In recent years, graphene has been widely utilised as a supercapacitor electrode, and doping heteroatom on graphene is reported to enhance the pseudocapacitance of the electrode materials significantly resulting in a high energy density. However, the relationship and charge storage mechanism of a so-called “synergistic effect” between those doped atoms including oxygen-, nitrogen-, and sulphur-doping on supercapacitor performances remain inscrutable. In this study, a machine learning model – artificial neural network (ANN) is used to predict the capacitance of heteroatom-doped graphene-based supercapacitors and establish the effects of heteroatom-doping. Trained ANN can accurately predict the capacitance of the electrode, drawing the best synthesis conditions for the heteroatom doped graphene. Furthermore, we successfully demonstrate the synergistic effect that arises from co-doping nitrogen, sulphur, and locate the optimised region for N/S-co-doping with high capacitance, and high retention rate. Machine learning methods allow us to consider a much larger space of heteroatom-doping combinations to maximise the supercapacitor performances and provide a useful guideline for co-doping graphene-based supercapacitors.

There has been a great deal of interest in the electrochemical intercalation of two-dimensional materials in recent years, with studies of transition metal dichalcogenides (TMDs), a so-called “beyond graphene” material,...

This work succeeded in the preparation of graphene-based membranes with ultrahigh stability in water, high salt concentration and seawater, and also studied the machine leaning-based ion permeability.

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 À26.83 and À29.15 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.

Ion transport is a significant concept that underlies a variety of technologies including membrane technology, energy storages, optical, chemical, and biological sensors and ion-mobility exploration techniques. These applications are based on the concepts of capacitance and ion transport, so a prior understanding of capacitance and ion transport phenomena is crucial. In this review, the principles of capacitance and ion transport are described from a theoretical and practical point of view. The review covers the concepts of Helmholtz capacitance, diffuse layer capacitance and space charge capacitance, which is also referred to as quantum capacitance in low-dimensional materials. These concepts are attributed to applications in the electrochemical technologies such as energy storage and excitable ion sieving in membranes. This review also focuses on the characteristic role of channel heights (from micrometer to angstrom scales) in ion transport. Ion transport technologies can also be used in newer applications including biological sensors and multifunctional microsupercapacitors. This review improves our understanding of ion transport phenomena and demonstrates various applications that is applicable of the continued development in the technologies described.

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.

Two-dimensional materials (e.g. graphene, and transition metal dichalcogenides) have become ubiquitous in electrochemical contexts including energy storage, electrocatalyst, and ion-selective membranes. This is due to its superior electrochemical properties, specifically “capacitance”, which can be referred to the storage ions at the electrolyte/materials interfaces. Experimental work and computational chemistry were carried out in the past decade for solving and improving the understanding of two-dimensional materials; however, these techniques are relatively expensive, complex, and time-consuming. Therefore, we accentuate the future trend of two-dimensional material study with machine learning as the modest alternative. In this perspective, the intrinsic capacitance properties of the two dimension materials were described from an atomic level, explaining the heteroatom doping to a nanoscopic level, showing (basal vs edge capacitance). The studies also extended to the macroscopic level i.e., the flake size of the two-dimensional materials. We then shed more light on the applicability of machine learning coupled with the “fundamental measurement” for solving electrochemistry of two-dimensional materials. The shallow artificial neural network was demonstrated for the prediction of CV curves using the data from size-dependent graphene. In addition, the application of deep neural networks with complicated architecture has also been explored through the prediction of capacitance for heteroatom-doped graphene. This perspective provides a clear background and creates the connection between fundamental measurement and machine learning for understanding the capacitance properties of two-dimensional materials.

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 β-cyclodextrin (BCD) and its two derivatives, which are dimethyl-β-cyclodextrin (MBCD), and 2-O-monohydroxypropyl-β-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.