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Controlled quantum well formation on DNA-wrapped carbon nanotubes via peroxide-mediated aryl diazonium reduction
No full textQuantum well defect-modified single walled carbon nanotubes are environmentally sensitive nanomaterials with wide-ranging applications in biosensing, imaging, light harvesting, quantum computing, energy storage and catalysis. The most common method for covalent functionalization of nanotubes for biosensing applications involves reactions with aryl diazonium salts to generate sp3 aryl defect sites, commonly followed by wrapping with single stranded DNA. We describe herein a rapid aryl diazonium functionalization reaction directly compatible with DNA-wrapped nanotubes. The reaction uses mild aqueous conditions at physiological pH and can be easily monitored in real-time via fluorescence analysis to control the degree of functionalization. Overall, this reaction greatly simplifies the production of covalently functionalized DNA-wrapped carbon nanotubes, expanding their potential for industrial and biomedical applications
Lattice Models in Molecular Thermodynamics: Merging the Configurational and Translational Entropies
No full textLattice models are a central idea in statistical thermodynamics, as they allow for the introduction of a configurational entropy by counting the multiplicity of placing indistinguishable particles on a lattice. In this work we use a lattice model to show that the configurational entropy needs to be merged with the molecular translational entropy in order to have a consistent model. This is achieved by replacing the volume of a lattice site with a quantum volume (i.e., the cube of the thermal wavelength). This modified lattice model allows us to derive a new equation of state, referred to as the Bragg-Williams equation state, from which we may also derive a generalized version of the van der Waals equation of state. In contrast to the standard van der Waals equation of state, the heat capacity of the new models has temperature dependence
Acquiring and Transferring Comprehensive Catalyst Knowledge through Integrated High-Throughput Experimentation and Automatic Feature Engineering
No full textSolid catalyst development has traditionally relied on trial-and-error approaches, limiting the broader application of valuable insights across different catalyst families. To overcome this fragmentation, we introduce a framework that integrates high-throughput experimentation (HTE) and automatic feature engineering (AFE) with active learning to acquire comprehensive catalyst knowledge. The framework is demonstrated for oxidative coupling of methane (OCM), where active learning is continued until the machine learning model achieves robustness for each of the BaO-, CaO-, La2O3-, TiO2-, and ZrO2-supported catalysts, with 333 catalysts newly tested. The resulting models are utilized to extract catalyst design rules, revealing key synergistic combinations in high-performing catalysts. Moreover, we propose a method for transferring knowledge between supports, showing that features refined on one support can improve predictions on others. This framework advances the understanding of catalyst design and promotes reliable machine learning
Machine Learning Applications in Drug Discovery
No full textIntegrating machine learning (ML) into drug discovery has ushered in a new era of innovation, dramatically enhancing the efficiency and precision of identifying and developing new therapeutics. This review provides a comprehensive analysis of the current applications of machine learning in drug discovery, focusing on its transformative impact across various stages of the drug development pipeline. We delve into key ML methodologies, including supervised and unsupervised learning, neural networks, and reinforcement learning, examining their underlying principles and specific contributions to drug discovery processes. By exploring case studies and recent advancements, this review illustrates how ML algorithms have been utilized to predict drug-target interactions, optimize drug design, and streamline clinical trial processes. Furthermore, we discuss the challenges and limitations of implementing ML techniques in this field and highlight emerging trends and future directions. This review aims to offer researchers a thorough understanding of ML\u27s potential to revolutionize drug discovery and equip them with the insights needed to leverage these technologies effectively
Symmetry is the key to the design of reticular frameworks
No full textDe novo prediction of reticular framework structures is a challenging task for chemists and materials scientists. Herein, a computational workflow that predicts a list of possible reticular frameworks based on only the connectivity and symmetry of node and linker building blocks is presented. This list is ranked based on the occurrence of topologies in known structures, thus providing a manageable number of structures that can be optimized using density functional theory, and inform future experiments. This workflow is broadly applicable, correctly predicts known reticular materials, and furthermore identifies heretofore unknown phases for some systems. This workflow is available online at https://rationaldesign.pythonanywhere.com
Intermediate Knowledge Enhanced the Performance of N-Amide Coupling Yield Prediction Model
