Harvesting Water from Air with High-Capacity, Stable Furan-Based Metal-Organic Frameworks.

We synthesized two isoreticular furan-based metal-organic frameworks (MOFs), MOF-LA2-1(furan) and MOF-LA2-2(furan) with rod-like secondary building units (SBUs) featuring 1D channels, as sorbents for atmospheric water harvesting (LA = long arm). These aluminum-based MOFs demonstrated a combination of high water uptake and stability, exhibiting working capacities of 0.41 and 0.48 gwater/gMOF (under isobaric conditions of 1.70 kPa), respectively. Remarkably, both MOFs showed a negligible loss in water uptake after 165 adsorption-desorption cycles. These working capacities rival that of MOF-LA2-1(pyrazole), which has a working capacity of 0.55 gwater/gMOF. The current MOFs stand out for their high water stability, as evidenced by 165 cycles of water uptake and release. MOF-LA2-2(furan) is the first aluminum MOF to employ a double 'long arm' extension strategy, which is confirmed through single-crystal X-ray diffraction (SCXRD). The MOFs were synthesized by using a straightforward synthesis route. This study offers valuable insights into the design of durable, water-stable MOFs and underscores their potential for efficient water harvesting.

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

We construct a data set of metal-organic framework (MOF) linkers and employ a fine-tuned GPT assistant to propose MOF linker designs by mutating and modifying the existing linker structures. This strategy allows the GPT model to learn the intricate language of chemistry in molecular representations, thereby achieving an enhanced accuracy in generating linker structures compared with its base models. Aiming to highlight the significance of linker design strategies in advancing the discovery of water-harvesting MOFs, we conducted a systematic MOF variant expansion upon state-of-the-art MOF-303 utilizing a multidimensional approach that integrates linker extension with multivariate tuning strategies. We synthesized a series of isoreticular aluminum MOFs, termed Long-Arm MOFs (LAMOF-1 to LAMOF-10), featuring linkers that bear various combinations of heteroatoms in their five-membered ring moiety, replacing pyrazole with either thiophene, furan, or thiazole rings or a combination of two. Beyond their consistent and robust architecture, as demonstrated by permanent porosity and thermal stability, the LAMOF series offers a generalizable synthesis strategy. Importantly, these 10 LAMOFs establish new benchmarks for water uptake (up to 0.64 g g-1) and operational humidity ranges (between 13 and 53%), thereby expanding the diversity of water-harvesting MOFs.

Structural Chemistry of Zeolitic Imidazolate Frameworks.

Zeolitic imidazolate frameworks (ZIFs) are a subclass of reticular structures based on tetrahedral four-connected networks of zeolites and minerals. They are composed of transition-metal ions and imidazolate-type linkers, and their pore size and shape, surface area, and functionality can be precisely controlled. Despite their potential, two questions remain unanswered: how to synthesize more diverse ZIF structures and how ZIFs differentiate from other crystalline solids. In other words, how can we use our understanding of their unique structures to better design and synthesize ZIFs? In this Review, we first summarize the methods for synthesizing a wide range of ZIFs. We then review the crystal structure of ZIFs and describe the relationship between their structure and properties using an in-depth analysis. We also discuss several important and intrinsic features that make ZIFs stand out from MOFs and discrete molecular cages. Finally, we outline the future direction for this class of porous crystals.

ChatGPT Research Group for Optimizing the Crystallinity of MOFs and COFs.

We leveraged the power of ChatGPT and Bayesian optimization in the development of a multi-AI-driven system, backed by seven large language model-based assistants and equipped with machine learning algorithms, that seamlessly orchestrates a multitude of research aspects in a chemistry laboratory (termed the ChatGPT Research Group). Our approach accelerated the discovery of optimal microwave synthesis conditions, enhancing the crystallinity of MOF-321, MOF-322, and COF-323 and achieving the desired porosity and water capacity. In this system, human researchers gained assistance from these diverse AI collaborators, each with a unique role within the laboratory environment, spanning strategy planning, literature search, coding, robotic operation, labware design, safety inspection, and data analysis. Such a comprehensive approach enables a single researcher working in concert with AI to achieve productivity levels analogous to those of an entire traditional scientific team. Furthermore, by reducing human biases in screening experimental conditions and deftly balancing the exploration and exploitation of synthesis parameters, our Bayesian search approach precisely zeroed in on optimal synthesis conditions from a pool of 6 million within a significantly shortened time scale. This work serves as a compelling proof of concept for an AI-driven revolution in the chemistry laboratory, painting a future where AI becomes an efficient collaborator, liberating us from routine tasks to focus on pushing the boundaries of innovation.

A GPT-4 Reticular Chemist for Guiding MOF Discovery.

