Iron Nanoparticles Catalyzing the Asymmetric Transfer Hydrogenation of Ketones
Bibliographic record
Abstract
Investigation into the mechanism of transfer hydrogenation using trans-[Fe(NCMe)CO(PPh(2)C(6)H(4)CH═NCHR-)(2)][BF(4)](2), where R = H (1) or R = Ph (2) (from R,R-dpen), has led to strong evidence that the active species in catalysis are iron(0) nanoparticles (Fe NPs) functionalized with achiral (with 1) and chiral (with 2) PNNP-type tetradentate ligands. Support for this proposition is given in terms of in operando techniques such as a kinetic investigation of the induction period during catalysis as well as poisoning experiments using substoichiometric amounts of various poisoning agents. Further support for the presence of Fe(0) NPs includes STEM microscopy imaging with EDX analysis, XPS analysis, and SQUID magnetometry analysis of catalytic solutions. Further evidence of Fe NPs acting as the active catalyst is given in terms of a polymer-supported substrate experiment whereby the NPs are too large to permeate the pores of a functionalized polymer. Final support is given in terms of a combined poisoning/STEM/EDX experiment whereby the poisoning agent is shown to be bound to the Fe NPs. This paper provides evidence of a rare example of asymmetric catalysis with nonprecious metal, zerovalent nanoparticles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".