Rapid Cycling and Exceptional Yield in a Metal-Organic Framework Water Harvester.

Sorbent-assisted water harvesting from air represents an attractive way to address water scarcity in arid climates. Hitherto, sorbents developed for this technology have exclusively been designed to perform one water harvesting cycle (WHC) per day, but the productivities attained with this approach cannot reasonably meet the rising demand for drinking water. This work shows that a microporous aluminum-based metal-organic framework, MOF-303, can perform an adsorption-desorption cycle within minutes under a mild temperature swing, which opens the way for high-productivity water harvesting through rapid, continuous WHCs. Additionally, the favorable dynamic water sorption properties of MOF-303 allow it to outperform other commercial sorbents displaying excellent steady-state characteristics under similar experimental conditions. Finally, these findings are implemented in a new water harvester capable of generating 1.3 L kgMOF -1 day-1 in an indoor arid environment (32% relative humidity, 27 °C) and 0.7 L kgMOF -1 day-1 in the Mojave Desert (in conditions as extreme as 10% RH, 27 °C), representing an improvement by 1 order of magnitude over previously reported devices. This study demonstrates that creating sorbents capable of rapid water sorption dynamics, rather than merely focusing on high water capacities, is crucial to reach water production on a scale matching human consumption

ChatGPT Chemistry Assistant for Text Mining and Prediction of MOF Synthesis

We use prompt engineering to guide ChatGPT in the automation of text mining of metal-organic frameworks (MOFs) synthesis conditions from diverse formats and styles of the scientific literature. This effectively mitigates ChatGPT's tendency to hallucinate information -- an issue that previously made the use of Large Language Models (LLMs) in scientific fields challenging. Our approach involves the development of a workflow implementing three different processes for text mining, programmed by ChatGPT itself. All of them enable parsing, searching, filtering, classification, summarization, and data unification with different tradeoffs between labor, speed, and accuracy. We deploy this system to extract 26,257 distinct synthesis parameters pertaining to approximately 800 MOFs sourced from peer-reviewed research articles. This process incorporates our ChemPrompt Engineering strategy to instruct ChatGPT in text mining, resulting in impressive precision, recall, and F1 scores of 90-99%. Furthermore, with the dataset built by text mining, we constructed a machine-learning model with over 86% accuracy in predicting MOF experimental crystallization outcomes and preliminarily identifying important factors in MOF crystallization. We also developed a reliable data-grounded MOF chatbot to answer questions on chemical reactions and synthesis procedures. Given that the process of using ChatGPT reliably mines and tabulates diverse MOF synthesis information in a unified format, while using only narrative language requiring no coding expertise, we anticipate that our ChatGPT Chemistry Assistant will be very useful across various other chemistry sub-disciplines.Comment: Published on Journal of the American Chemical Society (2023); 102 pages (18-page manuscript, 84 pages of supporting information

GPT-4 Reticular Chemist for 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 apprentice. 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 apprentice provides feedback on the experimental outcomes, including both success and failures, for the in-text 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 GPT-4 Reticular Chemist demonstrated the discovery of an isoreticular series of metal-organic frameworks (MOFs), each of which was made using distinct synthesis strategies and optimal conditions. 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.Comment: 163 pages (an 8-page manuscript and 155 pages of supporting information

Finite element thermal analysis of the fusion welding of a P92 steel pipe

Fusion welding is common in steel pipeline construction in fossil-fuel power generation plants. Steel pipes in service carry steam at high temperature and pressure, undergoing creep during years of service; their integrity is critical for the safe operation of a plant. The high-grade martensitic P92 steel is suitable for plant pipes for its enhanced creep strength. P92 steel pipes are usually joined together with a similar weld metal. Martensitic pipes are sometimes joined to austenitic steel pipes using nickel based weld consumables. Welding involves severe thermal cycles, inducing residual stresses in the welded structure, which, without post weld heat treatment (PWHT), can be detrimental to the integrity of the pipes. Welding residual stresses can be numerically simulated by applying the finite element (FE) method in Abaqus. The simulation consists of a thermal analysis, determining the temperature history of the FE model, followed by a sequentially-coupled structural analysis, predicting residual stresses from the temperature history. <br><br> In this paper, the FE thermal analysis of the arc welding of a typical P92 pipe is presented. The two parts of the P92 steel pipe are joined together using a dissimilar material, made of Inconel weld consumables, producing a multi-pass butt weld from 36 circumferential weld beads. Following the generation of the FE model, the FE mesh is controlled using Model Change in Abaqus to activate the weld elements for each bead at a time corresponding to weld deposition. The thermal analysis is simulated by applying a distributed heat flux to the model, the accuracy of which is judged by considering the fusion zones in both the parent pipe as well as the deposited weld metal. For realistic fusion zones, the heat flux must be prescribed in the deposited weld pass and also the adjacent pipe elements. The FE thermal results are validated by comparing experimental temperatures measured by five thermocouples on the pipe outside surface with the FE temperature history at corresponding nodal points

Thermal maps of gases in heterogeneous reactions.

