Role of the Quantum Interactions in H<sub>2</sub> Adsorption on Late Transition Metal Chelated Linkers of Covalent Organic Frameworks
Bibliographic record
Abstract
Abstract Transition metal (Tm) chelation is an effective strategy to achieve optimal binding enthalpy (▵H) of H2‐adsorption in the linkers of covalent organic frameworks (COFs). The first principle‐based DFT method has been implemented to determine the H2 adsorption in nine organic linkers chelated with transition metal atoms from Cr to Zn. The obtained range of binding enthalpy for single H2 adsorbed on the pure and chelated complexes is −7 to −20 kJ/mol, which is required for onboard H2 storage. The Linker‐3 chelated with Ni (II) metal exhibits the most favorable binding enthalpy of approximately −18.72 kJ/mol for the single adsorbed H2 molecule, which falls within the physisorption range. Some of the complexes have shown the binding enthalpy range between physisorption and chemisorption, i. e., in that case, H2 binds via Kubas interactions. However, physisorption‐based complexes are preferable to others because physisorption is a reversible process with rapid kinetics. This study reveals that the dispersion, polarization, and electrostatic interactions mainly contribute to the binding enthalpy of H2 adsorption. Molecular surface potential analysis verifies the origin of induced dipole moment in the H2 molecule, which enhances the hydrogen adsorption in transition metal chelated COFs.
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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".