A Comparative Density Functional Theory Study of Hydrogen Storage in Cellulose and Chitosan Functionalized by Transition Metals (Ti, Mg, and Nb)
Bibliographic record
Abstract
The focus of this work is hydrogen storage in pristine cellulose, chitosan, and cellulose. Chitosan doped with magnesium, titanium, and niobium is analyzed using spin unrestricted plane-wave density functional theory implemented in the Dmol3 module. The results of this study demonstrate that hydrogen interaction with pure cellulose and chitosan occurred in the gas phase, with an adsorption energy of Eb = 0.095 eV and 0.090 eV for cellulose and chitosan, respectively. Additionally, their chemical stability was determined as Eb= 4.63 eV and Eb = 4.720 eV for pure cellulose and chitosan, respectively, by evaluating their band gap. Furthermore, the presence of magnesium, titanium, and niobium on cellulose and chitosan implied the transfer of an electron from metal to cellulose and chitosan. Moreover, our calculations predict that cellulose doped with niobium is the most favorable medium where 6H2 molecules are stored compared with molecules stored in niobium-doped chitosan with Tmax = 818 K to release all H2 molecules. Furthermore, our findings showed that titanium-doped cellulose has a storage capacity of five H2 molecules, compared to a storage capacity of four H2 molecules in titanium-doped chitosan. However, magnesium-doped cellulose and chitosan have insufficient hydrogen storage capacity, with only two H2 molecules physisorbed in the gas phase. These results suggest that niobium-doped cellulose and chitosan may play a crucial role in the search for efficient and inexpensive hydrogen storage media.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".