Liquid Crystal Networks Meet Water: It's Complicated!

Soft robots are composed of compliant materials that facilitate high degrees of freedom, shape-change adaptability, and safer interaction with humans. An attractive choice of material for soft robotics is crosslinked networks of liquid crystal polymers (LCNs), as they are responsive to a wide variety of external stimuli and capable of undergoing fast, programmable, complex shape morphing, which allows for their use in a wide range of soft robotic applications. However, unlike hydrogels, another popular material in soft robotics, LCNs have limited applicability in flooded or aquatic environments.

Plasticized liquid crystal networks and chemical motors for the active control of power transmission in mechanical devices.

The miniaturization of mechanical devices poses new challenges in powering, actuation, and control since traditional approaches cannot be used due to inherent size limitations. This is particularly challenging in untethered small-scale machines where independent actuation of multicomponent and multifunctional complex systems is required. This work showcases the integration of self-powered chemical motors and liquid crystal networks into a powertrain transmission device to achieve orthogonal untethered actuation for power and control.

Monitoring of polycyclic aromatic hydrocarbon contamination at four oil spill sites using fluorescence spectroscopy coupled with parallel factor-principal component analysis.

Fluorescence spectroscopy analysis of oil and environmental samples collected from four oil spill incidents in Canada-a 2016 pipeline spill into the North Saskatchewan River (NSR), Saskatchewan; a 2015 train derailment in Gogama, Ontario; the 1970 sinking of the SS Arrow ship in Chedabucto Bay, Nova Scotia; and the 1970 sinking of the Irving Whale barge in the Gulf of St. Lawrence-permitted assessment of the PAH content of environmentally weathered samples. A recently developed fluorescence fingerprinting model based on excitation-emission matrix-parallel factor analysis-principal component analysis (EEM-PARAFAC-PCA) was applied to (i) evaluate the intensity of the abundant PAH groups in the samples, (ii) investigate changes in the PAH composition of environmental samples over time due to weathering, and (iii) classify the original spilled oil and environmental samples within the already established classes of the fingerprinting PCA model.