Predictive Model for the Electrical Transport within Nanowire Networks

Thin networks of high aspect ratio conductive nanowires can combine high electrical conductivity with excellent optical transparency, which has led to a widespread use of nanowires in transparent electrodes, transistors, sensors, and flexible and stretchable conductors. Although the material and application aspects of conductive nanowire films have been thoroughly explored, there is still no model which can relate fundamental physical quantities, like wire resistance, contact resistance, and nanowire density, to the sheet resistance of the film. Here, we derive an analytical model for the electrical conduction within nanowire networks based on an analysis of the parallel resistor network. The model captures the transport characteristics and fits a wide range of experimental data, allowing for the determination of physical parameters and performance-limiting factors, in sharp contrast to the commonly employed percolation theory. The model thus constitutes a useful tool with predictive power for the evaluation and optimization of nanowire networks in various applications.Funding Agencies|ETH Zurich; Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linkoping University (Faculty Grant SFO Mat LiU) [2009 00971]; Swedish Foundation for Strategic Research</p

Predictive Model for the Electrical Transport within Nanowire Networks

Thin networks of high aspect ratio conductive nanowires can combine high electrical conductivity with excellent optical transparency, which has led to a widespread use of nanowires in transparent electrodes, transistors, sensors, and flexible and stretchable conductors. Although the material and application aspects of conductive nanowire films have been thoroughly explored, there is still no model which can relate fundamental physical quantities, like wire resistance, contact resistance, and nanowire density, to the sheet resistance of the film. Here, we derive an analytical model for the electrical conduction within nanowire networks based on an analysis of the parallel resistor network. The model captures the transport characteristics and fits a wide range of experimental data, allowing for the determination of physical parameters and performance-limiting factors, in sharp contrast to the commonly employed percolation theory. The model thus constitutes a useful tool with predictive power for the evaluation and optimization of nanowire networks in various applications

“Brains on a chip”: Towards engineered neural networks

The fundamental mechanisms of complex neural computation remain largely unknown, especially in respect to the characteristics of distinct neural circuits within the mammalian brain. The bottom-up approach of building well-defined neural networks with controlled topology has immense promise for improved reproducibility and increased target selectivity and response of drug action, along with hopes to unravel the relationships between functional connectivity and its imprinted physiological and pathological functions. In this review, we summarize the different approaches available for engineering neural networks treated analogously to a mathematical graph consisting of cell bodies and axons as nodes and edges, respectively. After discussing the advances and limitations of the current techniques in terms of cell placement to the nodes and guiding the growth of axons to connect them, the basic properties of patterned networks are analyzed in respect to cell survival and activity dynamics, and compared to that of in vivo and random in vitro cultures. Besides the fundamental scientific interest and relevance to drug and toxicology tests, we also visualize the possible applications of such engineered networks. The review concludes by comparing the possibilities and limitations of the different methods for realizing in vitro engineered neural networks in 2D

Simple and Inexpensive Paper-Based Astrocyte Co-culture to Improve Survival of Low-Density Neuronal Networks

Bottom-up neuroscience aims to engineer well-defined networks of neurons to investigate the functions of the brain. By reducing the complexity of the brain to achievable target questions, such in vitro bioassays better control experimental variables and can serve as a versatile tool for fundamental and pharmacological research. Astrocytes are a cell type critical to neuronal function, and the addition of astrocytes to neuron cultures can improve the quality of in vitro assays. Here, we present cellulose as an astrocyte culture substrate. Astrocytes cultured on the cellulose fiber matrix thrived and formed a dense 3D network. We devised a novel co-culture platform by suspending the easy-to-handle astrocytic paper cultures above neuronal networks of low densities typically needed for bottom-up neuroscience. There was significant improvement in neuronal viability after 5 days in vitro at densities ranging from 50,000 cells/cm2 down to isolated cells at 1,000 cells/cm2. Cultures exhibited spontaneous spiking even at the very low densities, with a significantly greater spike frequency per cell compared to control mono-cultures. Applying the co-culture platform to an engineered network of neurons on a patterned substrate resulted in significantly improved viability and almost doubled the density of live cells. Lastly, the shape of the cellulose substrate can easily be customized to a wide range of culture vessels, making the platform versatile for different applications that will further enable research in bottom-up neuroscience and drug development

Modular microstructure design to build neuronal networks of defined functional connectivity

