Carbon-based smart nanomaterials in biomedicine and neuroengineering

• Antonina M. Monaco and
• Michele Giugliano

Beilstein J. Nanotechnol. 2014, 5, 1849–1863, doi:10.3762/bjnano.5.196

• the spontaneous extracellular electrical activity in a murine neuronal cell line, which yielded results in good agreement with recordings made by means of conventional MEAs (Figure 5). Single-spin NV-NDs embedded in an artificial lipid bilayer [136] and in a real cell membrane, in which there is a

Model systems for studying cell adhesion and biomimetic actin networks

• Dorothea Brüggemann,
• Johannes P. Frohnmayer and
• Joachim P. Spatz

Beilstein J. Nanotechnol. 2014, 5, 1193–1202, doi:10.3762/bjnano.5.131

• actin networks and talin into model cells. Keywords: actin network; cell adhesion; giant unilamellar vesicle; integrin; lipid bilayer; synthetic cell; protein reconstitution; talin; Review Introduction Since Hooke first described a biological cell in 1665 tremendous progress has been made in

• found to quantify the binding energy of different integrin–ligand pairs under bioanalogue conditions [43]. This system was developed further to facilitate the mobility of the integrin receptors within the supported lipid bilayer. Thus, it was shown that integrin mobility controls the force-induced

• lipid bilayer, like the membrane of natural cells. With these attributes GUVs have gained increasing importance as bottom-up model systems in synthetic biology over the past years. GUVs can be used to study cellular functions and the interplay between various proteins, which are incorporated in the

Large-scale analysis of high-speed atomic force microscopy data sets using adaptive image processing

• Blake W. Erickson,
• Séverine Coquoz,
• Jonathan D. Adams,
• Daniel J. Burns and
• Georg E. Fantner

Beilstein J. Nanotechnol. 2012, 3, 747–758, doi:10.3762/bjnano.3.84

• routine on an example lipid bilayer of mixed composition are shown as the inputs and outputs of each major block. Each block will be discussed separately in detail. 1.1 Identify the background, generate a mask, estimate the polynomial background The purpose of this section is to identify the background

• in the image. Panel E shows the raw data of a mixed lipid bilayer on mica. Panel F shows the results of line-by-line second-order polynomial subtraction. Panel G shows 1-D artifact correction followed by 2-D second-order polynomial subtraction. Panel H shows the results of 1-D artifact correction

• fitting, are 2-D polynomial fits. 2-D polynomial fits can be performed by using either the whole figure for the fit, or only certain regions of the image determined by using thresholds. Figure 2 shows a comparison of different processing methods on a standard calibration grating as well as on a lipid

Microfluidic anodization of aluminum films for the fabrication of nanoporous lipid bilayer support structures

• Jaydeep Bhattacharya,
• Alexandre Kisner,
• Andreas Offenhäusser and
• Bernhard Wolfrum

Beilstein J. Nanotechnol. 2011, 2, 104–109, doi:10.3762/bjnano.2.12

• monitored by impedance spectroscopy across the nanoporous alumina membrane in real-time. Our approach offers a simple and efficient methodology to investigate the activity of transmembrane proteins or ion diffusion across membrane bilayers. Keywords: anodization; lipid bilayer; microfluidics

• nanoporous membrane for lipid bilayer formation, the nanoporous alumina surface was first subjected to silanization. The silanization was carried out in the solution phase according to the method described by Steinle and coworkers [37][38] with slight modifications. Briefly, a 10% (v/v) solution of (3

• whole system was then cured at 60 °C for 60 min. The lipid bilayer on the modified nanoporous alumina surface was prepared by the method of liposomal fusion [39]. The liposomes were prepared from 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC, Avanti Polar Lipids, U.S.A.) by the following method