More than 85 per cent of all chemical industry products are made using catalysts1,2, the overwhelming majority of which are heterogeneous catalysts2 that function at the gas–solid interface3. Consequently, much effort is invested in optimizing the design of catalytic reactors, usually by modelling4 the coupling between heat transfer, fluid dynamics and surface reaction kinetics. The complexity involved requires a calibration of model approximations against experimental observations5,6, with temperature maps being particularly valuable because temperature control is often essential for optimal operation and because temperature gradients contain information about the energetics of a reaction. However, it is challenging to probe the behaviour of a gas inside a reactor without disturbing its flow, particularly when trying also to map the physical parameters and gradients that dictate heat and mass flow and catalytic efficiency1,2,3,4,5,6,7,8,9. Although optical techniques10,11,12 and sensors13,14 have been used for that purpose, the former perform poorly in opaque media and the latter perturb the flow. NMR thermometry can measure temperature non-invasively, but traditional approaches applied to gases produce signals that depend only weakly on temperature15,16 are rapidly attenuated by diffusion16,17 or require contrast agents18 that may interfere with reactions. Here we present a new NMR thermometry technique that circumvents these problems by exploiting the inverse relationship between NMR linewidths and temperature caused by motional averaging in a weak magnetic field gradient. We demonstrate the concept by non-invasively mapping gas temperatures during the hydrogenation of propylene in reactors packed with metal nanoparticles and metal–organic framework catalysts, with measurement errors of less than four per cent of the absolute temperature. These results establish our technique as a non-invasive tool for locating hot and cold spots in catalyst-packed gas–solid reactors, with unprecedented capabilities for testing the approximations used in reactor modelling

Reticular synthesis and the design of new materials

The long-standing challenge of designing and constructing new crystalline solid-state materials from molecular building blocks is just beginning to be addressed with success. A conceptual approach that requires the use of secondary building units to direct the assembly of ordered frameworks epitomizes this process: we call this approach reticular synthesis. This chemistry has yielded materials designed to have predetermined structures, compositions and properties. In particular, highly porous frameworks held together by strong metal-oxygen-carbon bonds and with exceptionally large surface area and capacity for gas storage have been prepared and their pore metrics systematically varied and functionalized.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62718/1/nature01650.pd

Advances in ab-initio theory of Multiferroics. Materials and mechanisms: modelling and understanding

Within the broad class of multiferroics (compounds showing a coexistence of magnetism and ferroelectricity), we focus on the subclass of "improper electronic ferroelectrics", i.e. correlated materials where electronic degrees of freedom (such as spin, charge or orbital) drive ferroelectricity. In particular, in spin-induced ferroelectrics, there is not only a {\em coexistence} of the two intriguing magnetic and dipolar orders; rather, there is such an intimate link that one drives the other, suggesting a giant magnetoelectric coupling. Via first-principles approaches based on density functional theory, we review the microscopic mechanisms at the basis of multiferroicity in several compounds, ranging from transition metal oxides to organic multiferroics (MFs) to organic-inorganic hybrids (i.e. metal-organic frameworks, MOFs)Comment: 22 pages, 9 figure

The Influence of Chemical Modification on Linker Rotational Dynamics in Metal–Organic Frameworks

The robust synthetic flexibility of metal–organic frameworks (MOFs) offers a promising class of tailorable materials, for which the ability to tune specific physicochemical properties is highly desired. This is achievable only through a thorough description of the consequences for chemical manipulations both in structure and dynamics. Magic angle spinning solid‐state NMR spectroscopy offers many modalities in this pursuit, particularly for dynamic studies. Herein, we employ a separated‐local‐field NMR approach to show how specific intraframework chemical modifications to MOF UiO‐66 heavily modulate the dynamic evolution of the organic ring moiety over several orders of magnitude.Ringrotationen in MOFs wurden in Festkörper‐NMR‐Experimenten unter Probenrotation um den magischen Winkel durch dipolare Dephasierung über die Rotorperiode detektiert. Informationen zur Dynamik in Metall‐organischen Gerüsten sind wichtig, weil die Geschwindigkeit der Rotationsbewegung des Linkers die Sorptions‐ und Trenneigenschaften von MOFs beeinflusst.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/144665/1/ange201805004_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/144665/2/ange201805004-sup-0001-misc_information.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/144665/3/ange201805004.pd

Unveiling thermal transitions of polymers in subnanometre pores

The thermal transitions of confined polymers are important for the application of polymers in molecular scale devices and advanced nanotechnology. However, thermal transitions of ultrathin polymer assemblies confined in subnanometre spaces are poorly understood. In this study, we show that incorporation of polyethylene glycol (PEG) into nanochannels of porous coordination polymers (PCPs) enabled observation of thermal transitions of the chain assemblies by differential scanning calorimetry. The pore size and surface functionality of PCPs can be tailored to study the transition behaviour of confined polymers. The transition temperature of PEG in PCPs was determined by manipulating the pore size and the pore–polymer interactions. It is also striking that the transition temperature of the confined PEG decreased as the molecular weight of PEG increased