MétaCan
Menu
Back to cohort
Record W4234657189 · doi:10.1149/ma2019-02/16/935

Electrochemical Generation of Metal Nanostructures Using Self-Assembled Monolayers As Templates

2019· article· en· W4234657189 on OpenAlexaff
Zhen Yao, Zhe She, Andrea Di Falco, Michael Buehl, Manfred Buck

Bibliographic record

VenueECS Meeting Abstracts · 2019
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsQueen's University
Fundersnot available
KeywordsCyclic voltammetryChronoamperometryX-ray photoelectron spectroscopyNanoparticleMonolayerElectrochemistryMaterials scienceElectrolyteBimetallic stripNanotechnologyChemical engineeringNoble metalElectrodeChemistryMetalPhysical chemistry

Abstract

fetched live from OpenAlex

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

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.012
GPT teacher head0.236
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting AbstractsSame topicElectrocatalysts for Energy ConversionFrench-language works237,207