Nanoporous graphene-based thin-film microelectrodes for in vivo high-resolution neural recording and stimulation.

One of the critical factors determining the performance of neural interfaces is the electrode material used to establish electrical communication with the neural tissue, which needs to meet strict electrical, electrochemical, mechanical, biological and microfabrication compatibility requirements. This work presents a nanoporous graphene-based thin-film technology and its engineering to form flexible neural interfaces. The developed technology allows the fabrication of small microelectrodes (25 µm diameter) while achieving low impedance (∼25 kΩ) and high charge injection (3-5 mC cm-2). In vivo brain recording performance assessed in rodents reveals high-fidelity recordings (signal-to-noise ratio >10 dB for local field potentials), while stimulation performance assessed with an intrafascicular implant demonstrates low current thresholds (<100 µA) and high selectivity (>0.8) for activating subsets of axons within the rat sciatic nerve innervating tibialis anterior and plantar interosseous muscles. Furthermore, the tissue biocompatibility of the devices was validated by chronic epicortical (12 week) and intraneural (8 week) implantation. This work describes a graphene-based thin-film microelectrode technology and demonstrates its potential for high-precision and high-resolution neural interfacing.

A cross-reactive plasmonic sensing array for drinking water assessment.

The continuous monitoring of remote drinking water purification systems is a global challenge with direct consequences for human and environmental health. Here, we utilise a "nano-tastebud" sensor comprised of eight chemically-tailored plasmonic metasurfaces, for testing the composition of drinking water. Through undertaking a full chemometric analysis of the water samples and likely contaminants we were able to optimise the sensor specification to create an array of suitable tastebuds. By generating a unique set of optical responses for each water sample, we show that the array-based sensor can differentiate between untreated influent and treated effluent water with over 95% accuracy in flow and can detect compositional changes in distributed modified tap water. Once fully developed, this system could be integrated into water treatment facilities and distribution systems to monitor for changes in water composition.

Full-bandwidth electrophysiology of seizures and epileptiform activity enabled by flexible graphene microtransistor depth neural probes.

Mapping the entire frequency bandwidth of brain electrophysiological signals is of paramount importance for understanding physiological and pathological states. The ability to record simultaneously DC-shifts, infraslow oscillations (<0.1 Hz), typical local field potentials (0.1-80 Hz) and higher frequencies (80-600 Hz) using the same recording site would particularly benefit preclinical epilepsy research and could provide clinical biomarkers for improved seizure onset zone delineation. However, commonly used metal microelectrode technology suffers from instabilities that hamper the high fidelity of DC-coupled recordings, which are needed to access signals of very low frequency. In this study we used flexible graphene depth neural probes (gDNPs), consisting of a linear array of graphene microtransistors, to concurrently record DC-shifts and high-frequency neuronal activity in awake rodents. We show here that gDNPs can reliably record and map with high spatial resolution seizures, pre-ictal DC-shifts and seizure-associated spreading depolarizations together with higher frequencies through the cortical laminae to the hippocampus in a mouse model of chemically induced seizures. Moreover, we demonstrate the functionality of chronically implanted devices over 10 weeks by recording with high fidelity spontaneous spike-wave discharges and associated infraslow oscillations in a rat model of absence epilepsy. Altogether, our work highlights the suitability of this technology for in vivo electrophysiology research, and in particular epilepsy research, by allowing stable and chronic DC-coupled recordings.

Whisky tasting using a bimetallic nanoplasmonic tongue.

Metallic nanostructures are ideal candidates for optical tongue devices thanks to their chemical stability, the sensitivity of their plasmonic resonance to environmental changes, and their ease of chemical-functionalization. Here, we describe a reusable optical tongue comprising multiplexed gold and aluminum nano-arrays: a bimetallic device which produces two distinct resonance peaks for each sensing region. Through specific modification of these plasmonic arrays with orthogonal surface chemistries, we demonstrate that a dual-resonance device allows us to halve sensor sizes and data-acquisition times when compared to single-resonance, monometallic devices. We applied our bimetallic tongue to differentiate off-the-shelf whiskies with >99.7% accuracy by means of linear discriminant analysis (LDA). This advance in device miniaturization, functionalization, and multiplexed readout indicates nanoplasmonic tongues will have future applications in chemical mixture identification in applications where portability, reusability, and measurement speed are key.

