Direct costing

Call number: LD2668 .R4 1965 Y9

Biodegradable Luminescent Silicon Quantum Dots for Two Photon Imaging Applications

Cadmium- and lead-based quantum dots are normally coated for biological applications, because their degradation may result in the release of toxic heavy metal ions. Here, we synthesize silicon quantum dots that are expected to biodegrade to non-toxic products. A chitosan coating is used to render the silicon quantum dots stable in storage conditions and biodegradable at physiological conditions. The applications of these particles are demonstrated in cellular imaging with single and two-photon excitation. These results open the door for a new generation of silicon quantum dots that may have a wide variety of applications derived from the flexibility of chitosan

Temperature Dependence Of Brillouin Light Scattering Spectra Of Acoustic Phonons In Silicon

Electrons, optical phonons, and acoustic phonons are often driven out of local equilibrium in electronic devices or during laser-material interaction processes. The need for a better understanding of such non-equilibrium transport processes has motivated the development of Raman spectroscopy as a local temperature sensor of optical phonons and intermediate frequency acoustic phonons, whereas Brillouin light scattering (BLS) has recently been explored as a temperature sensor of low-frequency acoustic phonons. Here, we report the measured BLS spectra of silicon at different temperatures. The origins of the observed temperature dependence of the BLS peak position, linewidth, and intensity are examined in order to evaluate their potential use as temperature sensors for acoustic phonons. (C) 2015 AIP Publishing LLC.National Science Foundation (NSF) Thermal Transport Processes Program CBET-1336968PhysicsCenter for Complex Quantum SystemsMaterials Science and EngineeringTexas Materials InstituteMechanical Engineerin

Score-based Conditional Generation with Fewer Labeled Data by Self-calibrating Classifier Guidance

Score-based Generative Models (SGMs) are a popular family of deep generative models that achieves leading image generation quality. Earlier studies have extended SGMs to tackle class-conditional generation by coupling an unconditional SGM with the guidance of a trained classifier. Nevertheless, such classifier-guided SGMs do not always achieve accurate conditional generation, especially when trained with fewer labeled data. We argue that the issue is rooted in unreliable gradients of the classifier and the inability to fully utilize unlabeled data during training. We then propose to improve classifier-guided SGMs by letting the classifier calibrate itself. Our key idea is to use principles from energy-based models to convert the classifier as another view of the unconditional SGM. Then, existing loss for the unconditional SGM can be adopted to calibrate the classifier using both labeled and unlabeled data. Empirical results validate that the proposed approach significantly improves the conditional generation quality across different percentages of labeled data. The improved performance makes the proposed approach consistently superior to other conditional SGMs when using fewer labeled data. The results confirm the potential of the proposed approach for generative modeling with limited labeled data

Optimization of Superplastic Forming Process of AA7075 Alloy for the Best Wall Thickness Distribution

This work aims to optimize the process parameters for improving the wall thickness distribution of the sheet superplastic forming process of AA7075 alloy. The considered factors include forming pressure p (MPa), deformation temperature T (°C), and forming time t (minutes), while the responses are the thinning degree of the wall thickness ε (%) and the relative height of the product h*. First, a series of experiments are conducted in conjunction with response surface method (RSM) to render the relationship between inputs and outputs. Subsequently, an analysis of variance (ANOVA) is conducted to verify the response significance and parameter effects. Finally, a numerical optimization algorithm is used to determine the best forming conditions. The results indicate that the thinning degree of 13.121% is achieved at the forming pressure of 0.7 MPa, the deformation temperature of 500°C, and the forming time of 31 minutes