Continuous Fixed-Bed Column Studies on Congo Red Dye Adsorption-Desorption Using Free and Immobilized Nelumbo nucifera Leaf Adsorbent.

The adsorption of Congo red (CR), an azo dye, from aqueous solution using free and immobilized agricultural waste biomass of Nelumbo nucifera (lotus) has been studied separately in a continuous fixed-bed column operation. The N. nucifera leaf powder adsorbent was immobilized in various polymeric matrices and the maximum decolorization efficiency (83.64%) of CR occurred using the polymeric matrix sodium silicate. The maximum efficacy (72.87%) of CR dye desorption was obtained using the solvent methanol. Reusability studies of free and immobilized adsorbents for the decolorization of CR dye were carried out separately in three runs in continuous mode. The % color removal and equilibrium dye uptake of the regenerated free and immobilized adsorbents decreased significantly after the first cycle. The decolorization efficiencies of CR dye adsorption were 53.66% and 43.33%; equilibrium dye uptakes were 1.179 mg g-1 and 0.783 mg g-1 in the third run of operation with free and immobilized adsorbent, respectively. The column experimental data fit very well to the Thomas and Yoon-Nelson models for the free and immobilized adsorbent with coefficients of correlation R2 ≥ 0.976 in various runs. The study concludes that free and immobilized N. nucifera can be efficiently used for the removal of CR from synthetic and industrial wastewater in a continuous flow mode. It makes a substantial contribution to the development of new biomass materials for monitoring and remediation of toxic dye-contaminated water resources

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

A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics

In drug discovery, molecular dynamics (MD) simulation for protein-ligand binding provides a powerful tool for predicting binding affinities, estimating transport properties, and exploring pocket sites. There has been a long history of improving the efficiency of MD simulations through better numerical methods and, more recently, by utilizing machine learning (ML) methods. Yet, challenges remain, such as accurate modeling of extended-timescale simulations. To address this issue, we propose NeuralMD, the first ML surrogate that can facilitate numerical MD and provide accurate simulations in protein-ligand binding. We propose a principled approach that incorporates a novel physics-informed multi-grained group symmetric framework. Specifically, we propose (1) a BindingNet model that satisfies group symmetry using vector frames and captures the multi-level protein-ligand interactions, and (2) an augmented neural differential equation solver that learns the trajectory under Newtonian mechanics. For the experiment, we design ten single-trajectory and three multi-trajectory binding simulation tasks. We show the efficiency and effectiveness of NeuralMD, with a 2000$\times$ speedup over standard numerical MD simulation and outperforming all other ML approaches by up to 80% under the stability metric. We further qualitatively show that NeuralMD reaches more stable binding predictions compared to other machine learning methods

Formulation of Metal-Organic Framework-Based Drug Carriers by Controlled Coordination of Methoxy PEG Phosphate: Boosting Colloidal Stability and Redispersibility.

Metal-organic framework nanoparticles (nanoMOFs) have been widely studied in biomedical applications. Although substantial efforts have been devoted to the development of biocompatible approaches, the requirement of tedious synthetic steps, toxic reagents, and limitations on the shelf life of nanoparticles in solution are still significant barriers to their translation to clinical use. In this work, we propose a new postsynthetic modification of nanoMOFs with phosphate-functionalized methoxy polyethylene glycol (mPEG-PO3) groups which, when combined with lyophilization, leads to the formation of redispersible solid materials. This approach can serve as a facile and general formulation method for the storage of bare or drug-loaded nanoMOFs. The obtained PEGylated nanoMOFs show stable hydrodynamic diameters, improved colloidal stability, and delayed drug-release kinetics compared to their parent nanoMOFs. Ex situ characterization and computational studies reveal that PEGylation of PCN-222 proceeds in a two-step fashion. Most importantly, the lyophilized, PEGylated nanoMOFs can be completely redispersed in water, avoiding common aggregation issues that have limited the use of MOFs in the biomedical field to the wet form-a critical limitation for their translation to clinical use as these materials can now be stored as dried samples. The in vitro performance of the addition of mPEG-PO3 was confirmed by the improved intracellular stability and delayed drug-release capability, including lower cytotoxicity compared with that of the bare nanoMOFs. Furthermore, z-stack confocal microscopy images reveal the colocalization of bare and PEGylated nanoMOFs. This research highlights a facile PEGylation method with mPEG-PO3, providing new insights into the design of promising nanocarriers for drug delivery

