Silver Exchange Dynamics in Monolayer-Protected Doped Gold Clusters
Bibliographic record
Abstract
The mechanism of inter-cluster exchange reactions remains an open problem in nanoparticle chemistry. Such reactions, first reported in 2015, involve the exchange of metal atoms between ligand-protected nanoparticles in solution and have enabled the synthesis of heterometallic clusters with precise dopant counts. While previous computational studies have proposed plausible exchange pathways, none have explicitly captured the dynamics of a cluster-cluster collision. Here, we use a direct dynamics approach combined with quantum-based semiempirical potentials to simulate collisions between silver-doped and undoped gold nanoparticles and to follow atom exchange events in real time. The simulations reveal that restructuring at the core-monolayer interface is a key initiating step, enabling exposure of core metal atoms. Subsequent transfer of silver between clusters is mediated by thiolate ligands, which stabilize the migrating atom through successive metal-sulfur interactions across the inter-cluster region. Beyond elucidating the exchange mechanism, this work demonstrates a general strategy for modeling reactive collisions and large-scale dynamical processes in nanomaterials.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".