Size-Dependent Graphene Support for Decorating Gold Nanoparticles as a Catalyst for Hydrogen Evolution Reaction with Machine Learning-Assisted Prediction.

Size-dependent two-dimensional (2D) materials (e.g., graphene) have been recently used to improve their performance in various applications such as membrane filtration, energy storage, and electrocatalysts. It has also been demonstrated that 2D nanosheets can be one of the promising support materials for decorating nanoparticles (NPs). However, the optimum nanosheet size (lateral length and thickness) for supporting NPs has not yet been explored to enhance their catalytic performance. Herein, we elucidate the mechanism behind size-dependent graphene (GP) as a support due to which gold nanoparticles (AuNPs) are used as an active catalyst for the hydrogen evolution reaction (HER). Surprisingly, the decoration of AuNPs increased with the increasing nanosheet size, counter to what is widely reported in the literature (high surface area for smaller nanosheet size). We found that a large graphene nanosheet (lGP; ∼800 nm) used as the AuNP support (lGP/AuNPs) exhibited superior performance for the HER with long-term stability. The lGP/AuNPs with a suitable content of AuNPs provides a low overpotential and a small Tafel slope, being lower than that of other reported carbon-based HER electrocatalysts. This results from highly exposed active sites of well-dispersed AuNPs on lGP giving high conductivity. The laminar structure of the stacked graphene nanosheets and the high wettability of the lGP/AuNPs electrode surface also play crucial roles in enhancing electrolytes for penetration in the electrode, suggesting a highly electrochemical surface area. Moreover, machine learning (Random Forest) was also used to reveal the essential features of the advanced catalytic material design for catalyst-based applications.

Insight into the effects of different oxygen heteroatoms on nicotine adsorption from cigarette mainstream smoke.

Cigarette smoke contains many chemicals, including nicotine, which is harmful and can cause health problems such as carcinogenesis disease, cardiovascular, respiratory, renal, and reproductive systems. Removal of nicotine from mainstream smoke can be done through adsorption with filters or solid adsorbents. In this study, we explored the use of activated carbons for the removal of nicotine from cigarette mainstream smoke. Activated carbons were prepared from dried hemp (Cannabis sativa) stem at an activation temperature of 350-550 °C using phosphoric acid as an activating agent. The results showed that the activated carbons with variable surface functional groups and porosity exhibited high efficiency for nicotine adsorption, removing 68-88% of nicotine from cigarette mainstream smoke. Through X-ray photoelectron spectroscopy and temperature-programmed desorption analyses, we identified that oxygen-containing functional groups, particularly carboxylic groups, exhibited a superior ability to adsorb nicotine. The computational analysis with DFT simulations further supported the importance of oxygen-containing surface functional groups in facilitating nicotine adsorption, with the carboxylic group providing the lowest adsorption energy among other functional groups.

Unlocking the Full Potential of Heteroatom-Doped Graphene-Based Supercapacitors through Stacking Models and SHAP-Guided Optimization.

Graphene-based supercapacitors have emerged as a promising candidate for energy storage due to their superior capacitive properties. Heteroatom-doping is a method of improving the capacitive properties of graphene-based electrodes, but the optimal doping conditions and electrochemical properties are not yet fully understood due to the synergistic effects that occur. Many parameters, such as doping content, defects, specific surface area (SA), electrolyte, and more, could affect the capacitance (CAP). In this study, we use machine learning to solve these critical issues. We applied many models, such as Light Gradient Boost Machine, Extreme Gradient Boost, Polynomial Regression, Neural Network, Elastic Net, Lasso Regression, Ridge Regression, Random Forest, Support Vector Machine, K-Nearest Neighbors, Gradient Boost, AdaBoost, and Decision Tree, to find a suitable model for CAP prediction. Moreover, we enhance the prediction result by taking advantage of the top candidate model and creating a stacking concept (called "stacking models"). The SHAP value was used to identify the range of properties that affect CAP, and it was discussed in detail. Our results suggest that high-CAP graphene supercapacitors should have a large SA, with 4-5% nitrogen, 10-15% oxygen, high percentages of sulfur, a defect ratio close to 1, with acid electrolyte, and a low current density. These findings, along with the developed model and code, are expected to serve as a valuable computational tool for future electrochemical research from fundamental to applications.

Ultrahigh stable laminar graphene membranes for effective ionic and molecular nanofiltration with a machine learning-assisted study.

Graphene oxide (GO) membranes have gained great attention for water purification due to the formation of stacked nanosheets giving nanocapillary channels. Unlike graphene, the interlayer spacing of GO membranes gets readily expanded in aqueous solution due to their high oxygen content, resulting in poor ion rejection. Herein, we prepared ultralow oxygen-containing graphene (∼1 at%) via facile liquid-phase exfoliation which was formed as membrane laminates. The graphene membranes exhibited ultrahigh stability with no observed swelling or deformation of the laminar structure when kept in water, aqueous salt solutions, and various pH solutions for over one week. The membranes with a high degree of tortuous nanocapillary channels can efficiently reject the ions found in seawater as well as various charged dye molecules. This indicates that the graphene membranes exhibited ionic and molecular sieving properties due to the effect of size exclusion obtained from the narrow nanocapillary channel and electrostatic repulsion from negatively charged graphene nanosheets. Moreover, we also demonstrated machine learning to gain insights into the membrane performance, which allowed us to obtain membrane optimization as a model for water purification technology.

Rotations of F-ATPase and V-ATPase analyzed by a torque approach.

