Insight into the nature of carbon–metal bonding for <i>N</i>-heterocyclic carbenes in gold/silver complexes and nanoparticles using DFT-correlated Raman spectroscopy: strong evidence for π-backbonding
Bibliographic record
Abstract
SCAN-3c method allows an excellent correlation between experimental and predicted Raman spectra which helps calibrate an accurate description of NHC-metal bonding. While π-backbonding should stabilize the NHC-metal bond, conflicting reports for the presence and absence of π-backbonding are seen in the literature. This debate led us to further investigate experimental and theoretical results to ultimately confirm and quantify the presence of π-backbonding in these systems. Experimentally, an observed decrease in the NHC's CN stretching due to the population of the π* orbital is a good indication for the presence of π-backbonding. Using energy decomposition analysis - natural orbitals for chemical valence (EDA-NOCV), our calculations concur and quantify π-backbonding in these NHC-bound complexes and NPs. Surprisingly, we observe that NPs are less stabilized by π-backbonding compared to their respective complexes-a result that partially explains the weaker NHC-NP bond. The protocol described herein will help optimize metal-carbon bonding in NHC-stabilized metal complexes, nanoparticles and surfaces.
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) | 0.004 | 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".