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Register a new userTransfer Printing Approach to All-Carbon Nanoelectronics
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Transfer printing methods are used to pattern and assemble monolithic carbon nanotube (CNT) thin-film transistors on large-area transparent, flexible substrates. Airbrushed CNT thin-films with sheet resistance 1kOhmsquare^{-1} at 80% transparency were used as electrodes, and high quality chemical vapor deposition (CVD)-grown CNT networks were used as the semiconductor component. Transfer printing was used to pre-pattern and assemble thin film transistors on polyethylene terephthalate (PET) substrates which incorporated Al_{2}O_{3}/poly-methylmethacrylate (PMMA) dielectric bi-layer. CNT-based ambipolar devices exhibit field-effect mobility in range 1 - 33 cm^{2}/Vs and on/off ratio ~10^{3}, comparable to the control devices fabricated using Au as the electrode material.
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The thermal deposition and transfer Printing method had been used to produce pentacene thin-films on SiO2/Si and plastic substrates (PMMA and PVP), respectively. X-ray diffraction patterns of pentacene thin films showed reflections associated with highly ordered polycrystalline films and a coexistence of two polymorph phases classified by their d-spacing, d(001): 14.4 and 15.4 A.The dependence of the c-axis correlation length and the phase fraction on the film thickness and printing temperature were measured. A transition from the 15.4 A phase towards 14.4 A phase was also observed with increasing film thickness. An increase in the c-axis correlation length of approximately 12% ~16% was observed for Pn films transfer printed onto a PMMA coated PET substrate at 100~120 C as compared to as-grown Pn films on SiO2/Si substrates. The transfer printing method is shown to be an attractive for the fabrication of pentacene thin-film transistors on flexible substrates partly because of the resulting improvement in the quality of the pentacene film.
Using the first-principles spin density functional approach, we have studied magnetism of a new type of all-carbon nanomaterials, i.e., the carbon nanowires inserted into the single-walled carbon nanotubes. It is found that if the 1D carbon nanowire density is not too higher, the ferromagnetic ground state will be more stable than the antiferromagnetic one, which is caused by weak coupling between the 1D carbon nanowire and the single-walled carbon nanotube. Also, both dimerization of the carbon nanowire and carbon vacancy on the tube-wall are found to enhance the magnetic moment of the composite.
To induce intrinsic magnetism in the nominally nonmagnetic carbon materials containing only $s$ and $p$ electrons is an intriguing yet challenging task. Here, based on first-principles electronic structure calculations, we propose a universal approach inspired by Ovchinnikovs rule to guide us the design of a series of imaginative magnetic all-carbon structures. The idea is to combine the differently stacked graphene layers via the acetylenic linkages (-C$equiv$C-) to obtain a class of two-dimensional (2D) and three-dimensional (3D) carbon networks. With first-principles electronic structure calculations, we confirm the effectiveness of this approach via concrete examples of double-layer ALBG-C14, triple-layer ALTG-C22, and bulk IALG-C30. We show that these materials are antiferromagnetic (AFM) semiconductors with intralayer Neel and interlayer AFM couplings. According to the above idea, our work not only provides a promising design scheme for magnetic all-carbon materials, but also can apply to other $pi$-bonding network systems.
Availability of affordable and widely applicable interatomic potentials is the key needed to unlock the riches of modern materials modelling. Artificial neural network based approaches for generating potentials are promising; however neural network training requires large amounts of data, sampled adequately from an often unknown potential energy surface. Here we propose a self-consistent approach that is based on crystal structure prediction formalism and is guided by unsupervised data analysis, to construct an accurate, inexpensive and transferable artificial neural network potential. Using this approach, we construct an interatomic potential for Carbon and demonstrate its ability to reproduce first principles results on elastic and vibrational properties for diamond, graphite and graphene, as well as energy ordering and structural properties of a wide range of crystalline and amorphous phases.
Vibrational energy transfer from photo-excited single-wall carbon nanotubes (SWCNTs) to coupled proteins is a key to engineer thermally induced biological reactions such as photothermal therapy. Here, we explored vibrational energy transfer from the photo-excited SWCNTs to different adsorbed biological materials by means of a femtosecond pump-probe technique. We show that the vibrational relaxation time of the radial breathing modes (RBMs) in SWCNTs significantly depends on the structure of coupled materials, i.e. proteins or biopolymers, indicating the vibrational energy transfer is governed by overlap of phonon density of states between the SWCNTs and coupled materials.
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