DFT Study of Gold Surfaces–Ligand Interactions: Alkanethiols versus Halides
Bibliographic record
Abstract
The variation in the surface reactivity of gold nanorods (GNR) is explored by density functional theory (DFT) simulations of the different facets. The physical and chemical properties of Au310 and Au520 surfaces, recently detected on the GNR tips, were compared with conventional Au100 and Au111 surfaces. Due to the tight gold packing, the sloped Au111 surfaces have the highest binding energy with rigid ligands, whereas the Au310 have the lowest. The observed correspondence between the ligand binding energies and the surface moduli of elasticity demonstrates that the overall monolayer structure, stability and reactivity is a balance between the ligand induced surface strain and the interligand strain. The GNR tips are able to distribute the ligand induced strain with minimal surface reorganization due to a high modulus of elasticity combined with extra free volume within the ligand shell. A simulation of the halide-surface interaction, consisting of multiple bond breaking and forming points, showed that the halides will induce the same surface strain but halides with reduced ligand-to-ligand interaction can move freely around the gold surface. More importantly, the charge transfer between halide to gold depends mainly on this mobility and is a factor in the role of halides as shape directing agents in gold nanoparticle synthesis. These simulations reveal some of the key factors that must be considered to effectively functionalize GNRs for specific applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".