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Graphene and its functionalised derivatives are transforming the development of biosensors that are capable of detecting nucleic acid hybridization. Using a Molecular Dynamics (MD) approach, we explored single-stranded or double-stranded deoxyribose nucleic acid (ssDNA or dsDNA) adsorption on two graphenic species: graphene oxide (GO) and reduced graphene oxide functionalized with aminated polyethylene glycol (rGO-PEG-NH2). Innovatively, we included chloride (Cl−) and magnesium (Mg2+) ions that influenced both the ssDNA and dsDNA adsorption on GO and rGO-PEG-NH2 surfaces. Unlike Cl−, divalent Mg2+ ions formed bridges between the GO surface and DNA molecules, promoting adsorption through electrostatic interactions. For rGO-PEG-NH2, the Mg2+ ions were repulsed from the graphenic surface. The subsequent ssDNA adsorption, mainly influenced by electrostatic forces and hydrogen bonds, could be supported by π–π stacking interactions that were absent in the case of dsDNA. We provide a novel insight for guiding biosensor development.

Clean energy technologies represent a hot topic for research communities worldwide. Hydrogen fuel, a prized alternative to fossil fuels, displays weaknesses such as the poisoning by impurities of the precious metal catalyst which controls the reaction involved in its production. Thus, separating H2 out of the other gases, meaning CH4, CO, CO2, N2, and H2O is essential. We present a rotating partially double-walled carbon nanotube membrane design for hydrogen separation and evaluate its performance using molecular dynamics simulations by imposing three discrete angular velocities. We provide a nano-perspective of the gas behaviors inside the membrane and extract key insights from the filtration process, pore placement, flux, and permeance of the membrane. We display a very high selectivity case (ω = 180° ps−1) and show that the outcome of Molecular Dynamics (MD) simulations can be both intuitive and counter-intuitive when increasing the ω parameter (ω = 270° ps−1; ω = 360° ps−1). Thus, in the highly selective, ω = 180° ps−1, only H2 molecules and 1–2 H2O molecules pass into the filtrate area. In the ω = 270° ps−1, H2, CO, CH4, N2, and H2O molecules were observed to pass, while, perhaps counter-intuitively, in the third case, with the highest imposed angular velocity of 360° ps−1 only CH4 and H2 molecules were able to pass through the pores leading to the filtrate area.

Hydrogen fuel cells rely on the purity of the hydrogen gas for maintaining a high performance. This study investigates a novel nanostructure design for its effectiveness in separating H2 molecules from a mixture of gases containing H2, CH4, CO2, N2, CO and H2O molecules using Molecular Dynamics simulations. Based on an open-ended (28, 0) rotating carbon nanotube with one carbon nanocone at each of its two extremes, this device is predicted through Molecular Dynamics simulations to be able to separate hydrogen from a gas mixture contained within. The nanocones were placed with their tips inside the nanotube and the size of the open channel created between the two was controlled to find a configuration that allows hydrogen to pass while restricting the other gases. Although in need of optimization, we find it capable of high selectivity and highlight captivating gas behavior insights to help advance rational gas separation device development.

Computational methods can greatly aid nucleic acid fluorescence experiments by either offering fully detailed atomic insights into the conformations and interactions present in the studied system or by providing accurate simulations of the fundamental parameters. Fluorescence-based optical biosensors show great potential for clinical diagnosis of life-altering diseases with a very high specificity. Many of the designs for such rely on the concept of Förster resonance energy transfer (FRET). Currently, the methods used experimentally make use of theoretical assumptions which fundamentally affect the results. Having a detailed atomistic overview or significant simulated parameters could improve the understanding of the calculations and provide much more accurate outcomes. However, there are many challenges that need to be addressed before standardized computational protocols can be employed. This review is meant to highlight the progress made for computational methods used to simulate FRET experiments for nucleic acid probes. Recent advances have been made in computational tools, such as force field parametrizations and improved protocols. Complementary simulations to experimental data are also comprised in the this review.

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