F-type ATP synthase (F-ATPase) and vacuolar ATP hydrolase (V-ATPase) are well-known biomolecular motors, which play significant catalytic roles in ATP synthesis and ATP hydrolysis reactions. Their rotational torques are important factors involved in their rotational behavior that can be measured experimentally but with considerable difficulty. To overcome this difficulty and thereby provide an in-depth understanding of their operation mechanism, we herein carry out simple and fast computer modelling to study the two proteins, using our torque approach that relies on interatomic forces and coordinates of unequilibrated configurations taken from brief molecular dynamics (MD) simulations. As predicted by the torque approach, F-ATPase is demonstrated to be a random rotor, but it prefers to rotate in clockwise direction (as seen from the membrane toward the protein) for ATP synthesis, owing to the predominantly negative angle-averaged torques. By contrast, V-ATPase tends to rotate only in counterclockwise direction for ATP hydrolysis, due to the almost uniform averaged positive torques generated by the unidirectional rotation near the three catalytic sites. The rotational behaviors of both proteins are also affected by the surrounding solvent which can promote or hinder the internal rotation. By combining the torque approach with classic force-field MD simulations, the torques of two biomolecular motors can be calculated economically, and are found to agree with previous experiments and theoretical calculations. This work demonstrates that our torque approach can be extended to the field of biology and can help gain a deeper insight into the mechanistic rotation of biomolecular motors with modest computation time.Communicated by Ramaswamy H. Sarma.

Surface-Mounted Dipolar Molecular Rotors Driven by External Electric Field, As Revealed by Torque Analyses.

Driven by a high-speed rotating electric field (E-field), molecular motors with polar groups may perform a unidirectional, repetitive, and GHz frequency rotation and thus offer potential applications as nanostirrers. To drive the unidirectional rotation of molecular motors, it is crucial to consider factors of internal charge flow, thermal noise, molecular flexibility, and so forth before selecting an appropriate frequency of a rotating E-field. Herein, we studied two surface-mounted dipolar rotors of a "caltrop-like" molecule and a "sandwich" molecule by using quantum-mechanical computations in combination with torque analyses. We find that the rotational trend as indicated by the magnitude and the direction of torque vectors can sensitively change with the lag angle (α) between the dipolar arm and the E-field. The atomic charges timely flow within the molecule as the E-field rotates, so the lag angle α must be kept in particular intervals to maintain the rotor's unidirectional rotation. The thermal effect can substantially slow down the rotation of the dipolar rotor in the E-field. The flexible dipolar arm shows a more rigid geometry in the E-field with higher rotation speed. Our work would be useful for designing E-driven molecular rotors and for guiding their practical applications in future.

Molecular Dynamic Simulation Analysis on the Inclusion Complexation of Plumbagin with ß-Cyclodextrin Derivatives in Aqueous Solution.

Stable encapsulation of medically active compounds can lead to longer storage life and facilitate the slow-release mechanism. In this work, the dynamic and molecular interactions between plumbagin molecule with ß-cyclodextrin (BCD) and its two derivatives, which are dimethyl-ß-cyclodextrin (MBCD), and 2-O-monohydroxypropyl-ß-cyclodextrin (HPBCD) were investigated. Molecular dynamics simulations (MD) with GLYCAM-06 and AMBER force fields were used to simulate the inclusion complex systems under storage temperature (4 °C) in an aqueous solution. The simulation results suggested that HPBCD is the best encapsulation agent to produce stable host-guest binding with plumbagin. Moreover, the observation of the plumbagin dynamic inside the binding cavity revealed that it tends to orient the methyl group toward the wider rim of HPBCD. Therefore, HPBCD is a decent candidate for the preservation of plumbagin with a promising longer storage life and presents the opportunity to facilitate the slow-release mechanism.

Intramolecular Torque Study of a Molecular Rotation Stimulated by Electron Injection and Extraction.

Rotation-inducing torque based on interatomic forces is a true indicator of internal molecular rotations. We use the induced intramolecular torque to study the underlying rotational mechanism stimulated by an electron injection or extraction for the rotor molecule 9-(2,4,7-trimethyl-2,3-dihydro-1 H-inden-1-ylidene)-9 H-fluorene, which consists of a "rotator" fragment and a "stator" fragment. The results show that the charged molecule in a quartet spin state can rotate internally, while that in the doublet state cannot. The torque on the rotator in the quartet state always maintains unidirectional rotation. In addition, the attachment/extraction of an electron leads to the reduction of the rotational energy barrier by about 18 kcal/mol, facilitating a more favorable molecular rotation than in the neutral singlet state. Our finding provides a molecular-level understanding of various transformation pathways for experimental designs and further demonstrates the effectiveness of the torque approach.

Simulation Study of Interactions Between Two Bioactive Components from Zingiber cassumunar and 5-Lipoxygenase.

INTRODUCTION: Compound D and DMPBD are compounds extracted from Plai or Zingiber cassumunar Roxb., which have antiasthmatic properties. Thai herbal pharmacopoeia have indicated that approximate 50% of Thai prescriptions for asthma contain Plai. However, the inhibition mechanisms of these compounds are not clearly known. METHODS: In this study, molecular docking and molecular dynamics (MD) simulations have been used to simulate complex systems and analyze molecular interactions between these compounds and protein target, 5-lipoxygenase (5-LO) enzyme, which is an enzyme involved with asthma symptoms. RESULTS: From our MD simulations, Compound D and DMPBD molecules bind at the same binding site of its natural substrate (arachidonic acid) on 5-LO enzyme, which is similar to the binding of commercial asthma drug (Zileuton). Molecular mechanics generalized born surface area binding energy calculations of the 5-LO complex with Compound D and DMPBD are -26.83 and -29.15 kcal/mol, respectively. CONCLUSIONS: This work indicated that Compound D and DMPBD are competitive inhibitors, which are able to bind at the same 5-LO substrate binding site. This reveals opportunities for using Compound D and DMPBD as novel antiasthmatic drugs.