KXH 3 (X = Ca, Sc, Ti, Ni) Hydride Perovskites: A DFT Study for Physical Properties and Hydrogen Storage Capability
Bibliographic record
Abstract
Abstract The present study has been performed with the help of density functional theory to investigate structural, electronic, hydrogen storage, mechanical, thermal, and optical properties of KXH3 (X = Ca, Sc, Ti, & Ni) hydride perovskites. The lattice parameters are calculated by using the GGA-PBE functional and are found as 4.482 Å, 4.154 Å, 3.974 Å, and 3.686 Å for KCaH3, KScH3, KTiH3, and KNiH3, respectively. The electronic properties reveal that all the materials exhibit metallic behavior except KCaH3, which shows a semiconducting behavior. The population analysis suggests these compounds can store hydrogen due to their strong bonds and long bond lengths. The dynamic and mechanical stability predict that studied materials can be experimentally synthesized as the materials are thermodynamically and mechanically stable. The gravimetric ratio of hydrogen storage capacities has been calculated as 3.646 wt%, 3.452 wt%, 3.346 wt%, and 3.005 wt% for KCaH3, KScH3, KTiH3, and KNiH3, respectively. The calculated temperatures for hydrogen desorption are as follows: 442.40 K for KCaH3, 518.68 K for KScH3, 592.47 K for KTiH3, and 614.82 K for KNiH3, the formation energy was analyzed in the range − 57.822 to -80.358 KJ/mol.H2. These parameters suggest that all the materials are capable of hydrogen storage applications.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".