Extensive Multiscale Investigations of Li/Na-Functionalized Three-Dimensional Borophosphene for Enhanced Hydrogen Storage
Bibliographic record
Abstract
Three-dimensional porous borophosphene (3D-B 2 P 2 ) has emerged as a promising material for battery applications. Beyond this, its inherently porous crystal structure, along with excellent thermal and mechanical stability, suggests a broader potential. Therefore, in this work, we extend its application scope by systematically exploring its suitability for hydrogen storage using density functional theory (DFT) calculations and ab initio molecular dynamics (AIMD) simulations by exploiting the polarizing mechanism induced by alkali atoms. Particularly, our findings demonstrate that Li (−4.16 eV) and Na (−3.31 eV) bind strongly to 3D-B 2 P 2 driven by pronounced charge transfer. This phenomenon enables the polarization of up to five H 2 molecules per metal atom, yielding capacities of 7.07 and 6.36 wt %, meeting the DOE’s targets, with optimal average adsorption energies for reversible storage (−0.152 and −0.105 eV/H 2 ). Furthermore, the functionalized systems exhibit very low H 2 diffusion barriers (0.0056–0.12 eV) ensuring efficient kinetics. Pressure–H 2 capacity–temperature-dependent studies reveal that the Li-functionalized system achieves an effective, reversible gravimetric capacity of 5.74 wt % at 298.15 K and 46 bar, aligning with practical application requirements. AIMD simulations confirm stable hydrogen cycling under ambient conditions. Thus, experimental investigations of 3D-B 2 P 2 as a potential material for reversible H 2 storage are recommended.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".