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Record W4252426561 · doi:10.1149/ma2018-01/37/2203

From Salt to Germanene: A Cookbook for Electrochemical Formation of 2D Materials (Inspired by R. Adžić)

2018· article· en· W4252426561 on OpenAlexaff
Jakub Drnec, John L. Stickney, David A. Harrington

Bibliographic record

VenueECS Meeting Abstracts · 2018
Typearticle
Languageen
FieldMaterials Science
TopicMachine Learning in Materials Science
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsGermaneneSiliceneGrapheneMonolayerNanotechnologyIonic bondingMaterials scienceExfoliation jointUnderpotential depositionChemical vapor depositionChemical physicsChemistryElectrochemistryIonElectrodePhysical chemistryCyclic voltammetryOrganic chemistry

Abstract

fetched live from OpenAlex

The re-discovery of graphene[1], until now the most studied 2D material, opened a large playground for scientists to study exotic properties of confined electrons. A few monolayers thick 2D sheets of insulating or semiconducting materials with various atomic compositions rapidly became a hot topic. Such sheets were shown to behave differently compared to their 3D counterparts, in large part due to the different electronic structure arising from the 2D nature of the material. They bear great promise for applications in electronics and optoelectronics, sensors, composite materials, photovoltaics, medicine, quantum dots, energy storage and cryptography. The most renowned 2D material is certainly graphene, but its analogs silicene and germanene have also drawn increased attention due to their potentially simpler incorporation in current semiconductor electronics. There are several means of preparation for 2D materials: exfoliation or cleavage, chemical vapor deposition (CVD) and, as recently shown, also by underpotential deposition (UPD) [2]. Because standard UPD of transition metals also effectively results in formation of 2D layers, it might therefore be possible to explain the recently observed electrochemical formation of germanene by some of the principles discovered by R. Adžić et al. regarding UPD growth. They suggested that depending on the nature of the bond within the 2D layer (ionic vs. covalent) one can perhaps predict the general 2D structure of the layer (mixed monolayer vs. bilayer) [3]. We tested this idea in both UHV and electrochemical environments and found that this suggestion indeed holds in the case of truly ionic CsI layers [4]. The CsI layers, prepared in UHV by coadsorption of Cs and iodine, undergo several phase transitions from mixed monolayer with a honeycomb motif through to a bilayer. Simple electrostatic calculations showed that this evolution is expected from a theoretical perspective. To further test the hypothesis, we also prepared 2D CsO layers which are also strongly ionic due to the high oxygen electronegativity. Indeed, the observed 2D structures are the same as in the CsI case pointing to the universality of the hypothesis proposed by R. Adžić et al. These results later sparked the idea that, by following the same principles, the honeycomb structure could be patterned onto the surface also for different elements because the presence of the anion in the electrolyte forces the layer to be deposited in the honeycomb arrangements. To show that this approach is feasible, we deposited germanium on Au(111) as the UPD has been studied before and it is still difficult to prepare germanene in UHV environment. We found that indeed the germanium deposition results in a honeycomb layer after following a certain protocol [2]. Furthermore the growth process was also characterized by STM, Raman spectroscopy and Surface X-ray diffraction (SXRD). [1] K. S. Novoselov, A. K. Geim, S. V. Morozov, D. Jiang, Y. Zhang, S. V. Dubonos, I. V. Grigorieva, A. A. Firsov, Science , 306 (2004), 666–669 [2] M. Ledina, N. Bui, X. Liang, Y. -G. Kim, J. Jung, B. Perdue, C. Tsang, J. Drnec, F. Carla, M. P. Soriaga, T. J. Reber, J. L. Stickney, J. Electrochem. Soc. , 164 (2017), D469-D477 [3] J. X. Wang, I. K. Robinson, J. E. DeVilbiss and R. Adžić, J. Phys. Chem. B, 104 (2000), 7951–7959 [4] J. Drnec and D. A. Harrington, Surface Science, 604 (2010), 2106–2115; J. Drnec and D. A. Harrington, Surface Science, 630 (2014), 9-15

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.026
Threshold uncertainty score0.898

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.267
Teacher spread0.256 · 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 teacher head, 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
Published2018
Admission routes1
Has abstractyes

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