Shaping the Water-Harvesting Behavior of Metal-Organic Frameworks Aided by Fine-Tuned GPT Models

Two-Dimensional Covalent Organic Frameworks in Organic Electronics

Two-dimensional covalent organic frameworks (2DCOFs) are a unique class of crystalline porous materials interconnected by covalent bonds, which have attracted significant attention in recent years due to their chemical and structural diversity, as well as their applications in adsorption, separation, catalysis, and drug delivery. However, research on the electrical properties of 2DCOFs remains limited, despite their potential in organic electronics. Early studies recognized the poor electrical conductivity of 2DCOFs as a significant obstacle to their application in this field. To overcome this challenge, various strategies have been proposed to enhance conductivity. This review first introduces the concept of computational screening for 2DCOFs and explores approaches to improve their intrinsic conductivity, with a focus on four key aspects: in-plane and out-of-plane charge transport, topology, bandgap, and morphology. It then examines the application of pristine 2DCOFs in organic electronics, including applications in field-effect transistors, memristors, photodetectors, and chemiresistive gas sensors. We support these strategies with detailed statistical data, providing a comprehensive guide for the design and development of novel 2DCOFs for organic electronics. Finally, we outline future research directions, emphasizing the challenges that remain to be addressed in this emerging area.

Solvent Screening for Separation Processes Using Machine Learning and High-Throughput Technologies

As the chemical industry shifts toward sustainable practices, there is a growing initiative to replace conventional fossil-derived solvents with environmentally friendly alternatives such as ionic liquids (ILs) and deep eutectic solvents (DESs). Artificial intelligence (AI) plays a key role in the discovery and design of novel solvents and the development of green processes. This review explores the latest advancements in AI-assisted solvent screening with a specific focus on machine learning (ML) models for physicochemical property prediction and separation process design. Additionally, this paper highlights recent progress in the development of automated high-throughput (HT) platforms for solvent screening. Finally, this paper discusses the challenges and prospects of ML-driven HT strategies for green solvent design and optimization. To this end, this review provides key insights to advance solvent screening strategies for future chemical and separation processes.

A combined soft X-ray and theoretical investigation discloses the water harvesting behaviour of Mg-MOF-74 at the crystal surface

Metal-organic frameworks (MOFs) are receiving growing interest as transformative materials for real-world atmospheric water harvesting applications. However, obtaining molecular-level details on how surface effects regulate MOF water uptake has proven to be elusive. Here, we present a novel methodology based on ambient pressure soft X-ray absorption spectroscopy (AP-NEXAFS), machine learning-assisted theoretical spectroscopy and molecular dynamics simulations to gain selective insights into the behaviour of water at a MOF crystal surface. We applied our interdisciplinary method to investigate the structural and dynamical properties of water at the surface of the Mg-MOF-74 system, while obtaining complementary information on the water uptake and release from the bulk by synchrotron powder X-ray diffraction. Our investigation pointed out the simultaneous presence of Mg open sites and residual gas-phase water during dehydration, and proved that during water release a high number of surface Mg sites still interact with one or two water molecules. Conversely, when looking at the bulk, a significantly lower number of Mg sites have been found to interact with water molecules in the same experimental conditions. This behaviour suggests that the water adsorption (desorption) process starts from the interior of the material and propagates towards the channel openings. The combined approach based on AP-NEXAFS, PXRD experimental determinations and ML-supported theoretical analyses has been found to be a valuable tool to provide a thorough description of the water harvesting process at both surface and bulk of the crystal.

Hydrolytically Stable Phosphonate-Based Metal-Organic Frameworks for Harvesting Water from Low Humidity Air

Harvesting water from air offers a promising solution to the global water crisis. However, existing sorbents often struggle in arid climates due to limitations such as low sorption capacities, hydrolytic instability, slow mass transport, high desorption enthalpy, and costly operation. Phosphonate-based metal-organic frameworks (MOFs), known for their exceptional water stability, have not been extensively explored for water harvesting. This study systematically investigates the performance of STA-12 (M═Co, Ni, Mg) and STA-16 (M═Co, Ni), a series of stable phosphonate-based MOFs, as water sorbents. STA-12 MOFs demonstrate remarkable adsorption at ultra-low humidity (<10%), while STA-16(Co) exhibits a high water uptake capacity of 0.54 g g-1 at 10-50% relative humidity (RH) and 0.72 g g-1 at 34% RH. Molecular simulations and solid-state NMR identified liquid-like water, critical for harvesting applications, as the key contributor to the superior sorption performance of STA-16(Co). Scalable aqueous synthesis methods are developed, producing tens of grams of MOFs per batch without high-pressure equipment. A prototype device incorporating STA-12(Ni) demonstrated the feasibility of these materials for real-world water harvesting, showcasing their potential to address water scarcity in arid regions.