Theoretical and in vivo neuroscience research suggests that functional information transfer within neuronal networks is influenced by circuit architecture. Due to the dynamic complexities of the brain, it remains a challenge to test the correlation between structure and function of a defined network. Engineering controlled neuronal networks in vitro offers a way to test structural motifs; however, no method has achieved small, multi-node networks with stable, unidirectional connections. Here, we screened ten different microchannel architectures within polydimethylsiloxane (PDMS) devices to test their potential for axonal guidance. The most successful design had a 92% probability of achieving strictly unidirectional connections between nodes. Networks built from this design were cultured on multielectrode arrays and recorded on days in vitro 9, 12, 15 and 18 to investigate spontaneous and evoked bursting activity. Transfer entropy between subsequent nodes showed up to 100 times more directional flow of information compared to the control. Additionally, directed networks produced a greater amount of information flow, reinforcing the importance of directional connections in the brain being critical for reliable communication. By controlling the parameters of network formation, we minimized response variability and achieved functional, directional networks. The technique provides us with a tool to probe the spatio-temporal effects of different network motifs

Controlled single-cell deposition and patterning by highly flexible hollow cantilevers

Single-cell patterning represents a key approach to decouple and better understand the role and mechanisms of individual cells of a given population. In particular, the bottom-up approach of engineering neuronal circuits with a controlled topology holds immense promises to perceive the relationships between connectivity and function. In order to accommodate these efforts, highly flexible SU-8 cantilevers with integrated microchannels have been fabricated for both additive and subtractive patterning. By directly squeezing out single cells onto adhesive surfaces, controlled deposition with a spatial accuracy of 5 μm could be achieved, while subtractive patterning has been realized by selective removal of targeted single cells. Complex cell patterns were created on substrates pre-patterned with cell-adhesive and repulsive areas, preserving the original pattern geometry for long-term studies. For example, a circular loop with a diameter of 530 μm has been realized using primary hippocampal neurons, which were fully connected to their respective neighbors along the loop. Using the same cantilevers, the versatility of the technique has also been demonstrated via in situ modification of already mature neuronal cultures by both detaching individual cells of the population and adding fresh ones, incorporating them into the culture

Easy to Apply Polyoxazoline-Based Coating for Precise and Long-Term Control of Neural Patterns

Arranging cultured cells in patterns via surface modification is a tool used by biologists to answer questions in a specific and controlled manner. In the past decade, bottom-up neuroscience emerged as a new application, which aims to get a better understanding of the brain via reverse engineering and analyzing elementary circuitry in vitro. Building well-defined neural networks is the ultimate goal. Antifouling coatings are often used to control neurite outgrowth. Because erroneous connectivity alters the entire topology and functionality of minicircuits, the requirements are demanding. Current state-of-the-art coating solutions such as widely used poly­(l-lysine)-<i>g</i>-poly­(ethylene glycol) (PLL-<i>g</i>-PEG) fail to prevent primary neurons from making undesired connections in long-term cultures. In this study, a new copolymer with greatly enhanced antifouling properties is developed, characterized, and evaluated for its reliability, stability, and versatility. To this end, the following components are grafted to a poly­(acrylamide) (PAcrAm) backbone: hexaneamine, to support spontaneous electrostatic adsorption in buffered aqueous solutions, and propyldimethylethoxysilane, to increase the durability via covalent bonding to hydroxylated culture surfaces and antifouling polymer poly­(2-methyl-2-oxazoline) (PMOXA). In an assay for neural connectivity control, the new copolymer’s ability to effectively prevent unwanted neurite outgrowth is compared to the gold standard, PLL-<i>g</i>-PEG. Additionally, its versatility is evaluated on polystyrene, glass, and poly­(dimethylsiloxane) using primary hippocampal and cortical rat neurons as well as C2C12 myoblasts, and human fibroblasts. PAcrAm-<i>g</i>-(PMOXA, NH₂, Si) consistently outperforms PLL-<i>g</i>-PEG with all tested culture surfaces and cell types, and it is the first surface coating which reliably prevents arranged nodes of primary neurons from forming undesired connections over the long term. Whereas the presented work focuses on the proof of concept for the new antifouling coating to successfully and sustainably prevent unwanted connectivity, it is an important milestone for in vitro neuroscience, enabling follow-up studies to engineer neurologically relevant networks. Furthermore, because PAcrAm-<i>g</i>-(PMOXA, NH₂, Si) can be quickly applied and used with various surfaces and cell types, it is an attractive extension to the toolbox for in vitro biology and biomedical engineering