All-Solution-Processed Wrinkled Gold Nanoparticles As Sensors
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".