A Multiagent-Driven Robotic AI Chemist Enabling Autonomous Chemical Research On Demand

The successful integration of large language models (LLMs) into laboratory workflows has demonstrated robust capabilities in natural language processing, autonomous task execution, and collaborative problem-solving. This offers an exciting opportunity to realize the dream of autonomous chemical research on demand. Here, we report a robotic AI chemist powered by a hierarchical multiagent system, ChemAgents, based on an on-board Llama-3.1-70B LLM, capable of executing complex, multistep experiments with minimal human intervention. It operates through a Task Manager agent that interacts with human researchers and coordinates four role-specific agents─Literature Reader, Experiment Designer, Computation Performer, and Robot Operator─each leveraging one of four foundational resources: a comprehensive Literature Database, an extensive Protocol Library, a versatile Model Library, and a state-of-the-art Automated Lab. We demonstrate its versatility and efficacy through six experimental tasks of varying complexity, ranging from straightforward synthesis and characterization to more complex exploration and screening of experimental parameters, culminating in the discovery and optimization of functional materials. Additionally, we introduce a seventh task, where ChemAgents is deployed in a new robotic chemistry lab environment to autonomously perform photocatalytic organic reactions, highlighting ChemAgents's scalability and adaptability. Our multiagent-driven robotic AI chemist showcases the potential of on-demand autonomous chemical research to accelerate discovery and democratize access to advanced experimental capabilities across academic disciplines and industries.

LLM-Driven Synthesis Planning for Quantum Dot Materials Development

The application of large language models in materials science has opened new avenues for accelerating materials development. Building on this advancement, we propose a novel framework leveraging large language models to optimize experimental procedures for synthesizing quantum dot materials with multiple desired properties. Our framework integrates the synthesis protocol generation model and the property prediction model, both fine-tuned on open-source large language models using parameter-efficient training techniques with in-house synthesis protocol data. Once the synthesis protocol with target properties and a masked reference protocol is generated, it undergoes validation through the property prediction models, followed by assessments of its novelty and human evaluation. Our synthesis experiments demonstrate that among the six synthesis protocols derived from the entire framework, three successfully update the Pareto front, and all six improve at least one property. Through empirical validation, we confirm the effectiveness of our fine-tuned large language model-driven framework for synthesis planning, showcasing strong performance under multitarget optimization.

Small Rotations, Big Effects: Lessons from Water Adsorption in NU-1000

In this study, the adsorption mechanism of water in the metal-organic framework NU-1000 was investigated using molecular simulations. The simulations predict a significant impact of small changes in terminal aquo ligand orientation on the shape and pressure of the condensation step in the water adsorption isotherm. The analysis revealed that the rotational mobility of aquo ligands, often neglected in computational studies, can shift the condensation step by up to 20% in the relative humidity scale. By examining adsorption modes and interaction sites, it was demonstrated that configurational changes in the Zr6O8 node affect water adsorption significantly and can change the nature of the interactions from hydrophobic to hydrophilic. We propose a robust approach to account for these changes in simulations, achieving good agreement with experimental results. This work underscores the necessity of considering local, molecular flexibility in water adsorption simulations to avoid mischaracterization of MOFs' water adsorption properties.

A review of large language models and autonomous agents in chemistry

Large language models (LLMs) have emerged as powerful tools in chemistry, significantly impacting molecule design, property prediction, and synthesis optimization. This review highlights LLM capabilities in these domains and their potential to accelerate scientific discovery through automation. We also review LLM-based autonomous agents: LLMs with a broader set of tools to interact with their surrounding environment. These agents perform diverse tasks such as paper scraping, interfacing with automated laboratories, and synthesis planning. As agents are an emerging topic, we extend the scope of our review of agents beyond chemistry and discuss across any scientific domains. This review covers the recent history, current capabilities, and design of LLMs and autonomous agents, addressing specific challenges, opportunities, and future directions in chemistry. Key challenges include data quality and integration, model interpretability, and the need for standard benchmarks, while future directions point towards more sophisticated multi-modal agents and enhanced collaboration between agents and experimental methods. Due to the quick pace of this field, a repository has been built to keep track of the latest studies: https://github.com/ur-whitelab/LLMs-in-science.

