant loss in capacity.

Triptycene based microporous polymers (TMPs): Efficient small gas (H2 and CO2) storage and high CO2/N2 selectivity

In contemporary research, there is a growing interest in the development of novel porous materials that can be used as adsorbents for small gas molecules such as CO2 and H2. This is important from a perspective of more efficient energy alternative and a cleaner environment. Hyper-cross-linked polymers are a subclass of microporous organic polymers that are also reportedly known to efficiently adsorb H2 and CO2. In this context, triptycene based microporous polymers are known to exhibit high gas storage and separation properties. Herein, facile synthesis of a set of three triptycene based microporous polymers (TMPs) is described. TMPs were prepared by crosslinking triptycene monomers with various (bromomethyl)benzene units using AlCl3 catalyzed Friedel-Crafts alkylation reactions in high yield. Gas uptake experiments indicate that TMPs are microporous having moderately high surface areas (up to 1372 m2 g−1). Interestingly, TMP3 demonstrated very high reversible adsorption of molecular H2 (2.21% by mass at 1 bar/77 K) and it may be considered in the league of porous organic frameworks demonstrating outstanding H2 uptake at low pressure. Simultaneously, these polymers also show quite high CO2 uptake at 273 K and 1 bar (up to 223 mg g−1). Additionally TMPs have also exhibited very high CO2/N2 gas selectivity (up to 70). Considering the facile synthetic protocol of TMPs (mandatory for scale-up synthesis), large H2 and CO2 uptake and concurrent high CO2/N2 (up to 70) selectivity, these are potential materials for clean energy applications.

Vacancies in functional materials for clean energy storage and harvesting: the perfect imperfection.

Vacancies exist throughout nature and determine the physical properties of materials. By manipulating the density and distribution of vacancies, it is possible to influence their physical properties such as band-gap, conductivity, magnetism, etc. This can generate exciting applications in the fields of water treatment, energy storage, and physical devices such as resistance-change memories. In this review, we focus on recent progress in vacancy engineering for the design of materials for energy harvesting applications. A brief discription of the concept of vacancies, the way to create and control them, as well as their fundamental properties, is first provided. Then, emphasis is placed on the strategies used to tailor vacancies for metal-insulator transitions, electronic structures, and introducing magnetism in non-magnetic materials. Finally, we present representative applications of different structures with vacancies as active electrode materials of lithium or sodium ion batteries, catalysts for water splitting, and hydrogen evolution.

Molten Salt Assisted Self Assembly (MASA): Synthesis of Mesoporous Metal Titanate (CoTiO3, MnTiO3, and Li4Ti5O12) Thin Films and Monoliths

Mesoporous metal titanates are very important class of materials for clean energy applications, specifically transition metal titanates and lithium titanates. The molten salt assisted self-assembly (MASA) process offers a new synthetic route to produce mesoporous metal titanate thin films. The process is conducted as follows: first a clear solution that contains two solvents (namely the hydrated salt (Co(NO3)2·6H2O or Mn(NO3)2·6H2O, or LiNO3·xH2O, and ethanol), two surfactants (cethyltrimethylammonium bromide, CTAB, and 10-lauryl ether, C12EO10), an acid and titanium source (titanium tetrabutoxide, TTB) is prepared and then spin or spray coated over a substrate to form a thin or thick lyotropic liquid crystalline (LLC) film, respectively. Finally, the films are converted into transparent spongy mesoporous metal titanates by a fast calcination step. Three mesoporous metal titanates (namely, CoTiO3, MnTiO3, and Li4Ti5O12) have been successfully synthesized and structurally/thermally characterized using microscopy, spectroscopy, diffraction, and thermal techniques. The mesoporous cobalt and manganese titanates are stable up to 500 °C and collapse at around 550 °C into nanocrystalline Co3O4–TiO2 and Mn2O3–TiO2; however, lithium titanate is stable up to 550 °C and crystalline even at 350 °C. The crystallinity and pore size of these titanates can be adjusted by simply controlling the annealing and/or calcination temperatures.

ChemInform Abstract: Oxide‐ion and Proton Conducting Electrolyte Materials for Clean Energy Applications: Structural and Mechanistic Features

AbstractReview: 217 refs.

Total scattering investigation of materials for clean energy applications: The importance of the local structure

In this Perspective article we give an account of the application of total scattering methods and pair distribution function (PDF) analysis to the investigation of materials for clean energy applications such as materials for solid oxide fuel cells and lithium batteries, in order to show the power of this technique in providing new insights into the structure-property correlation in this class of materials.

Oxide-ion and proton conducting electrolyte materials for clean energy applications: structural and mechanistic features

This critical review presents an overview of the various classes of oxide materials exhibiting fast oxide-ion or proton conductivity for use as solid electrolytes in clean energy applications such as solid oxide fuel cells. Emphasis is placed on the relationship between structural and mechanistic features of the crystalline materials and their ion conduction properties. After describing well-established classes such as fluorite- and perovskite-based oxides, new materials and structure-types are presented. These include a variety of molybdate, gallate, apatite silicate/germanate and niobate systems, many of which contain flexible structural networks, and exhibit different defect properties and transport mechanisms to the conventional materials. It is concluded that the rich chemistry of these important systems provides diverse possibilities for developing superior ionic conductors for use as solid electrolytes in fuel cells and related applications. In most cases, a greater atomic-level understanding of the structures, defects and conduction mechanisms is achieved through a combination of experimental and computational techniques (217 references).