Stretchable and Degradable Semiconducting Block Copolymers

This paper describes the synthesis and characterization of a class of highly stretchable and degradable semiconducting polymers. These materials are block copolymers (BCPs) in which the semiconducting blocks are based on the diketo­pyrrolopyrrole (DPP) unit flanked by furan rings and the insulating blocks are poly­(ε-caprolactone) (PCL). The combination of stiff conjugated segments with flexible aliphatic polyesters produces materials that can be stretched >100%. Remarkably, BCPs containing up to 90 wt % of insulating PCL have the same field-effect mobility as the pure semiconductor. Spectroscopic (ultraviolet–visible absorption) and morphological (atomic force microscopic) evidence suggests that the semiconducting blocks form aggregated and percolated structures with increasing content of the insulating PCL. Both PDPP and PCL segments in the BCPs degrade under simulated physiological conditions. Such materials could find use in wearable, implantable, and disposable electronic devices

Metallic Nanoislands on Graphene for Monitoring Swallowing Activity in Head and Neck Cancer Patients

There is a need to monitor patients with cancer of the head and neck postradiation therapy, as diminished swallowing activity can result in disuse atrophy and fibrosis of the swallowing muscles. This paper describes a flexible strain sensor comprising palladium nanoislands on single-layer graphene. These piezoresistive sensors were tested on 14 disease-free head and neck cancer patients with various levels of swallowing function: from nondysphagic to severely dysphagic. The patch-like devices detected differences in (1) the consistencies of food boluses when swallowed and (2) dysphagic and nondysphagic swallows. When surface electromyography (sEMG) is obtained simultaneously with strain data, it is also possible to differentiate swallowing <i>vs</i> nonswallowing events. The plots of resistance <i>vs</i> time are correlated to specific events recorded by video X-ray fluoroscopy. Finally, we developed a machine-learning algorithm to automate the identification of bolus type being swallowed by a healthy subject (86.4%. accuracy). The algorithm was also able to discriminate between swallows of the same bolus from either the healthy subject or a dysphagic patient (94.7% accuracy). Taken together, these results may lead to noninvasive and home-based systems for monitoring of swallowing function and improved quality of life