Photocurrent in a visible-light graphene photodiode

We calculate the photocurrent in a clean graphene sample normally irradiated by a monochromatic electromagnetic field and subject to a step-like electrostatic potential. We consider the photon energies $\hbar\Omega$ that significantly exceed the height of the potential barrier, as is the case in the recent experiments with graphene-based photodetectors. The photocurrent comes from the resonant absorption of photons by electrons and decreases with increasing ratio $\hbar\Omega/U_0$. It is weakly affected by the background gate voltage and depends on the light polarization as $\propto\sin^2\gamma$, $\gamma$ being the angle between the potential and the polarization plane.Comment: 5 pages, 3 figure

Micro heat exchanger by using MEMS impinging jets

A micro impinging-jet heat exchanger is presented here. Heat transfer is studied for single jet, slot arrays and jet arrays. In order to facilitate micro heat transfer measurements with these devices, a MEMS sensor chip, which has an 8 x 8 temperature-sensor array on one side, and an integrated heater on the other side has been designed and fabricated. This sensor chip allows 2-D surface temperature measurement with various jets impinging on it. It is found that micro impinging jets can be highly efficient when compared to existing macro impinging-jet microelectronics packages such as IBM 4381. For example, using a single nozzle jet (500-μm diameter driven by 5 psig pressure), the sensor chip (2 x 2 cm^2) temperature can be cooled down from 70 to 33°C. The cooling becomes more efficient when nozzle arrays (4x5 over 1 cm^2 area) are used under the same driving pressure. Interestingly, although higher driving pressure gives better cooling (lower surface temperature), the cooling efficiency, defined as h/0.5pv^2, is actually higher for lower driving pressure

A suspended microchannel with integrated temperature sensors for high-pressure flow studies

A freestanding microchannel, with integrated temperature sensors, has been developed for high-pressure flow studies. These microchannels are approximately 20μm x 2μm x 4400μm, and are suspended above 80 μm deep cavities, bulk micromachined using BrF3 dry etch. The calibration of the lightly boron-doped thermistor-type sensors shows that the resistance sensitivity of these integrated sensors is parabolic with respect to temperature and linear with respect to pressure. Volumetric flow rates of N2 in the microchannel were measured at inlet pressures up to 578 psig. The discrepancy between the data and theory results from the flow acceleration in a channel, the non-parabolic velocity profile, and the bulging of the channel. Bulging effects were evaluated by using incompressible water flow measurements, which also measures 1.045x10^-3N-s/m^2 for the viscosity of DI water. The temperature data from sensors on the channel shows the heating of the channel due to the friction generated by the high-pressure flow inside

Radial excitations of Q-balls, and their D-term

We study the structure of the energy-momentum tensor of radial excitations of Q-balls in scalar field theories with U(1) symmetry. The obtained numerical results for the $1\le N \le 23$ excitations allow us to study in detail patterns how the solutions behave with N. We show that although the fields and energy-momentum tensor densities exhibit a remarkable degree of complexity, the properties of the solutions scale with N with great regularity. This is to best of our knowledge the first study of the D-term d1 for excited states, and we demonstrate that it is negative --- in agreement with results from literature on the d1 of ground state particles.Comment: 11 pages, 12 figure

A basic helix-loop-helix transcription factor, PhFBH4, regulates flower senescence by modulating ethylene biosynthesis pathway in petunia.

The basic helix-loop-helix (bHLH) transcription factors (TFs) play important roles in regulating multiple biological processes in plants. However, there are few reports about the function of bHLHs in flower senescence. In this study, a bHLH TF, PhFBH4, was found to be dramatically upregulated during flower senescence. Transcription of PhFBH4 is induced by plant hormones and abiotic stress treatments. Silencing of PhFBH4 using virus-induced gene silencing or an antisense approach extended flower longevity, while transgenic petunia flowers with an overexpression construct showed a reduction in flower lifespan. Abundance of transcripts of senescence-related genes (SAG12, SAG29) was significantly changed in petunia PhFBH4 transgenic flowers. Furthermore, silencing or overexpression of PhFBH4 reduced or increased, respectively, transcript abundances of important ethylene biosynthesis-related genes, ACS1 and ACO1, thereby influencing ethylene production. An electrophoretic mobility shift assay showed that the PhFBH4 protein physically interacted with the G-box cis-element in the promoter of ACS1, suggesting that ACS1 was a direct target of the PhFBH4 protein. In addition, ectopic expression of this gene altered plant development including plant height, internode length, and size of leaves and flowers, accompanied by alteration of transcript abundance of the gibberellin biosynthesis-related gene GA2OX3. Our results indicate that PhFBH4 plays an important role in regulating plant growth and development through modulating the ethylene biosynthesis pathway

Outcome prediction in mathematical models of immune response to infection

Clinicians need to predict patient outcomes with high accuracy as early as possible after disease inception. In this manuscript, we show that patient-to-patient variability sets a fundamental limit on outcome prediction accuracy for a general class of mathematical models for the immune response to infection. However, accuracy can be increased at the expense of delayed prognosis. We investigate several systems of ordinary differential equations (ODEs) that model the host immune response to a pathogen load. Advantages of systems of ODEs for investigating the immune response to infection include the ability to collect data on large numbers of `virtual patients', each with a given set of model parameters, and obtain many time points during the course of the infection. We implement patient-to-patient variability $v$ in the ODE models by randomly selecting the model parameters from Gaussian distributions with variance $v$ that are centered on physiological values. We use logistic regression with one-versus-all classification to predict the discrete steady-state outcomes of the system. We find that the prediction algorithm achieves near $100\%$ accuracy for $v=0$, and the accuracy decreases with increasing $v$ for all ODE models studied. The fact that multiple steady-state outcomes can be obtained for a given initial condition, i.e. the basins of attraction overlap in the space of initial conditions, limits the prediction accuracy for $v>0$. Increasing the elapsed time of the variables used to train and test the classifier, increases the prediction accuracy, while adding explicit external noise to the ODE models decreases the prediction accuracy. Our results quantify the competition between early prognosis and high prediction accuracy that is frequently encountered by clinicians.Comment: 14 pages, 7 figure