High-throughput electrochemical sensing platform for screening nanomaterial-biomembrane interactions.

A high-throughput, automated screening platform has been developed for the assessment of biological membrane damage caused by nanomaterials. Membrane damage is detected using the technique of analyzing capacitance-current peak changes obtained through rapid cyclic voltammetry measurements of a phospholipid self-assembled monolayer formed on a mercury film deposited onto a microfabricated platinum electrode after the interaction of a biomembrane-active species. To significantly improve wider usability of the screening technique, a compact, high-throughput screening platform was designed, integrating the monolayer-supporting microfabricated electrode into a microfluidic flow cell, with bespoke pumps used for precise, automated control of fluid flow. Chlorpromazine, a tricyclic antidepressant, and a citrate-coated 50 nm diameter gold nanomaterial (AuNM) were screened to successfully demonstrate the platform's viability for high-throughput screening. Chlorpromazine and the AuNM showed interactions with a 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) monolayer at concentrations in excess of 1 µmol dm-3. Biological validity of the electrochemically measured interaction of chlorpromazine with DOPC monolayers was confirmed through quantitative comparisons with HepG2 and A549 cytotoxicity assays. The platform also demonstrated desirable performance for high-throughput screening, with membrane interactions detected in <6 min per assay. Automation contributed to this significantly by reducing the required operating skill level when using the technique and minimizing fluid consumption.

Robots testing robots: ALAN-Arm, a humanoid arm for the testing of robotic rehabilitation systems.

Robotics is increasing in popularity as a method of providing rich, personalized and cost-effective physiotherapy to individuals with some degree of upper limb paralysis, such as those who have suffered a stroke. These robotic rehabilitation systems are often high powered, and exoskeletal systems can attach to the person in a restrictive manner. Therefore, ensuring the mechanical safety of these devices before they come in contact with individuals is a priority. Additionally, rehabilitation systems may use novel sensor systems to measure current arm position. Used to capture and assess patient movements, these first need to be verified for accuracy by an external system. We present the ALAN-Arm, a humanoid robotic arm designed to be used for both accuracy benchmarking and safety testing of robotic rehabilitation systems. The system can be attached to a rehabilitation device and then replay generated or human movement trajectories, as well as autonomously play rehabilitation games or activities. Tests of the ALAN-Arm indicated it could recreate the path of a generated slow movement path with a maximum error of 14.2mm (mean = 5.8mm) and perform cyclic movements up to 0.6Hz with low gain (<1.5dB). Replaying human data trajectories showed the ability to largely preserve human movement characteristics with slightly higher path length and lower normalised jerk.