Multilayered Nanoplasmonic Arrays for Self-Referenced Biosensing.

Nanostructured sensors based on localized surface plasmon resonance (LSPR) offer a number of advantages over other optical sensing technologies, making them excellent candidates for miniaturized, label-free chemical and biological detection. Highly sensitive to local refractive index changes, the resonance peaks of the nanosensors shift by different amounts when subject to different biological and chemical environments. Modifications to the nanostructure surface allow for the detection of specific molecules and chemicals with shifts so sensitive that the presence of single molecules can be detected. However, this extreme sensitivity has its drawbacks. Resonance shifts also occur because of temperature shifts, light-intensity fluctuations, and other environmental factors. To distinguish detection from drift, a secondary sensor region is often required. This often doubles the size of the device, requires two light sources and detectors (or complex optics), doubles the sample volume required (which may be expensive, or may not be possible if the sample quantity is limited), and subjects the reference to potential biofouling. Here, we present a new proof-of-concept multilayered LSPR sensor design that incorporates both a sensing layer and an encapsulated reference layer within the same region. By doing so, we are able to monitor and correct for sensor drift without the need for a secondary reference channel. We demonstrate the suitability of this sensor for sucrose concentration measurements and for the detection of biotin-avidin interactions, while also showing that the sensor can self-correct for drift. We believe that this multilayer sensor design holds promise for point-of-care diagnostics.

Bridging the Gap: Rewritable Electronics Using Real-Time Light-Induced Dielectrophoresis on Lithium Niobate.

In the context of micro-electronics, the real-time manipulation and placement of components using optics alone promises a route towards increasingly dynamic systems, where the geometry and function of the device is not fixed at the point of fabrication. Here, we demonstrate physically reconfigurable circuitry through light-induced dielectrophoresis on lithium niobate. Using virtual electrodes, patterned by light, to trap, move, and chain individual micro-solder-beads in real-time via dielectrophoresis, we demonstrate rewritable electrical contacts which can make electrical connections between surface-bound components. The completed micro-solder-bead bridges were found to have relatively low resistances that were not solely dominated by the number of interfaces, or the number of discrete beads, in the connection. Significantly, these connections are formed without any melting/fusing of the beads, a key feature of this technique that enables reconfigurability. Requiring only a low-power (~3.5 mW) laser source to activate, and without the need for external power supply or signal generation, the all-optical simplicity of virtual-electrodes may prove significant for the future development of reconfigurable electronic systems.

Reversible DNA micro-patterning using the fluorous effect.

We describe a new method for the immobilisation of DNA into defined patterns with sub-micron resolution, using the fluorous effect. The method is fully reversible via a simple solvent wash, allowing the patterning, regeneration and re-patterning of surfaces with no degradation in binding efficiency following multiple removal/attachment cycles of different DNA sequences.

Temporal microstructure of cortical networks (TMCN) underlying task-related differences.

Neuro-electromagnetic recording techniques (EEG, MEG, iEEG) provide high temporal resolution data to study the dynamics of neurocognitive networks: large scale neural assemblies involved in task-specific information processing. How does a neurocognitive network reorganize spatiotemporally on the order of a few milliseconds to process specific aspects of the task? At what times do networks segregate for task processing, and at what time scales does integration of information occur via changes in functional connectivity? Here, we propose a data analysis framework-Temporal microstructure of cortical networks (TMCN)-that answers these questions for EEG/MEG recordings in the signal space. Method validation is established on simulated MEG data from a delayed-match to-sample (DMS) task. We then provide an example application on MEG recordings during a paired associate task (modified from the simpler DMS paradigm) designed to study modality specific long term memory recall. Our analysis identified the times at which network segregation occurs for processing the memory recall of an auditory object paired to a visual stimulus (visual-auditory) in comparison to an analogous visual-visual pair. Across all subjects, onset times for first network divergence appeared within a range of 0.08-0.47 s after initial visual stimulus onset. This indicates that visual-visual and visual auditory memory recollection involves equivalent network components without any additional recruitment during an initial period of the sensory processing stage which is then followed by recruitment of additional network components for modality specific memory recollection. Therefore, we propose TMCN as a viable computational tool for extracting network timing in various cognitive tasks.