Reconfigurable photonic crystals enabled by pressure-responsive shape-memory polymers

Smart shape-memory polymers can memorize and recover their permanent shape in response to an external stimulus (for example, heat). They have been extensively exploited for a wide spectrum of applications ranging from biomedical devices to aerospace morphing structures. However, most of the existing shape-memory polymers are thermoresponsive and their performance is hindered by heat-demanding programming and recovery steps. Although pressure is an easily adjustable process variable such as temperature, pressure-responsive shape-memory polymers are largely unexplored. Here we report a series of shape-memory polymers that enable unusual ‘cold' programming and instantaneous shape recovery triggered by applying a contact pressure at ambient conditions. Moreover, the interdisciplinary integration of scientific principles drawn from two disparate fields—the fast-growing photonic crystal and shape-memory polymer technologies—enables fabrication of reconfigurable photonic crystals and simultaneously provides a simple and sensitive optical technique for investigating the intriguing shape-memory effects at nanoscale.


Supplementary Tables
Supplementary Table 1  were chosen to compare the Young's modulus of different indentation forces/depths. With each force, ten impressions were indented on each sample with an inter-distance of 200 m, which is ten times over the average residual impression size. All indentations were triggered by 7.5 N force, corresponding to ~2 nm deflection in the indenter spring. Overall, 30 indents were made on each sample. All indents were made at room temperature (23 C) and the system was allowed to reach thermal equilibrium for 30 minutes prior to indentation to minimize the thermal drift effect.
To calculate the Young's modulus, the force-displacement curves obtained from indentation experiments were fitted with the Oliver-Pharr model 1 in 80%  20% portion of the unloading curves. The calculations were conducted using IGOR Pro analysis software routine (WaveMetrics Inc.). The fitting curves are in power law function form: (1) where α and m are power law fitting constants. h f is the final depth of the contact impression after unloading.
The contact depth is defined as the difference of the maximum indentation depth and the sink-in depth 5 (2) where ϵ is the indenter geometry parameter and S is the measured unloading stiffness, which is defined as where is a dimensionless parameter used to account for deviations in stiffness caused by lack of axial symmetry, A c is the projected contact area of the indenter with repect to contact depth h c , and E eff is the effective (reduced) Young's modulus defined by (5) where E s , ν s and E i , v i are Young's modulus and Poisson's ratio of sample and indenter, respectively. R is the indenter tip radius.
According to the Oliver-Pharr method 1 and the manufacturer's specifications, several parameters used in our case were set as . AFM surface microstructure characterization and roughness analysis. Amplitudemodulation atomic force microscopy (Asylum Research, Inc.) was used to characterize the topography of the macroporous SMP membranes. All AM-AFM imaging was performed using the MFP-3D AFM with a Nanosensor PPP-NCHR probe (tip radius < 10 nm). A total of 5 images were scanned in different locations on each sample with an average interval larger than 100 . All the , and images were scanned with a data collection density of pixels per image. For all images presented, the trace and retrace images of the topography matched excellently, ensuring the absence of image artifacts and the 6 accuracy of the data collected. Both the sample preparation and imaging were performed at room temperature (~ 23 °C) and relative humidity ~ 50%.
All the surface topographic images and the surface roughness were generated and calculated in the commercial software package Scanning Probe Imaging Processor (SPIP). A 1 st order plane correction was performed to compensate for surface tilt. Cross-sectional profiles were measured from AFM imaging data to provide quantitative information, such as feature heights and lengths. The AFM images and the height profile scanned across the dashed line shown in Supplementary Fig. 2 illustrate the difference in the surface microstructures between the fingerprint valleys and ridges printed on a macroporous SMP membrane with 300 nm macropores.
The surface roughness was determined by both the arithmetic average (AA) roughness and the root mean square (RMS) roughness using AFM topographic images. Each average roughness datum was calculated from 15 roughness values  3 samples with 5 different locations on each. Two different approaches were implemented to characterize the difference between the whole area (including pores) roughness and only non-porous area roughness: (i) 3D areal roughness: This method includes whole surface area and porous features.
(ii) Linear profile roughness: This method extracts out several lines without porous features.
The roughness values were calculated according to the ASME B46.1. The formulas used in calculating the roughness are: