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Record W2257207188 · doi:10.1149/ma2015-01/14/1138

Rapid Prototyping of Multi-Scale Electrodes on Polymer Surfaces

2015· article· en· W2257207188 on OpenAlexaff
Christine M. Gabardo, Leyla Soleymani

Bibliographic record

VenueECS Meeting Abstracts · 2015
Typearticle
Languageen
FieldEngineering
TopicNanofabrication and Lithography Techniques
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMaterials scienceNanotechnologyMicroscale chemistryTemplateNanomaterialsElectrodeFabricationRapid prototypingSubstrate (aquarium)LithographyMicrofabricationOptoelectronicsComposite material

Abstract

fetched live from OpenAlex

Rapid prototyping of multi-scale electrodes on polymer surfaces Multiscale electrodes that combine features in multiple lengthscales – from the millimetre to the nanometer scale – are essential for developing a wide range of electrical and optoelectronic devices including solar cells1 and biosensors2. While nanomaterials, used in the active region of such devices, have shown to enhance device performance, these often have to be interfaced with wires and contact pads in the milimeter or micrometer lengthscales for use in practical devices3. Although electrodeposition is a simple, inexpensive and rapid method for the fabrication of tunable nanomaterials, it often has to be combined with templates for organizing the nanostructures in the desired device-specific geometries4. Templates created by machining or lithographic processing often add significantly to the complexity and cost of the manufacturing process, and require long design to production times. Self-assembled templates are simple and inexpensive and can be rapidly created; however, they often have shortcomings in terms of controllability of geometry and size5. Here we demonstrate a facile method for developing multiscale electrodes by combining electrodeposition with templates created using benchtop and rapid prototyping methods. Macroscale patterning is performed using a craft cutter to define electrode patterns on a self-adhesive vinyl film immobilized on a pre-stressed polystyrene substrate. Microscale patterning is induced by shrinking metallic thin films through heating the pre-stressed substrate. Tunable nanostructuring is achieved by manipulating the parameters of the electrodeposition process. The surface structure of these electrodes was characterized using scanning electron microscopy and white light interference microscopy, while their electrical and electrochemical properties were characterized using the four-point-probe and voltammetric methods. The multiscale electrodes fabricated here were shown to be tunable in terms of structure, sheet resistance, and electroactive surface area. Furthermore, complex electrode structures were created for a variety of biomedical applications including electrochemical detection, magnetic separation, and bacterial lysis with a design to fabrication time of a few hours. (1) Battaglia, C.; Escarré, J.; Söderström, K.; Charrière, M.; Despeisse, M.; Haug, F.-J.; Ballif, C. Nanomoulding of Transparent Zinc Oxide Electrodes for Efficient Light Trapping in Solar Cells. Nat. Photonics 2011, 5, 535–538. (2) Soleymani, L.; Fang, Z. C.; Sargent, E. H.; Kelley, S. O. Programming the Detection Limits of Biosensors through Controlled Nanostructuring. Nat. Nanotechnol. 2009, 4, 844–848. (3) Soleymani, L.; Fang, Z.; Sun, X.; Yang, H.; Taft, B. J.; Sargent, E. H.; Kelley, S. O. Nanostructuring of Patterned Microelectrodes to Enhance the Sensitivity of Electrochemical Nucleic Acids Detection. Angew. Chem. Int. Ed. Engl. 2009, 48, 8457–8460. (4) Plasmonic, T. N.; Halpern, A. R.; Corn, R. M. Lithographically Patterned Electrodeposition of Gold , Silver , and Resonances. 2013, 1755–1762. (5) Fan, Z.; Razavi, H.; Do, J.; Moriwaki, A.; Ergen, O.; Chueh, Y.-L.; Leu, P. W.; Ho, J. C.; Takahashi, T.; Reichertz, L. a; et al. Three-Dimensional Nanopillar-Array Photovoltaics on Low-Cost and Flexible Substrates. Nat. Mater. 2009, 8, 648–653. 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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.025
GPT teacher head0.253
Teacher spread0.228 · 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
GenreMethods

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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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