Electrochemical Generation of Metal Nanostructures Using Self-Assembled Monolayers As Templates
Bibliographic record
Abstract
Controlling electrochemical processes by molecular self-assemblies offers interesting perspectives for nanotechnology as precision and flexibility intrinsic to electrochemistry combines favourably with the range of possibilities for chemical, electronic, and structural modification of electrodes by molecular systems. We exploit this combination for metal electrodeposition using a coordination-controlled deposition scheme. Based on the reduction of a two-dimensional (2D) layer of metal ions coordinated to the tail groups of a self-assembled monolayer (SAM) (1-3), it is extended by using the nanoparticles formed initially from the 2D layer as seeds for deposition from the bulk (3D) electrolyte. A number of opportunities for the generation of nanoscale deposits arise from this particular 2D/3D scheme which, depending on conditions, range from isolated nanoparticles to continuous layers on top of the SAM. Furthermore, by choosing different species coordinated to the SAM and present in the bulk electrolyte, bimetallic nanoparticles can be synthesized. Exploring this coordination-controlled deposition scheme towards the generation of ultra-small metal structures, we investigate the combination of Pd and Cu, which is of interest for the generation of both electrocatalytically active PdCu nanoparticles and membranes for hydrogen separation (4). Deposition is accomplished via complexation of Pd2+ ions to a pyridine-terminated thiol SAM on a Au (111) electrode, followed by an electrochemical reduction in an acidic Cu2+-electrolyte. Cyclic voltammetry (CV), chronoamperometry, and X-ray photoelectron spectroscopy (XPS) reveal three phases, Pd nanoparticle formation, seeding of Cu deposition and formation of a Pd/Cu alloy, followed by the deposition of bulk Cu. To shed light on the initial stage of Pd deposition, density functional theory (DFT) calculations were performed. Contrasting other SAM based deposition schemes which rely on defects in the monolayer (5,6), the coordination controlled deposition harnesses a molecular property, thus providing a better control over the deposition process. The generation of nanostructures by templated electrodeposition was investigated using either SAMs patterned by electron beam lithography or patterns of PdCu nanoparticles produced by selective removal with tips of a scanning tunneling or atomic force microscope. Metal layers with an average thickness of less than 3 nm and structures with lateral dimensions ranging from the micrometer to the sub-10 nanometer range can be formed with this technique. References C. Silien, D. Lahaye, M. Caffio, R. Schaub, N. R. Champness and M. Buck, Langmuir, 27, 2567 (2011). T. Baunach, V. Ivanovo, D. M. Kolb, H. G. Boyen, P. Ziemann, M. Büttner and P. Oelhafen, Adv. Mater., 16, 2024 (2004). O. Shekhah, C. Busse, a Bashir, F. Turcu, X. Yin, P. Cyganik, a Birkner, W. Schuhmann and C. Wöll, Phys. Chem. Chem. Phys., 8, 3375 (2006). L. Mattarozzi, S. Cattarin, N. Comisso, R. Gerbasi, P. Guerriero, M. Musiani and E. Verlato, Electrochim. Acta, 230, 365 (2017). Z. She, A. Di Falco, G. Hähner and M. Buck, Appl. Surf. Sci., 373, 51 (2016). Z. She, A. Di Falco, G. Hähner and M. Buck, Beilstein J. Nanotechnol, 3, 101 (2012). Figure 1
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".