Large Language Model-Assisted Genotoxic Metal-Phenolic Nanoplatform for Osteosarcoma Therapy

Osteosarcoma, a leading primary bone malignancy in children and adolescents, is associated with a poor prognosis and a low global fertility rate. A large language model-assisted phenolic network (LLMPN) platform is demonstrated that integrates the large language model (LLM) GPT-4 into the design of multifunctional metal-phenolic network materials. Fine-tuned GPT-4 identified gossypol as a phenolic compound with superior efficacy against osteosarcoma after evaluating across a library of 60 polyphenols based on the correlation between experimental anti-osteosarcoma activity and multiplexed chemical properties of polyphenols. Subsequently, gossypol is then self-assembled into Cu2+-gossypol nanocomplexes with a hyaluronic acid surface modification (CuGOS NPs). CuGOS NPs has demonstrated the ability to induce genetic alterations and cell death in osteosarcoma cells, offering significant therapeutic benefits for primary osteosarcoma tumors and reducing metastasis without adverse effects on major organs or the genital system. This work presents an LLM-driven approach for engineering metal-organic nanoplatform and broadening applications by harnessing the capabilities of LLMs, thereby improving the feasibility and efficiency of research activities.

Large Language Model for Automating the Analysis of Cryoprotectants

The rapid expansion of scientific literature necessitates developing efficient data extraction and analysis methods. This study presents an innovative approach to automating the extraction of cryoprotectant information from scientific publications using a generative pretrained transformer (GPT) model integrated with a Telegram bot interface. Our system processes and analyzes scientific articles to identify and extract relevant data on cryoprotectants and bacteria, significantly reducing the time required for researchers to gather essential information. Our method optimizes the workflow for researchers in cryopreservation and related fields by utilizing modern artificial intelligence technologies, specifically large language models. The Telegram bot, designed to be user-friendly, provides a comfortable and easy platform for quick data access, enhancing scientific research efficiency. The study's methodology involves data preparation, algorithm development, and system validation using a substantial data set of scientific articles. Results demonstrate the model's capability to accurately recognize and extract critical information, although some limitations in term specificity were noted. Our findings suggest that further refinement and training of the model can enhance its accuracy and reliability for specialized scientific applications.

Two-Periodic MoS2-Type Metal-Organic Frameworks with Intrinsic Intralayer Porosity for High-Capacity Water Sorption

2D metal-organic frameworks (2D-MOFs) are an important class of functional porous materials. However, the low porosity and surface area of 2D-MOFs have greatly limited their functionalities and applications. Herein, the rational synthesis of a class of mos-MOFs with molybdenum disulfide (mos) net based on the assembly of trinuclear metal clusters and 3-connected tripodal organic ligands is reported. The non-crystallographic (3,6)-connected mos net, different from the 3-connected hcb net of graphene, offers abundant intralayer voids courtesy of the split of one node into two. Indeed, mos-MOFs exhibit high apparent Brunauer-Emmett-Teller surface areas, significantly superior to those of other 2D-MOF analogs. Markedly, hydrolytically stable Cr-mos-MOF-1 displays an impressive water vapor uptake of 0.75 g g-1 at 298 K and P/P0 = 0.9, among the highest in 2D-MOFs. The combined water adsorption and X-ray diffraction study reveal the water adsorption mechanisms, suggesting the importance of intralayer porosities of mos-MOFs for high-performance water capture. This study paves the way for a reliable approach to synthesizing 2D-MOFs with high porosity and surface areas for diverse applications.

