Chronically implanted neural implants are of clinical importance. However, currently used electrodes have several drawbacks. Some weeks after implantation in the brain, a glial scar forms around the electrode, causing decreased electrode functionality. Nanostructures, and in particular nanowires, are good candidates to overcome these drawbacks and reduce glial scar formation. Using a mechanically compliant substrate with protruding nanowires could further decrease the glial scar formation by reducing the mechanical mismatch between the tissue and the electrode. However, flexible substrates require strengthening upon brain implantation. One solution consists of embedding the implant in a gelatin-based matrix, which is resorbable. In the case where nanostructures are present at the surface of the implant, it is crucial that the embedding matrix also preserves the nanostructures, which can be challenging considering the forces involved during the drying phase of gelatin. Here, the authors show that freestanding gallium phosphide nanowires coated with hafnium oxide (HfO2), titanium (Ti), and gold (Au) were preserved in a gelatin-glycerol embedding matrix with subsequent implantation in 1% agar, which is a model for brain implantation.

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Assessing the human affective state using electroencephalography (EEG) have shown good potential but failed to demonstrate reliable performance in real-life applications. Especially if one applies a setup that might impact affective processing and relies on generalized models of affect. Additionally, using subjective assessment of ones affect as ground truth has often been disputed. To shed the light on the former challenge we explored the use of a convenient EEG system with 20 participants to capture their reaction to affective movie clips in a naturalistic setting. Employing state-of-the-art machine learning approach demonstrated that the highest performance is reached when combining linear features, namely symmetry features and single-channel features, with nonlinear ones derived by a multiscale entropy approach. Nevertheless, the best performance, reflected in the highest F1-score achieved in a binary classification task for valence was 0.71 and for arousal 0.62. The performance was 10–20% better compared to using ratings provided by 13 independent raters. We argue that affective self-assessment might be underrated and it is crucial to account for personal differences in both perception and physiological response to affective cues.