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Based on the combination of the unique features of both polyionic liquids and spherical colloidal crystals, a new class of inverse opaline spheres with a series of distinct properties was fabricated. It was found that such photonic spheres could not only be used as stimuli-responsive photonic microgels, but also serve as multifunctional microspheres that mimic the main characteristics of conventional molecules, including intrinsic optical properties, specific molecular recognition, reactivity and derivatization, and anisotropy.

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In this article, a new type of electrothermally driven photonic crystal based on liquid crystal elastomers (LCEs) was developed, and its optical properties (structural colour) driven by voltage were described. Graphite nanoparticles were spin-coated on glass-substrates and acted as an electrothermal conversion layer, on which the prepared LCE-based inverse opaline films were mounted. When voltage is applied on the fabricated system, the heat produced by the graphite layer will induce the deformation of the coated inverse opaline film and thus the electrothermally driven photonic system or structural colour is realized. We found that realignment behaviour existed when these films were first above their glass transition temperatures (T g ), and during this realignment process, the structural colour of weakly crosslinked inverse opaline films disappeared, probably due to the collapse of the periodically ordered porous structure. The threshold cross-linking density (C x ) for producing LCE-based inverse opalines with reversible response is 25 mol%. Interestingly, it is found that the treatment of the prepared photonic films by using silicone oil could reduce the threshold C x to 15 mol%, and the fabrication of LCE-based inverse opaline with widely tunable optical properties is possible. When the temperature of the used electrothermal conversion layer is close to the nematic-isotropic (N-I) transition temperature (T NI ) of the LCE films, the liquid crystal moieties in inverse opaline structure became isotropic, leading to rapid shift of the Bragg-diffraction peak and corresponding structural colour change. After turning off the voltage, they could regain to the initial state. With the decrease of the cross-linking density of the photonic-structured elastomers, the degree of Bragg-diffraction shift became larger. Remarkably, the response of these films stimulated by electric voltage is fast and the reversibility is perfect.

Identifying potentially vulnerable locations in a code base is critical as a pre-step for effective vulnerability assessment; i.e., it can greatly help security experts put their time and effort to where it is needed most. Metric-based and pattern-based methods have been presented for identifying vulnerable code. The former relies on machine learning and cannot work well due to the severe imbalance between non-vulnerable and vulnerable code or lack of features to characterize vulnerabilities. The latter needs the prior knowledge of known vulnerabilities and can only identify similar but not new types of vulnerabilities.In this paper, we propose and implement a generic, lightweight and extensible framework, LEOPARD, to identify potentially vulnerable functions through program metrics. LEOPARD requires no prior knowledge about known vulnerabilities. It has two steps by combining two sets of systematically derived metrics. First, it uses complexity metrics to group the functions in a target application into a set of bins. Then, it uses vulnerability metrics to rank the functions in each bin and identifies the top ones as potentially vulnerable. Our experimental results on 11 real-world projects have demonstrated that, LEOPARD can cover 74.0% of vulnerable functions by identifying 20% of functions as vulnerable and outperform machine learning-based and static analysis-based techniques. We further propose three applications of LEOPARD for manual code review and fuzzing, through which we discovered 22 new bugs in real applications like PHP, radare2 and FFmpeg, and eight of them are new vulnerabilities.