Synergistic global and local flexibilities in Zr-based metal-organic frameworks enable sequential sieving of hexane isomers

Separating hexane isomers based on the branching degree is crucial for their efficient utilization in the petrochemical industry, yet remains challenging due to their similar properties. Here we report a temperature-responsive Zr-based metal-organic framework, Zr-fum-FA, capable of sequentially sieving linear, mono-, and di-branched hexane isomers. Notably, the pore structure of Zr-fum-FA dynamically transforms from segmented triangular channels to an integrated rhombic configuration as the temperature increases, leading to distinct sieving effects. At low temperatures, the narrow triangular pores allow the exclusive adsorption of n-hexane while excluding branched isomers. In contrast, the expanded rhombic pores at high temperatures enable the sieving of mono- and di-branched isomers. Mechanistic studies reveal that this unique dual-sieving behavior originates from the synergistic effects of the global framework flexibility and the local dynamics of pendent hydroxyl groups. Furthermore, we demonstrate the decoupling of global and local flexibilities via two strategies: incorporating steric hindrance to dampen the global framework dynamics and enhancing the metal node rigidity to limit the local vibrations. These findings not only provide a promising adsorbent for the challenging separation of hexane isomers but also offer rational design principles for harnessing flexibility in MOFs.

An automatic end-to-end chemical synthesis development platform powered by large language models

The rapid emergence of large language model (LLM) technology presents promising opportunities to facilitate the development of synthetic reactions. In this work, we leveraged the power of GPT-4 to build an LLM-based reaction development framework (LLM-RDF) to handle fundamental tasks involved throughout the chemical synthesis development. LLM-RDF comprises six specialized LLM-based agents, including Literature Scouter, Experiment Designer, Hardware Executor, Spectrum Analyzer, Separation Instructor, and Result Interpreter, which are pre-prompted to accomplish the designated tasks. A web application with LLM-RDF as the backend was built to allow chemist users to interact with automated experimental platforms and analyze results via natural language, thus, eliminating the need for coding skills and ensuring accessibility for all chemists. We demonstrated the capabilities of LLM-RDF in guiding the end-to-end synthesis development process for the copper/TEMPO catalyzed aerobic alcohol oxidation to aldehyde reaction, including literature search and information extraction, substrate scope and condition screening, reaction kinetics study, reaction condition optimization, reaction scale-up and product purification. Furthermore, LLM-RDF's broader applicability and versability was validated on various synthesis tasks of three distinct reactions (SNAr reaction, photoredox C-C cross-coupling reaction, and heterogeneous photoelectrochemical reaction).

Enhancement of Water Productivity and Energy Efficiency in Sorption-based Atmospheric Water Harvesting Systems: From Material, Component to System Level

To address the increasingly serious water scarcity across the world, sorption-based atmospheric water harvesting (SAWH) continues to attract attention among various water production methods, due to it being less dependent on climatic and geographical conditions. Water productivity and energy efficiency are the two most important evaluation indicators. Therefore, this review aims to comprehensively and systematically summarize and discuss the water productivity and energy efficiency enhancement methods for SAWH systems based on three levels, from material to component to system. First, the material level covers the characteristics, categories, and mechanisms of different sorbents. Second, the component level focuses on the sorbent bed, regeneration energy, and condenser. Third, the system level encompasses the system design, operation, and synergetic effect generation with other mechanisms. Specifically, the key and promising improvement methods are: synthesizing composite sorbents with high water uptake, fast sorption kinetics, and low regeneration energy (material level); improving thermal insulation between the sorbent bed and condenser, utilizing renewable energy or electrical heating for desorption and multistage design (component level); achieving continuous system operation with a desired number of sorbent beds or rotational structure, and integrating with Peltier cooling or passive radiative cooling technologies (system level). In addition, applications and challenges of SAWH systems are explored, followed by potential outlooks and future perspectives. Overall, it is expected that this review article can provide promising directions and guidelines for the design and operation of SAWH systems with the aim of achieving high water productivity and energy efficiency.

Single and Multi-Hop Question-Answering Datasets for Reticular Chemistry with GPT-4-Turbo

The rapid advancement in artificial intelligence and natural language processing has led to the development of large-scale datasets aimed at benchmarking the performance of machine learning models. Herein, we introduce "RetChemQA", a comprehensive benchmark dataset designed to evaluate the capabilities of such models in the domain of reticular chemistry. This dataset includes both single-hop and multi-hop question-answer pairs, encompassing approximately 45,000 question and answers (Q&As) for each type. The questions have been extracted from an extensive corpus of literature containing about 2,530 research papers from publishers including NAS, ACS, RSC, Elsevier, and Nature Publishing Group, among others. The dataset has been generated using OpenAI's GPT-4 Turbo, a cutting-edge model known for its exceptional language understanding and generation capabilities. In addition to the Q&A dataset, we also release a dataset of synthesis conditions extracted from the corpus of literature used in this study. The aim of RetChemQA is to provide a robust platform for the development and evaluation of advanced machine learning algorithms, particularly for the reticular chemistry community. The dataset is structured to reflect the complexities and nuances of real-world scientific discourse, thereby enabling nuanced performance assessments across a variety of tasks.

