Stable Operation of a 300-m Laser Interferometer with Sufficient Sensitivity to Detect Gravitational-Wave Events within our Galaxy

TAMA300, an interferometric gravitational-wave detector with 300-m baseline length, has been developed and operated with sufficient sensitivity to detect gravitational-wave events within our galaxy and sufficient stability for observations; the interferometer was operated for over 10 hours stably and continuously. With a strain-equivalent noise level of $h\sim 5 \times 10^{-21} /\sqrt{\rm Hz}$, a signal-to-noise ratio (SNR) of 30 is expected for gravitational waves generated by a coalescence of 1.4 $M_\odot$-1.4 $M_\odot$ binary neutron stars at 10 kpc distance. %In addition, almost all noise sources which limit the sensitivity and which %disturb the stable operation have been identified. We evaluated the stability of the detector sensitivity with a 2-week data-taking run, collecting 160 hours of data to be analyzed in the search for gravitational waves.Comment: 5 pages, 4 figure

The History and Horizons of Microscale Neural Interfaces

Microscale neural technologies interface with the nervous system to record and stimulate brain tissue with high spatial and temporal resolution. These devices are being developed to understand the mechanisms that govern brain function, plasticity and cognitive learning, treat neurological diseases, or monitor and restore functions over the lifetime of the patient. Despite decades of use in basic research over days to months, and the growing prevalence of neuromodulation therapies, in many cases the lack of knowledge regarding the fundamental mechanisms driving activation has dramatically limited our ability to interpret data or fine-tune design parameters to improve long-term performance. While advances in materials, microfabrication techniques, packaging, and understanding of the nervous system has enabled tremendous innovation in the field of neural engineering, many challenges and opportunities remain at the frontiers of the neural interface in terms of both neurobiology and engineering. In this short-communication, we explore critical needs in the neural engineering field to overcome these challenges. Disentangling the complexities involved in the chronic neural interface problem requires simultaneous proficiency in multiple scientific and engineering disciplines. The critical component of advancing neural interface knowledge is to prepare the next wave of investigators who have simultaneous multi-disciplinary proficiencies with a diverse set of perspectives necessary to solve the chronic neural interface challenge

In Vivo Electrochemical Analysis of a PEDOT/MWCNT Neural Electrode Coating

Neural electrodes hold tremendous potential for improving understanding of brain function and restoring lost neurological functions. Multi-walled carbon nanotube (MWCNT) and dexamethasone (Dex)-doped poly(3,4-ethylenedioxythiophene) (PEDOT) coatings have shown promise to improve chronic neural electrode performance. Here, we employ electrochemical techniques to characterize the coating in vivo. Coated and uncoated electrode arrays were implanted into rat visual cortex and subjected to daily cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) for 11 days. Coated electrodes experienced a significant decrease in 1 kHz impedance within the first two days of implantation followed by an increase between days 4 and 7. Equivalent circuit analysis showed that the impedance increase is the result of surface capacitance reduction, likely due to protein and cellular processes encapsulating the porous coating. Coating’s charge storage capacity remained consistently higher than uncoated electrodes, demonstrating its in vivo electrochemical stability. To decouple the PEDOT/MWCNT material property changes from the tissue response, in vitro characterization was conducted by soaking the coated electrodes in PBS for 11 days. Some coated electrodes exhibited steady impedance while others exhibiting large increases associated with large decreases in charge storage capacity suggesting delamination in PBS. This was not observed in vivo, as scanning electron microscopy of explants verified the integrity of the coating with no sign of delamination or cracking. Despite the impedance increase, coated electrodes successfully recorded neural activity throughout the implantation period

Mechanical failure modes of chronically implanted planar silicon-based neural probes for laminar recording

Penetrating intracortical electrode arrays that record brain activity longitudinally are powerful tools for basic neuroscience research and emerging clinical applications. However, regardless of the technology used, signals recorded by these electrodes degrade over time. The failure mechanisms of these electrodes are understood to be a complex combination of the biological reactive tissue response and material failure of the device over time. While mechanical mismatch between the brain tissue and implanted neural electrodes have been studied as a source of chronic inflammation and performance degradation, the electrode failure caused by mechanical mismatch between different material properties and different structural components within a device have remained poorly characterized. Using Finite Element Model (FEM) we simulate the mechanical strain on a planar silicon electrode. The results presented here demonstrate that mechanical mismatch between iridium and silicon leads to concentrated strain along the border of the two materials. This strain is further focused on small protrusions such as the electrical traces in planar silicon electrodes. These findings are confirmed with chronic in vivo data (133–189 days) in mice by correlating a combination of single-unit electrophysiology, evoked multi-unit recordings, electrochemical impedance spectroscopy, and scanning electron microscopy from traces and electrode sites with our modeling data. Several modes of mechanical failure of chronically implanted planar silicon electrodes are found that result in degradation and/or loss of recording. These findings highlight the importance of strains and material properties of various subcomponents within an electrode array