ArticleJournal of molecular modeling2026
Electrostatic-field-modulated gas dynamics in charged nanotubes.
Article in Journal of molecular modeling, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
contextThe behaviour of gas molecules in nanopore materials is essential for research in gas separation, catalytic reactions, and nanofluidic transport. However, controlling gas dynamics at the nanoscale, especially in charged nanopores, remains a significant challenge. Through molecular dynamics simulations, this study reveals that external electric fields can synergise with the surface properties of charged nanotubes to regulate the distribution and aggregation behaviour of gases inside the nanotubes. Increasing the wall charge density of the nanopore leads to gas aggregation and the formation of nanobubbles at the tube centre, thereby obstructing water flow. In contrast, applying an axial external electric field weakens the stability of nitrogen bubbles, causing collapse and reopening the liquid transport channel. The formation and distribution of ordered water layers on nanopore walls is a key influencing factor on the dynamic behaviour of gases within nanotubes. This interfacial structure enables regulating gas adsorption and desorption, as well as liquid flow blockage or activation, by the combined modulation of surface charge and external electric fields. These insights provide a reference for designing electrically controlled nanofluidic valves in electrically charged nanopore materials, a technology with strong relevance to applications such as gas storage, sensing, and integrated nanofluidic systems.
methodsClassical molecular dynamics simulations were performed using GROMACS 2021.5 in NVT ensemble at 300 K for the water-N₂ system in an uncapped (40,40) carbon nanotube. The nanotube wall was implemented by alternating ± q charges on diagonally adjacent sites of neighbouring hexagons (q = 0-1.4 e/atom in 0.2 e/atom increments), resulting in a net neutral nanotube. The GROMOS43a1 force field was employed for system interactions, and water molecules were modelled using the SPC/E model. Electrostatic interactions were handled using the Particle-Mesh Ewald (PME) method with a real-space cutoff of 10 Å; van der Waals forces were similarly truncated at 10 Å. A uniform static axial electric field in the + z direction was simultaneously applied, with magnitudes varying from 0.00 V/Å to 0.10 V/Å at an interval of 0.01 V/Å. Data, including density distribution, water flux, and water molecule orientation/hydrogen bond counts, were extracted via Python/C + + scripts. Model Structure and trajectories were visualised using VMD 1.9.3 software.
Indexed as
Identifiers
41915290What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.