Cross-Modal Prediction of Spectral and Structural Descriptors via a Pretrained Model Enhanced with Chemical Insights

Proposing and utilizing machine learning descriptors for chemical property prediction and material screening have become a cutting-edge field in artificial intelligence-enabled chemical research. However, a single descriptor typically captures only partial features of a chemical object, resulting in an information deficiency and limiting generalizability. Obtaining a comprehensive set of descriptors is essential but challenging, especially when accessing some microlevel structural and electronic features due to technological limitations. Herein, we exploit multimodal chemical descriptors to construct an encoder-decoder machine learning framework that enables the cross-modal prediction of spectral and structural descriptors. By pretraining the model to endow it with chemical insights, the multimodal data fusion is implemented in a descriptor-encoded hidden layer. The model's capabilities are validated in the system of CO/NO adsorption on Au/Ag surfaces, demonstrating successful reciprocal prediction of infrared spectra, Raman spectra, and internal coordinates. This work provides a proof-of-concept for the feasibility of cross-modal predictions between different chemical features and will significantly reduce the machine learning model's dependence on complete physicochemical parameters and improve its multitarget prediction capabilities.

Machine Learning Optimizing Enzyme/ZIF Biocomposites for Enhanced Encapsulation Efficiency and Bioactivity

In this study, we present the first example of using a machine learning (ML)-assisted design strategy to optimize the synthesis formulation of enzyme/ZIFs (zeolitic imidazolate framework) for enhanced performance. Glucose oxidase (GOx) and horseradish peroxidase (HRP) were chosen as model enzymes, while Zn(eIM)2 (eIM = 2-ethylimidazolate) was selected as the model ZIF to test our ML-assisted workflow paradigm. Through an iterative ML-driven training-design-synthesis-measurement workflow, we efficiently discovered GOx/ZIF (G151) and HRP/ZIF (H150) with their overall performance index (OPI) values (OPI represents the product of encapsulation efficiency (E in %), retained enzymatic activity (A in %), and thermal stability (T in %)) at least 1.3 times higher than those in systematic seed data studies. Furthermore, advanced statistical methods derived from the trained random forest model qualitatively and quantitatively reveal the relationship among synthesis, structure, and performance in the enzyme/ZIF system, offering valuable guidance for future studies on enzyme/ZIFs. Overall, our proposed ML-assisted design strategy holds promise for accelerating the development of enzyme/ZIFs and other enzyme immobilization systems for biocatalysis applications and beyond, including drug delivery and sensing, among others.

Interconversion and functional composites of metal-organic frameworks and hydrogen-bonded organic frameworks

Metal-organic frameworks (MOFs), an emerging class of highly ordered crystalline porous materials, possess structural tunability, high specific surface area, well-defined pores, and diverse pore environments and morphologies, making them suitable for various potential applications. Moreover, hydrogen-bonded organic frameworks (HOFs), constructed from organic molecules with complementary hydrogen-bonding patterns, are rapidly evolving into a novel category of porous materials due to their facile mild preparation conditions, solution processability, easy regeneration capability, and excellent biocompatibility. These distinctive advantages have garnered significant attention across diverse fields. Considering the inherent binding affinity between MOFs and HOFs along with the fact that many MOF linkers can serve as building blocks for constructing HOFs, their combination holds promise in creating functional materials with enhanced performance. This feature paper provides an introduction to the interconversion between MOFs and HOFs followed by highlighting the emerging applications of MOF-HOF composites. Finally, we briefly discuss the current challenges associated with future perspectives on MOF-HOF composites.

Large Language Models for Inorganic Synthesis Predictions

We evaluate the effectiveness of pretrained and fine-tuned large language models (LLMs) for predicting the synthesizability of inorganic compounds and the selection of precursors needed to perform inorganic synthesis. The predictions of fine-tuned LLMs are comparable to─and sometimes better than─recent bespoke machine learning models for these tasks but require only minimal user expertise, cost, and time to develop. Therefore, this strategy can serve both as an effective and strong baseline for future machine learning studies of various chemical applications and as a practical tool for experimental chemists.

