Defect-Engineered Beryllium Dinitride (BeN2) Monolayer with Light-Metal Decoration for Reversible High-Capacity Hydrogen Storage
Wael Othman (1,2), Ibrahim Alghoul (3,4), K-F. Aguey-Zinsou (5), 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 introduces a defect-engineered BeN2 monolayer with light-metal decoration that achieves high-capacity, reversible hydrogen storage exceeding DOE targets, promising advancements in energy storage materials.
Contribution
The paper presents a novel defect-engineering approach in BeN2 monolayers enabling stable, high-capacity hydrogen storage through light-metal functionalization and vacancy stabilization.
Findings
Achieved gravimetric H2 storage capacities of up to 11.64 wt%.
Confirmed thermal stability of metal-decorated frameworks at 400 K.
Demonstrated reversible H2 adsorption within practical conditions.
Abstract
Hydrogen (H2) possesses the highest gravimetric energy density of any chemical fuel and is the most abundant element in the universe. However, its extremely low volumetric energy density at standard conditions imposes a fundamental materials challenge for safe, efficient, and reversible storage. Here, we report a defect-engineered 2D beryllium dinitride (BeN2) monolayer that enables stable light-metal functionalization for high-capacity H2 storage. A 2 x 2 supercell containing two intrinsic beryllium vacancies accommodates four Li, Na, and K atoms without clustering, exhibiting strong average metal-vacancy binding energies of -3.80, -2.94, and -3.18 eV, respectively. Ab initio molecular dynamics simulations at 400 K confirm the thermal stability of the metal-decorated frameworks and the suppression of metal aggregation. The vacancy-stabilized alkali-metal centers generate localized…
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.
