N-Graphdiyne as a Tunable Platform for Stabilizing Light Metals toward High-Capacity Reversible Hydrogen Storage
Wael Othman (1,2), Ibrahim Alghoul (3,4), K-F. Aguey-Zinsou5, Nacir Tit (3,4), and Tanveer Hussain (6) ((1) Biomedical Engineering, Biotechnology, Khalifa University, Abu Dhabi, United Arab Emirates (2) Healthcare Engineering Innovation Group (HEIG), Khalifa University

TL;DR
This study demonstrates that nitrogen-doped graphdiyne (N-GDY) can stabilize light metals and enable high-capacity, reversible hydrogen storage through computational modeling, offering a promising material for clean energy applications.
Contribution
The paper introduces N-GDY as a tunable 2D platform for dispersing light metals and achieving high-capacity hydrogen storage, validated by advanced computational methods.
Findings
N-GDY binds multiple light metal atoms with energies exceeding bulk cohesion.
Simulations confirm structural stability and metal dispersion at 400 K.
Systems achieve hydrogen capacities exceeding DOE targets, up to 13.08 wt%.
Abstract
Hydrogen (H2) is a promising carbon-neutral energy carrier. However, its deployment is limited by the lack of lightweight, reversible storage media that operate under practical conditions. Here, we establish nitrogen-doped graphdiyne (N-GDY) as a programmable two-dimensional platform for stabilizing dispersed light-metal dopants and enabling high-capacity physisorption of molecular H2. The computational package involves density functional theory (DFT) combined with ab initio molecular dynamics (AIMD) and Langmuir-based statistical thermodynamic modeling. The results revealed that N-sites of N-GDY bind up to five Li, Na, K, and Ca atoms per primitive cell with binding energies of -2.27, -1.57, -1.80, and -2.13 eV, respectively, exceeding their respective bulk cohesive energies. AIMD simulations at 400 K further confirm the structural robustness of the decorated frameworks and the absence…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