Isoreticular Curves: A Theory of Capillary Condensation To Model Water Sorption within Microporous Sorbents

Metal-organic frameworks have gained traction as leading materials for water sorption applications due to precise chemical tunability of their well-ordered pores. These applications include atmospheric water capture, heat pumps, desiccation, desalination, humidity control, and thermal batteries. However, the relationships between the framework pore structure and the measurable water sorption properties, namely critical relative humidity for condensation, maximal capacity, and pore size or temperature for the onset of hysteresis, have not been clearly delineated. Herein, we precisely formulate these relationships by application of the theory of capillary condensation and macroscopic thermodynamic models to a large data set of MOF water isotherms. These relationships include a concept termed isoreticular curves that relates the critical pressure for pore condensation (α), gravimetric capacity (Qmax), and hydrophilicity (the Gibbs free energy for binding water, ΔG) as Qmax = a1(ΔG/ln α)2 + a2(ΔG/ln α), with constants a1 and a2 dependent upon the density and volume occupied by the linker and secondary building unit, and framework topology. Through this analysis, we propose guidelines for the maximization of sorption capacity at a given relative humidity with minimal hysteresis and discuss the theoretical limits for capacity at low relative humidity. This model provides an explanation for the lack of high-capacity frameworks at low relative humidity, as increasing pore size also causes an increase in relative humidity. We propose a loose upper bound of Qmax = -0.25(1/ln α)2 - 1.75(1/ln α) for the limit of maximal capacity at a given relative humidity in the dry regime. These guidelines are consequential for the design of new materials for water sorption applications.

Solving Advanced Task-Specific Problems in Measurement Sciences with Generative AI

The Generative Pre-Trained Transformer known as ChatGPT-4 has undergone extensive pretraining on a diverse data set, enabling it to generate coherent and contextually relevant text based on the input it receives. This capability allows it to perform tasks from answering questions and has attracted significant interest in material sciences, synthetic chemistry, and drug discovery. In this work, we posed four advanced task-specific problems to ChatGPT, which were recently published in leading journals for topics in analytical chemistry, spectroscopy, bioimage super-resolution, and electrochemistry. ChatGPT-4 successfully implemented the four ideas after assigning the "persona" to the AI and posing targeted questions. We show two cases where "unguided" ChatGPT could complete the assignments with minimal human direction. The construction of a microwave spectrum from a free induction curve and super-resolution of bioimages was accomplished using this approach. Two other specific tasks, correcting a complex baseline with morphological operations of set theory and estimating the diffusion current, required additional input, e.g., equations and specific directions from the user. In each case, the MATLAB code was eventually generated by ChatGPT-4 even when the original authors did not provide any code themselves. We show that a validation test must be implemented to detect and correct mistakes made by ChatGPT-4, followed by feedback correction. These approaches will pave the way for open and transparent science and eliminate the black boxes in measurement science when it comes to advanced data processing.

Chemistries and materials for atmospheric water harvesting

Atmospheric water harvesting (AWH) is recognized as a crucial strategy to address the global challenge of water scarcity by tapping into the vast reserves of atmospheric moisture for potable water supply. Within this domain, sorbents lie in the core of AWH technologies as they possess broad adaptability across a wide spectrum of humidity levels, underpinned by the cyclic sorption and desorption processes of sorbents, necessitating a multi-scale viewpoint regarding the rational material and chemical selection and design. This Invited Review delves into the essential sorption mechanisms observed across various classes of sorbent systems, emphasizing the water-sorbent interactions and the progression of water networks. A special focus is placed on the insights derived from isotherm profiles, which elucidate sorbent structures and sorption dynamics. From these foundational principles, we derive material and chemical design guidelines and identify key tuning factors from a structural-functional perspective across multiple material systems, addressing their fundamental chemistries and unique attributes. The review further navigates through system-level design considerations to optimize water production efficiency. This review aims to equip researchers in the field of AWH with a thorough understanding of the water-sorbent interactions, material design principles, and system-level considerations essential for advancing this technology.

