First-Principles Density Functional Theory (DFT) Study of Gold Nanorod and Its Interaction with Alkanethiol Ligands
Bibliographic record
Abstract
The structure and mechanical properties of gold nanorods and their interactions with alkenthiolate self-assembled monolayers have been determined using a novel first-principle density functional theory simulation approach. The multifaceted, 1-dimensional, octagonal nanorod has alternate Au100 and Au110 surfaces. The structural optimization of the gold nanorods was performed with a mixed basis: the outermost layer of gold atoms used double-ζ plus polarization (DZP), the layer below used double-ζ (DZ), and the inner layers used single-ζ (SZ). The final structure compares favorably with simulations using DZP for all atoms. Phonon dispersion calculations and ab initio molecular dynamics (AIMD) were used to establish the dynamic and thermal stability of the system. From the AIMD simulations it was found that the nanorod system will undergo significant surface reconstruction at 300 K. In addition, when subjected to mechanical stress in the axial direction, the nanorod responds as an orthotropic material, with uniform expansion along the radial direction. The Young's moduli are 207 kbar in the axial direction and 631 kbar in the radial direction. The binding of alkanethiolates, ranging from methanethiol to pentanethiol, caused formation of surface point defects on the Au110 surfaces. On the Au100 surfaces, the defects occurred in the inner layer, creating a small surface island. These defects make positive and negative concavities on the gold nanorod surface, which helps the ligand to achieve a more stable state. The simulation results narrowed significant knowledge gaps on the alkanethiolate adsorption process and on their mutual interactions on gold nanorods. The mechanical characterization offers a new dimension to understand the physical chemistry of these complex 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".