MétaCan
Menu
Back to cohort
Record W2273928705 · doi:10.1149/ma2016-01/39/1983

All-Solution-Processed Wrinkled Gold Nanoparticles As Sensors

2016· article· en· W2273928705 on OpenAlexaff
Christine M. Gabardo, Jie Yang, Nathaniel J. Smith, Chris Adams-McGavin, Leyla Soleymani

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMaterials scienceNanotechnologySubstrate (aquarium)Colloidal goldLayer (electronics)NanoparticleRaman scatteringFabricationRaman spectroscopyThin filmOptics

Abstract

fetched live from OpenAlex

Design and Methodology : Materials with feature sizes spanning the nano- and micro-scale range have been sought after by materials scientists and engineers to address specific functional demands unmet by bulk materials, in fields ranging from energy to sensors. Surface-enhanced Raman scattering (SERS) based sensors for molecular detection rely on nano- and micro-structuring of metallic substrates to amplify the intrinsically weak Raman signal, however fabricating SERS substrates can be tedious and time consuming. Our vision was to create a versatile and simple approach to fabricate SERS substrates. Wrinkling of a thin film on a compliant substrate is a rapid and inexpensive fabrication method for controllably creating materials with features covering several lengthscales. [1] Bioprocessing and biosensing devices have been fabricated using wrinkling with sputtered thin films, [2–4] however we sought to develop a method to create tunable wrinkled materials for SERS without the use of complex, vacuum-based deposition systems. First, a layer of gold nanoparticles was formed on an amino-silane treated heat-shrinkable substrate using self-assembly. Then, wrinkling of the nanoparticle layer was induced by heating the coated polymer substrate over its glass transition temperature, causing the footprint of the substrate to be reduced to 16% of the original area, while exerting a compressive force on the gold nanoparticle layer. The mechanical stress was relieved through the buckling of the gold nanoparticle layer on the surface of the substrate. Original Data and Results : The wrinkling behaviour of the gold nanoparticle film was assessed before shrinking, after shrinking uniaxially (by physically constraining two sides of the substrate), and after shrinking biaxially using scanning electron microscopy (SEM), atomic force microscopy (AFM), and transmission electron microscopy (TEM). Well-ordered uniaxial and biaxial nanoparticle wrinkles were produced across the entire surface of the substrate. In order to control the wavelength and amplitude of the resulting wrinkles, nanoparticles of different diameters (~12 nm, ~18 nm, and ~36 nm) were deposited as the film layer. In addition, we assessed the effect of depositing multiple layers of ~12 nm nanoparticles on the resulting wrinkled structures. We observed that by increasing the diameter of the nanoparticles or by increasing the number of deposited nanoparticle layers, the wavelength and amplitude of the wrinkles could be controllably increased (Figure 1). These wrinkle structured nanoparticle surfaces were applied as SERS substrates, using 4-mercaptopyridine as the target analyte. By tuning the wavelength and morphology of the wrinkled structures, using different sized nanoparticles or multiple nanoparticle layers, the enhancement factor of the various SERS substrates was altered and optimized. Conclusions: We have developed a benchtop all-solution-processing method to create wrinkled metallic nano-/microstructures, tunable in size and morphology, on polymer substrates. By altering the nanoparticle diameters and number of deposited layers, we were able to control the amplitude and wavelength of the resulting wrinkled structures. Moreover, using physical constraints during the shrinking/wrinkling process allows for further regulation of the wrinkle morphology. We have demonstrated that these structures can used to create optical sensors as they were successfully applied as tunable SERS substrates to specifically detect a target analyte, and we envision these nanoparticle polymer composites finding other applications in chemical sensors, biosensors, and optoelectronics. [1] A. Schweikart, A. Horn, A. Böker, A. Fery, Adv. Polym. Sci. 2010 , 227 , 75. [2] S. M. Woo, C. M. Gabardo, L. Soleymani, Anal. Chem. 2014 , 86 , 12341. [3] C. M. Gabardo, A. M. Kwong, L. Soleymani, Analyst 2015 . [4] A. Hosseini, L. Soleymani, Appl. Phys. Lett. 2014 , 105 , 074102. 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 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)0.000
metaresearch head score (Gemma)0.000
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 score0.022
Threshold uncertainty score0.700

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.014
GPT teacher head0.230
Teacher spread0.217 · 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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting AbstractsSame topicAdvanced Materials and MechanicsFrench-language works237,207