Mining for Metal-Organic Systems: Chemistry Frontiers of Th-, U-, and Zr-Materials

The conceptual framework presented in this Perspective overviews the design principles of innovative thorium-based materials that could address urgent needs of the medicinal, nuclear energy, and waste remediation sectors from the lens of zirconium and uranium analogs. We survey the intersections of Zr, Th, and U chemistry with a focus on how the intrinsic behavior of each metal translates to broader material properties, including, but not limited to, structural and topological diversity, preferential metal-ligand binding, and reactivity. On the example of several classes of materials, including organometallic complexes, polyoxometalates, and the primary focus of this Perspective, metal-organic frameworks (MOFs), the design principles that govern the preparation of Zr-, Th-, and U-compounds, including oxophilicity, variation in oxidation states, and stable coordination environments have been considered. Further, we highlight how the impact of the mentioned variables may shift throughout the progression from discrete molecular systems to extended structures. We discuss the common assumption that zirconium-organic materials are typically considered a close analog of thorium-based congeners in areas such as material design and preparation. Through consideration of fundamental chemistry principles, we shed light on the relationships between Zr-, Th-, and U-based materials and highlight how a critical analysis of their distinct properties can be used to target a desired material performance. As a result, we provide a detailed understanding of Th-based materials chemistry by anchoring their fundamental properties between two well-studied reference points, zirconium- and uranium-containing analogs.

Engineered Nanoporous Frameworks for Adsorption Cooling Applications

The energy demand for traditional vapor-compressed technology for space cooling continues to soar year after year due to global warming and the increasing human population's need to improve living and working conditions. Thus, there is a growing demand for eco-friendly technologies that use sustainable or waste energy resources. This review discusses the properties of various refrigerants used for adsorption cooling applications followed by a brief discussion on the thermodynamic cycle. Next, sorbents traditionally used for cooling are reviewed to emphasize the need for advanced capture materials with superior properties to improve refrigerant sorption. The remainder of the review focus on studies using engineered nanoporous frameworks (ENFs) with various refrigerants for adsorption cooling applications. The effects of the various factors that play a role in ENF-refrigerant pair selection, including pore structure/dimension/shape, morphology, open-metal sites, pore chemistry and possible presence of defects, are reviewed. Next, in-depth insights into the sorbent-refrigerant interaction, and pore filling mechanism gained through a combination of characterization techniques and computational modeling are discussed. Finally, we outline the challenges and opportunities related to using ENFs for adsorption cooling applications and provide our views on the future of this technology.

Understanding and Predicting the Spatially Resolved Adsorption Properties of Nanoporous Materials

Using knowledge from statistical thermodynamics and crystallography, we develop an image-image translation model, called SorbIIT, that uses three-dimensional grids of adsorbate-adsorbent interaction energies as input to predict the spatially resolved loading surface of nanoporous materials over a broad range of temperatures and pressures. SorbIIT consists of a closed-form differential model for loading-surface prediction and a U-Net to generate spatial differential distributions from the energy grids. SorbIIT is trained using the energy grids and adsorbate distributions (obtained from high-throughput simulations) of 50 synthesized and 70 hypothetical zeolites and applied for predicting the adsorption of carbon dioxide, hydrogen sulfide, n-butane, 2-methylpropane, krypton, and xenon in other zeolites from 256 to 400 K. Employing a quadratic isotherm model for the local differentiation, SorbIIT yields mean R2 values of 0.998 for total adsorption and 0.6904 for local adsorption with a resolution of 0.2 Å, and a value of 0.721 for the structural similarity of the local loading distribution.

Mechanochemical Synthesis of MOF-303 and Its CO2 Adsorption at Ambient Conditions

Metal-organic structures have great potential for practical applications in many areas. However, their widespread use is often hindered by time-consuming and expensive synthesis procedures that often involve hazardous solvents and, therefore, generate wastes that need to be remediated and/or recycled. The development of cleaner, safer, and more sustainable synthesis methods is extremely important and is needed in the context of green chemistry. In this work, a facile mechanochemical method involving water-assisted ball milling was used for the synthesis of MOF-303. The obtained MOF-303 exhibited a high specific surface area of 1180 m2/g and showed an excellent CO2 adsorption capacity of 9.5 mmol/g at 0 °C and under 1 bar.