Computational Techniques for Studying Nanoporous Materials

The central goal of this thesis has been the development of new computational techniques to accelerate the discovery and characterization of nanoporous materials. Chaper 1 introduces the field of nanoporous materials/reticular chemistry, provides a brief history of molecular simulation in reticular chemistry, and finally, discusses some key challenges that need to be addressed from a computational perspective. Chapter 2 introduces the objectives of this thesis including the organization of this thesis, and some of the important questions this thesis aims to answer. Chapter 3 begins with a detailed discussion on the theory behind GCMC simulations, including the partition function and the different moves in a GCMC simulation and their associated probabilities. Next, the chapter goes into detail of how the potential energy function *U* is calculated, i.e. force fields, including it’s two main contributing terms, (i) the bonded potential, *Ubonded*, and (ii) the nonbonded potential, *Unonbonded*. Finally, the theory behind the calculation of different geometric properties like the accessible surface area, largest cavity diameter (LCD), and the pore limiting diameter (PLD) are discussed. Chapter 4 introduces our recent advances in HTS to rapidly screen *in silico* the adsorption properties of hundreds of MOFs for CO/N2 separations. Our approach involves the use of a multi-scale toolbox combining high-throughput molecular simulations, data mining and advanced visualization, as well as process system modeling, backed up by experimental validation. Chapter 5 extends the high-throughput screening approach introduced in the previous chapter to rapidly screen the properties of not hundreds, but thousands of MOFs for H2 storage. We also discuss how principal component analysis (PCA) can be used to extract meaningful insights from the vast amount of data generated from such screening studies. We validate our screening approach by synthesizing and evaluating the performance of the selected MOF (HKUST-1) in its monolithic form. Chapter 6 begins with an introduction to Small Angle X-ray Scattering (SAXS) and lattice gas models. Next, we introduce the concept of a monolith, and show experimentally the existence of interparticle mesopores - inaccessible from powders - that push final adsorption capacities above levels expected for single crystals. Finally, we show how lattice-gas models in combination with GCMC simulations can be used to accurately capture the monolithic structure across both the microporous and mesoporous range enabling the robust future predictions of the adsorption characteristics of monolithic materials. Chapter 7 begins with a derivation of the BET equation from first principles. We follow this up with a discussion on how the BET equation can be used to calculate the BET area, i.e. BET method, and some state-of-the-art problems with this method. Finally, we introduce an algorithmic approach called BETSI that addresses some of these problems. Chapter 8 summarizes the key results of this thesis and provides some context on the future outlook and challenges in this field.Cambridge International Scholarship Trinity-Henry Barlow Scholarship (Honorary

Structural Behavior of Isolated Asphaltene Molecules at the Oil–Water Interface

Asphaltenes are the heaviest component of crude oil, causing the formation of a stable oil–water emulsion. Even though asphaltenes are known to behave as an emulsifying agent for emulsion formation, their arrangement at the oil–water interface is poorly understood. We investigated the effect of asphaltene structure (island type vs archipelago type) and heteroatom type (Oxygen-O, Nitrogen-N, and Sulfur-S) on their structural behavior in the oil–water system. Out of six asphaltenes studied here, only three asphaltenes remain at the oil–water interface while others are soluble in the oil phase. Molecular orientation of asphaltene at the interface, position, and angle of asphaltene with the interface has also been determined. We observed that the N-based island type asphaltene is parallel, while the O-based island type asphaltene and N-based archipelago type are perpendicular to the interface. These asphaltene molecules are anchored at the interface by the heteroatom. The S-based asphaltenes (both island and archipelago type) and O-based archipelago type asphaltenes are soluble in the oil phase due to their inability to form a hydrogen bond with water and steric crowding near the heteroatom. This study will help in understanding the role of asphaltenes in oil–water emulsion formation based on its structure and how to avoid it

In Silico Discovery of Covalent Organic Frameworks for Carbon Capture

We screen a database of more than 69,000 hypothetical covalent organic frameworks (COFs) for carbon capture, using parasitic energy as a metric. In order to compute CO2-framework interactions in molecular simulations, we develop a genetic algorithm to tune the charge equilibration method and derive accurate framework partial charges. Nearly 400 COFs are identified with parasitic energy lower than that of an amine scrubbing process using monoethanolamine. Furthermore, we identify over 70 top performers that, based on the same metrics of evaluation, perform comparably to Mg-MOF-74 and outperform reported experimental COFs for this application. We analyze the effect of pore topology on carbon capture performance in order to guide development of improved carbon capture materials

In Silico Discovery of Covalent Organic Frameworks for Carbon Capture

We screen a database of more than 69 000 hypothetical covalent organic frameworks (COFs) for carbon capture using parasitic energy as a metric. To compute CO2-framework interactions in molecular simulations, we develop a genetic algorithm to tune the charge equilibration method and derive accurate framework partial charges. Nearly 400 COFs are identified with parasitic energy lower than that of an amine scrubbing process using monoethanolamine; more than 70 are better performers than the best experimental COFs and several perform similarly to Mg-MOF-74. We analyze the effect of pore topology on carbon capture performance to guide the development of improved carbon capture materials

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

Monolithic Zirconium-Based Metal-Organic Frameworks for Energy-Efficient Water Adsorption Applications

Space cooling and heating, ventilation, and air conditioning (HVAC) accounts for roughly 10% of global electricity use and are responsible for ca. 1.13 gigatonnes of CO emissions annually. Adsorbent-based HVAC technologies have long been touted as an energy-efficient alternative to traditional refrigeration systems. However, thus far, no suitable adsorbents have been developed which overcome the drawbacks associated with traditional sorbent materials such as silica gels and zeolites. Metal-organic frameworks (MOFs) offer order-of-magnitude improvements in water adsorption and regeneration energy requirements. However, the deployment of MOFs in HVAC applications has been hampered by issues related to MOF powder processing. Herein, three high-density, shaped, monolithic MOFs (UiO-66, UiO-66-NH, and Zr-fumarate) with exceptional volumetric gas/vapor uptake are developed-solving previous issues in MOF-HVAC deployment. The monolithic structures across the mesoporous range are visualized using small-angle X-ray scattering and lattice-gas models, giving accurate predictions of adsorption characteristics of the monolithic materials. It is also demonstrated that a fragile MOF such as Zr-fumarate can be synthesized in monolithic form with a bulk density of 0.76 gcm without losing any adsorption performance, having a coefficient of performance (COP) of 0.71 with a low regeneration temperature (≤ 100 °C)