Density functional theory

Density functional theory (DFT) finds increasing use in applications related to biological systems. Advancements in methodology and implementations have reached a point where predicted properties of reasonable to high quality can be obtained. Thus, DFT studies can complement experimental investigations, or even venture with some confidence into experimentally unexplored territory. In the present contribution, we provide an overview of the properties that can be calculated with DFT, such as geometries, energies, reaction mechanisms, and spectroscopic properties. A wide range of spectroscopic parameters is nowadays accessible with DFT, including quantities related to infrared and optical spectra, X-ray absorption and Mössbauer, as well as all of the magnetic properties connected with electron paramagnetic resonance spectroscopy except relaxation times. We highlight each of these fields of application with selected examples from the recent literature and comment on the capabilities and limitations of current methods

Learning course adjustments during arm movements with reversed sensitivity derivatives

<p>Abstract</p> <p>Background</p> <p>To learn, a motor system needs to know its sensitivity derivatives, which quantify how its neural commands affect motor error. But are these derivatives themselves learned, or are they known solely innately? Here we test a recent theory that the brain's estimates of sensitivity derivatives are revisable based on sensory feedback. In its simplest form, the theory says that each control system has a single, adjustable estimate of its sensitivity derivatives which affects all aspects of its task, e.g. if you learn to reach to mirror-reversed targets then your revised estimate should reverse not only your initial aiming but also your online course adjustments when the target jumps in mid-movement.</p> <p>Methods</p> <p>Human subjects bent a joystick to move a cursor to a target on a computer screen, but the cursor's motion was reversed relative to the joystick's. The target jumped once during each movement. Subjects had up to 4000 trials to practice aiming and responding to target jumps.</p> <p>Results</p> <p>All subjects learned to reverse both initial aiming and course adjustments.</p> <p>Conclusions</p> <p>Our study confirms that sensitivity derivatives can be relearned. It is consistent with the idea of a single, all-purpose estimate of those derivatives; and it suggests that the estimate is a function of context, as one would expect given that the true sensitivity derivatives may vary with the state of the controlled system, the target, and the motor commands.</p

Organic Semiconductors

One of the most exciting opportunities in electronics, optoelectronics or flexible electronics is to be able to make devices based on organic semiconductors. Organic active materials can exhibit many advantages such as lower demands on processing technology with less sensitivity to the processing environment, flexibility, and the opportunity to apply the simplicity of organic synthesis to tailoring the properties of the materials for specific applications [1]. Depending on their vapor pressure and solubility, organic semiconductors are deposited either from a vapor or solution phase. In this section, some of the organic semiconductor deposition methods are discussed. Similar to its inorganic counterparts, organic semiconductors have been the subject of extensive research to produce organic electronic devices such as organic photovoltaic cells (OPV), organic field-effect transistors (OFET), and organic lightemitting diodes (OLED) [2, 3, 73–77, 82]. However, organic semiconductors have certain limitations such as a short lifetime, degradation byUVlight, temperature sensitivity, low efficiency compared to inorganic semiconductors, and not well understood charge transfer mechanisms. Despite these limitations, advantages like their lightweight, transparency, flexibility, and lower production cost make them candidates for the development of novel electronic devices fomenting research in this area. It is worthwhile to note that organic semiconductors have been combined with other carbon nanomaterials like carbon nanotubes, fullerenes, and graphene, to improve their charge carrier mobility, which is one of the limitations of polymers and oligomers