No full textAmide coupling is an important reaction widely applied in medicinal chemistry. However, condition recommendation remains a challenging issue due to the broad conditions space. Recently, accurate condition recommendations via machine learning have emerged as a novel and efficient method to find a suitable condition to achieve the desired transformations. Nonetheless, accurately predicting yields is challenging due to the complex relationships involved. Herein, we present our strategy to address this problem. Two steps were taken to ensure the quality of the dataset. First, we selected a diverse and representative set of substrates to capture a broad spectrum of substrate structures and reaction conditions using an unbiased machine-based sampling approach. Second, experiments were conducted using our in-house high-throughput experimentation (HTE) platform to minimize the influence of human factors. Additionally, we proposed an intermediate knowledge-embedded strategy to enhance the model\u27s robustness. The performance of the model was first evaluated at three different levels—random split, partial substrate novelty, and full substrate novelty. All model metrics in these cases improved dramatically, achieving an R2 of 0.89, MAE of 6.1%, and RMSE of 8.0% in full substrate novelty test dataset. Moreover, the generalization of our strategy was assessed using external datasets from reported literature. The prediction error for 18 reactions among 88 was less than or equal to 5%. Meanwhile, the model could recommend suitable conditions for some reactions to elevate the reaction yields. Besides, the model was able to identify which reaction in a reaction pair with a reactivity cliff had a higher yield. In summary, our research demonstrated the feasibility of achieving accurate yield predictions through the combination of HTE and embedding intermediate knowledge into the model. This approach also has the potential to facilitate other related machine learning tasks
Impact of Sensor Placement on Indoor Air Quality Monitoring: A Comparative Analysis
No full textThis study investigates the impact of sensor placement on the accuracy and responsiveness of indoor air quality (IAQ) monitoring focusing on particulate matter concentrations. Measurements were conducted in a controlled environment using three intercalibrated sensors positioned at different locations: a wall-mounted sensor installed at 1.2 meters above the ground, a sensor placed at the inlet of an air purifier, and a sensor located at breathing height in the center of the room. A particle source was introduced at four different points within the room to simulate varying pollution scenarios. The results revealed that the wall-mounted sensor exhibited delays of up to 200 seconds in detecting peak pollutant concentrations compared to the sensor near the air purifier. Additionally, the wall-mounted sensor consistently recorded lower pollutant levels compared to the other two sensors. The findings underscore the critical importance of strategic sensor placement for accurate and real-time IAQ monitoring. Placing sensors closer to breathing zones and pollution sources provides data that more accurately reflects human exposure risks. The study concludes that wall-mounted sensors may not provide real-time air quality data in dynamic indoor environments. Further research, including computational fluid dynamics (CFD) simulations, is recommended to optimize sensor placement strategies
Tensor Train Optimization for Conformational Sampling of Organic Molecules
No full textExploring the conformational space of molecules remains a challenge of fundamental importance to quantum chemistry: identification of relevant conformers at ambient conditions enables predictive simulations of almost arbitrary properties. Here, we propose a novel approach to enable conformational sampling of large organic molecules where the combinatorial explosion of possible conformers prevents the use of a brute-force systematic conformer search. We employ tensor trains as a highly efficient dimensionality reduction algorithm, effectively reducing the scaling from exponential to polynomial. In our approach, the conformational search is expressed as global energy minimization task in a high-dimensional grid of dihedral angles. Dimensionality reduction is achieved through a tensor train representation of the high-dimensional torsion space. The performance of the approach is assessed on a variety of drug-like molecules in direct comparison to the state-of-the-art metadynamics based conformer rotamer ensemble sampling tool (CREST). The comparison shows significant acceleration of up to an order of magnitude, while maintaining comparable accuracy. More importantly, the presented approach allows treatment of larger molecules than typically accessible with metadynamics
Mechanosynthesis of ruthenium trisbipyridyl complexes and application in photoredox catalysis in a ball-mill
No full textHerein, we developed the mechanosynthesis of ruthenium trisbipyridyl complexes. Such complexes can be difficult to prepare in solution, with long reaction times and average yields. With ball-milling, less than 3.5 hours of milling were sufficient to obtain the complexes in high yield. Such complexes were then evaluated as catalysts in the light-promoted mechanochemical reductive dehalogenation reaction. In addition to working under solvent-less conditions, the use of a Hantzsch amide instead of the classical ester allowed to drastically simplify the purification of the final compounds
Formation of H2O2 in Near-Neutral Zn-air Batteries Enables Efficient Oxygen Evolution Reaction
No full textRechargeable Zn-air batteries with near-neutral electrolytes hold promise as cheap, safe and sustainable devices, but they suffer from slow charge kinetics and remain poorly studied. Here we reveal a charge storage mechanism of near-neutral Zn-air batteries that is mediated by H2O2 formation upon cell discharge and its oxidation upon charge. The manifestation of this mechanism strongly depends on the electrolyte composition and positive electrode material, being pronounced when ZnSO4 solutions and carbon nanotubes are employed. Oxidation of dissolved H2O2 is facile, enabling oxygen evolution reaction (OER) at low potentials (~1.5 V vs. Zn2+/Zn) which, in contrast to conventional four-electron OER, does not induce corrosion of carbon electrodes. Facilitation of the H2O2-mediated pathway might therefore be helpful for developing high-performance near-neutral Zn-air batteries