ABSTRACT
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.
ABSTRACT
We have developed two series of amine-functionalized zirconium (Zr) metal-organic framework-808 (MOF-808), which were produced by postsynthetic modifications to have either amino acids coordinated to Zr ions (MOF-808-AAs) or polyamines covalently bound to the chloro-functionalized structure (MOF-808-PAs). These MOF variants were comprehensively characterized by liquid-state 1H nuclear magnetic resonance (NMR) measurements and potentiometric acid-base titration to determine the amounts of amines, energy-dispersive X-ray spectroscopy to assess the extent of covalent substitution by polyamines, powder X-ray diffraction analysis to verify the maintenance of the MOF crystallinity and structure after postsynthetic modifications, nitrogen sorption isotherm measurements to confirm retention of the porosity, and water sorption isotherm measurements to find the water uptake in the pores of each member of the series. Evaluation and testing of these compounds in direct air capture (DAC) of CO2 showed improved CO2 capture performance for the functionalized forms, especially under humid conditions: In dry conditions, the l-lysine- and tris(3-aminopropyl)amine-functionalized variants, termed as MOF-808-Lys and MOF-808-TAPA, exhibited the highest CO2 uptakes at 400 ppm, measuring 0.612 and 0.498 mmol g-1, and further capacity enhancement was achieved by introducing 50% relative humidity, resulting in remarkable uptakes of 1.205 and 0.872 mmol g-1 corresponding to 97 and 75% increase compared to the dry uptakes, respectively. The mechanism underlying the enhanced uptake efficiency was revealed by 13C solid-state NMR and temperature-programmed desorption measurements, indicating the formation of bicarbonate species, and therefore a stoichiometry of 1:1 CO2 to each amine site.
ABSTRACT
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.
ABSTRACT
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.
