Iron Complex-Catalyzed Ammonia–Borane Dehydrogenation. A Potential Route toward B–N-Containing Polymer Motifs Using Earth-Abundant Metal Catalysts
Bibliographic record
Abstract
Ammonia-borane (NH(3)BH(3), AB) has garnered interest as a hydrogen storage material due to its high weight percent hydrogen content and ease of H(2) release relative to metal hydrides. As a consequence of dehydrogenation, B-N-containing oligomeric/polymeric materials are formed. The ability to control this process and dictate the identity of the generated polymer opens up the possibility of the targeted synthesis of new materials. While precious metals have been used in this regard, the ability to construct such materials using earth-abundant metals such as Fe presents a more economical approach. Four Fe complexes containing amido and phosphine supporting ligands were synthesized, and their reactivity with AB was examined. Three-coordinate Fe(PCy(3))[N(SiMe(3))(2)](2) (1) and four-coordinate Fe(DEPE)[N(SiMe(3))(2)](2) (2) yield a mixture of (NH(2)BH(2))(n) and (NHBH)(n) products with up to 1.7 equiv of H(2) released per AB but cannot be recycled (DEPE = 1,2-bis(diethylphosphino)ethane). In contrast, Fe supported by a bidentate P-N ligand (4) can be used in a second cycle to afford a similar product mixture. Intriguingly, the symmetric analogue of 4 (Fe(N-N)(P-P), 3), only generates (NH(2)BH(2))(n) and does so in minutes at room temperature. This marked difference in reactivity may be the result of the chemistry of Fe(II) vs Fe(0).
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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".