We present a new framework integrating the AI model GPT-4 into the iterative process of reticular chemistry experimentation, leveraging a cooperative workflow of interaction between AI and a human researcher. This GPT-4 Reticular Chemist is an integrated system composed of three phases. Each of these utilizes GPT-4 in various capacities, wherein GPT-4 provides detailed instructions for chemical experimentation and the human provides feedback on the experimental outcomes, including both success and failures, for the in-context learning of AI in the next iteration. This iterative human-AI interaction enabled GPT-4 to learn from the outcomes, much like an experienced chemist, by a prompt-learning strategy. Importantly, the system is based on natural language for both development and operation, eliminating the need for coding skills, and thus, make it accessible to all chemists. Our collaboration with GPT-4 Reticular Chemist guided the discovery of an isoreticular series of MOFs, with each synthesis fine-tuned through iterative feedback and expert suggestions. This workflow presents a potential for broader applications in scientific research by harnessing the capability of large language models like GPT-4 to enhance the feasibility and efficiency of research activities.

MOF Linker Extension Strategy for Enhanced Atmospheric Water Harvesting.

A linker extension strategy for generating metal-organic frameworks (MOFs) with superior moisture-capturing properties is presented. Applying this design approach involving experiment and computation results in MOF-LA2-1 {[Al(OH)(PZVDC)], where PZVDC2- is (E)-5-(2-carboxylatovinyl)-1H-pyrazole-3-carboxylate}, which exhibits an approximately 50% water capacity increase compared to the state-of-the-art water-harvesting material MOF-303. The power of this approach is the increase in pore volume while retaining the ability of the MOF to harvest water in arid environments under long-term uptake and release cycling, as well as affording a reduction in regeneration heat and temperature. Density functional theory calculations and Monte Carlo simulations give detailed insight pertaining to framework structure, water interactions within its pores, and the resulting water sorption isotherm.

Decoding complex order in reticular frameworks.

Periodicity and aperiodicity coexist in a nonchaotic, information-rich crystal structure.

Direct Optical Lithography of Colloidal Metal Oxide Nanomaterials for Diffractive Optical Elements with 2π Phase Control.

Spatially patterned dielectric materials are ubiquitous in electronic, photonic, and optoelectronic devices. These patterns are typically made by subtractive or additive approaches utilizing vapor-phase reagents. On the other hand, recent advances in solution-phase synthesis of oxide nanomaterials have unlocked a materials library with greater compositional, microstructural, and interfacial tunability. However, methods to pattern and integrate these nanomaterials in real-world devices are less established. In this work, we directly optically pattern oxide nanoparticles (NPs) by mixing them with photosensitive diazo-2-naphthol-4-sulfonic acid and irradiating with widely available 405 nm light. We demonstrate the direct optical lithography of ZrO2, TiO2, HfO2, and ITO NPs and investigate the chemical and physical changes responsible for this photoinduced decrease in solubility. Micron-thick layers of amorphous ZrO2 NPs were patterned with micron resolution and shown to allow 2π phase control of visible light. We also show multilayer patterning and use it to fabricate features with different thicknesses and distinct structural colors. Upon annealing at 400 °C, the deposited ZrO2 structures have excellent optical transparency across a wide wavelength range (0.3-10 µm), a high refractive index (n = 1.84 at 633 nm), and are optically smooth. We then fabricate diffractive optical elements, such as binary phase diffraction gratings, that show efficient diffractive behavior and good thermal stability. Different oxide NPs can also be mixed prior to patterning, providing a high level of material tunability. This work demonstrates a general patterning approach that harnesses the processability and diversity of colloidal oxide nanomaterials for use in photonic applications.

Co5.47N loaded N-doped carbon as an efficient bifunctional oxygen electrocatalyst for a Zn-air battery.

Highly active bifunctional electrocatalysts for the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) play a pivotal role in Zn-air batteries. The high cost, scarcity and instability of precious-metal-based electrocatalysts for the ORR and OER dramatically hamper their practical application in such clean-energy set-ups. Here, we report highly active Co5.47N-loaded N-doped carbon (CoNMC), prepared via the direct NH3 annealing of a millet-CoCl2 mixture, which is a cheap and mass-producible form of biomass. The optimized product shows superior ORR activity (a half-wave potential of 0.81 V vs. RHE) and electrochemical stability (a 16.5 mV negative shift of the half-wave potential after 2000 cycles) in alkaline media. Also, it shows appealing OER activity (an operating potential at 10 mA cm-2 of 1.62 V vs. RHE). This excellent electrochemical performance can be attributed to the formation of active Co5.47N nanoparticles, the large specific surface area, the abundance of nitrogen active sites, and the high graphitization degree. When assembled into a Zn-air battery, the CoNMC-based cell shows comparable performance to a Pt/C-RuO2 one.