Structural Design for EMI Shielding: From Underlying Mechanisms to Common Pitfalls

Modern human civilization deeply relies on the rapid advancement of cutting-edge electronic systems that have revolutionized communication, education, aviation, and entertainment. However, the electromagnetic interference (EMI) generated by digital systems poses a significant threat to the society, potentially leading to a future crisis. While numerous efforts are made to develop nanotechnological shielding systems to mitigate the detrimental effects of EMI, there is limited focus on creating absorption-dominant shielding solutions. Achieving absorption-dominant EMI shields requires careful structural design engineering, starting from the smallest components and considering the most effective electromagnetic wave attenuating factors. This review offers a comprehensive overview of shielding structures, emphasizing the critical elements of absorption-dominant shielding design, shielding mechanisms, limitations of both traditional and nanotechnological EMI shields, and common misconceptions about the foundational principles of EMI shielding science. This systematic review serves as a scientific guide for designing shielding structures that prioritize absorption, highlighting an often-overlooked aspect of shielding science.

Friends or Foes: Fundamental Principles of Th-Organic Scaffold Chemistry Using Zr-Analogs as a Guide

The fundamental interest in actinide chemistry, particularly for the development of thorium-based materials, is experiencing a renaissance owing to the recent and rapidly growing attention to fuel cycle reactors, radiological daughters for nuclear medicine, and efficient nuclear stockpile development. Herein, we uncover fundamental principles of thorium chemistry on the example of Th-based extended structures such as metal-organic frameworks in comparison with the discrete systems and zirconium extended analogs, demonstrating remarkable over two-and-half-year chemical stability of Th-based frameworks as a function of metal node connectivity, amount of defects, and conformational linker rigidity through comprehensive spectroscopic and crystallographic analysis as well as theoretical modeling. Despite exceptional chemical stability, we report the first example of studies focusing on the reactivity of the most chemically stable Th-based frameworks in comparison with the discrete Th-based systems such as metal-organic complexes and a cage, contrasting multicycle recyclability and selectivity (>97%) of the extended structures in comparison with the molecular compounds. Overall, the presented work not only establishes the conceptual foundation for evaluating the capabilities of Th-based materials but also represents a milestone for their multifaceted future and foreshadows their potential to shape the next era of actinide chemistry.

Analysis and prediction in SCR experiments using GPT-4 with an effective chain-of-thought prompting strategy

This study explores the use of large language models (LLMs) in interpreting and predicting experimental outcomes based on given experimental variables, leveraging the human-like reasoning and inference capabilities of LLMs, using selective catalytic reduction of NOx with NH3 as a case study. We implement the chain of thought (CoT) concept to formulate logical steps for uncovering connections within the data, introducing an "Ordered-and-Structured" CoT (OSCoT) prompting strategy. We compare the OSCoT strategy with the more conventional "One-Pot" CoT (OPCoT) approach and with human experts. We demonstrate that GPT-4, equipped with this new OSCoT prompting strategy, outperforms the other two settings and accurately predicts experimental outcomes and provides intuitive reasoning for its predictions.

Programmable Water Sorption through Linker Installation into a Zirconium Metal-Organic Framework

Hydrolytically stable materials exhibiting a wide range of programmable water sorption behaviors are crucial for on-demand water sorption systems. While notable advancements in employing metal-organic frameworks (MOFs) as promising water adsorbents have been made, developing a robust yet easily tailorable MOF scaffold for specific operational conditions remains a challenge. To address this demand, we employed a topology-guided linker installation strategy using NU-600, which is a zirconium-based MOF (Zr-MOF) that contains three vacant crystallographically defined coordination sites. Through a judicious selection of three N-heterocyclic auxiliary linkers of specific lengths, we installed them into designated sites, giving rise to six new MOFs bearing different combinations of linkers in predetermined positions. The resulting MOFs, denoted as NU-606 to NU-611, demonstrate enhanced structural stability against capillary force-driven channel collapse during water desorption due to the increased connectivity of the Zr6 clusters in the resulting MOFs. Furthermore, incorporating these auxiliary linkers with various hydrophilic N sites enables the systematic modulation of the pore-filling pressure from about 55% relative humidity (RH) for the parent NU-600 down to below 40% RH. This topology-driven linker installation strategy offers precise control of water sorption properties for MOFs, highlighting a facile route to design MOF adsorbents for use in water sorption applications.