1. Speckle size distribution comparison (90 degrees, 50 mm,532 nm) Speckle size distribution comparison (90 degrees, 40 mm,635 nm) Speckle size distribution comparison (135 degrees, 30 mm,532 nm) Speckle size distribution comparison (135 degrees, 50 mm,635 nm) Distribution comparison (90 degrees, 30 mm,904 nm) Distribution comparison (135 degrees, 50 mm,904 nm) Distribution comparison (135 degrees, 30 mm,904 nm) Towards an optical in-line characterization of nano petals Yiming Ding, Huisung Kim, Euiwon Bae, ME Purdue University, Indiana USA Materials and Preparation IntroductionMethodsResults (cont’d) I wish to acknowledge: Summer University Research Fellowship (SURF) Program National Science Foundation Scalable Nanomanufacuring Grant Iyll-Joon Doh, MS student, Mechanical Engineering, Purdue University Carbon nano petals (CNPs) are a cantilevered multilayer graphene sheet (i.e. petal like growths) that are seeded from a core graphite fiber. The resulting structures offer the possibility of minimizing interfacial losses in transport applications, improved interactions with surrounding matrix materials in composites, and a route toward substrate independence for device applications. For the mass production of these petal-like CNPs on the substrate requires a in-line monitoring system that can provide immediate feedback on the sample status without pulling them out of the production line. With knowledge about visual C programming skills, speckle analysis and auto-correlation, the purpose to identify the optical properties of CNP samples can be obtained with certain equipments such as industrial camera, servo motor and diode laser. Objective Parameters: Angles to set the cameras (90 and 135 degree) Distance between the CCD and the sample (30, 40 and 50 mm) Diode laser wavelength (532, 635 & 904 nm) Sample type Procedures 1.Adjust the distances to be 50 mm for camera #1, 30 mm for camera # 2 2.Fix the 532 nm laser and choose appropriate ND filter (P out = 91.8 μW) 3.Place the CNT with petals sample on the stage. Scan the sample 6 times each row, 6 rows in total, at interval of 0.5 mm (36 points & covers 3×3 mm square) 4.Switch the CNT w/o petals sample and do the scan 5.Replace the laser and ND filter (P out,635 nm = 131 μW, P out,904 nm = 658 μW), repeat steps 3 & 4 6.Adjust the distances and repeat steps 2-5 7.Apply Auto Correlation [1] and DIP method on the speckle patterns To develop a prototype of an inline inspection system for scalable nano- manufacturing, which can inspect small area with a interrogation light To collect typical speckle patterns and build Database for future development To make some general conclusion through speckle analysis on the speckle patterns To make a simple correlation in between the surface condition of the CNT and its speckle pattern for classification Materials: Industrial cameras DMK 23U274 (2) Servo motor (2) Diode lasers Laptop Servo motor controller DC power supply Two samples (w\ petals and on CuFoil) The GUI on the right (made on Microsoft Visual Studio 2008) connects the servo motors and cameras. Results and Conclusion No difference is observed between two samples from 90 degree camera (532 & 635 nm) Graphical user interface Experiment illustrationExperiment setup Typical distribution for 135 degree camera (532 & 635 nm) 904 nm laser gives fluctuated patterns in both cameras As distance between CCD and sample increases, the average speckle size gets bigger Positive correlation between speckle size and laser wavelength Slight (not obvious) increase in speckle size between two different samples Saturation is detected in the scenario of 135 degree & CNT sample without petals (CNT with petals, 135 degrees, 40 mm,904 nm) (CNT without petals, 135 degrees, 30 mm,532 nm) (CNT without petals, 135 degrees, 30 mm,904 nm) [1] D. Chicea, “An alternative algorithm to calculate the biospeckle size in coherent light scattering experiments,” Romanian Journal in Physics, vol. 54, no. 1–2, pp. 147–155, 2009. [2] AC method is not applicable to saturated speckle patterns. At the same condition, DIP method gives inaccurate results