Carbyne as a promising material for E-nose applications with machine learning.

There has been a lot of study and advancement in the area of carbon allotropes in the last several decades, driven by the exceptional and diverse physical and chemical characteristics of carbon nanomaterials. For example, nanostructured forms such as carbon nanotubes (CNTs), graphene, and carbon quantum dots have the potential to revolutionize various industries (Roston 2010; In and Noy 2014; Peng20147 1-29). The global scientific community continues to research in the field of creating new materials, particularly low-dimensional carbon allotropes such as CNTs and carbyne.

Electrical Characteristics of CMOS-Compatible SiO-Based Resistive-Switching Devices.

The electrical characteristics and resistive switching properties of memristive devices have been studied in a wide temperature range. The insulator and electrode materials of these devices (silicon oxide and titanium nitride, respectively) are fully compatible with conventional complementary metal-oxide-semiconductor (CMOS) fabrication processes. Silicon oxide is also obtained through the low-temperature chemical vapor deposition method.

Toward Reflective Spiking Neural Networks Exploiting Memristive Devices.

The design of modern convolutional artificial neural networks (ANNs) composed of formal neurons copies the architecture of the visual cortex. Signals proceed through a hierarchy, where receptive fields become increasingly more complex and coding sparse. Nowadays, ANNs outperform humans in controlled pattern recognition tasks yet remain far behind in cognition.

Neurohybrid Memristive CMOS-Integrated Systems for Biosensors and Neuroprosthetics.

Here we provide a perspective concept of neurohybrid memristive chip based on the combination of living neural networks cultivated in microfluidic/microelectrode system, metal-oxide memristive devices or arrays integrated with mixed-signal CMOS layer to control the analog memristive circuits, process the decoded information, and arrange a feedback stimulation of biological culture as parts of a bidirectional neurointerface. Our main focus is on the state-of-the-art approaches for cultivation and spatial ordering of the network of dissociated hippocampal neuron cells, fabrication of a large-scale cross-bar array of memristive devices tailored using device engineering, resistive state programming, or non-linear dynamics, as well as hardware implementation of spiking neural networks (SNNs) based on the arrays of memristive devices and integrated CMOS electronics. The concept represents an example of a brain-on-chip system belonging to a more general class of memristive neurohybrid systems for a new-generation robotics, artificial intelligence, and personalized medicine, discussed in the framework of the proposed roadmap for the next decade period.