Semiconductor Nanowire Based Piezoelectric Energy Harvesters: Modeling, Fabrication, and Characterization

Semiconductor nanowire (NW) arrays’ unique advantages over bulk forms, including enhanced surface area, high mechanical flexibility, high sensitivity to small forces, better charge collection, and enhanced light absorption through trapping, make them ideal templates on which to build other structures. This research on the piezoelectric behavior of NWs used in high-performance energy harvesters is based on device modeling, fabrication, and characterization. These activities optimize the electrical properties of a NW device in response to a compression/release force applied to the NWs. The dissertation first discusses the piezoelectric and semiconductor properties of wurtzite compound nanomaterials, emphasizing III-nitride semiconducting InN and GaN NWs. Static analysis identifies the role of carrier density, temperature, force, length/diameter ratio, and Schottky barrier height. Piezoelectric nanogenerators (NGs) based on vertically aligned InN nanowires (NWs) are fabricated, characterized, and evaluated. In these NGs, arrays of exclusively either p-type or intrinsic InN NWs prepared by plasma-assisted molecular beam epitaxy (MBE) demonstrate similar average piezoelectric properties. The p-type NGs show 160% more output current and 70% more output power product than the intrinsic NGs. The features driving performance enhancement are reduced electrostatic losses due to a higher NW areal density and longer NWs, and improved electromechanical energy conversion efficiency due to smaller NW diameters. These findings highlight the potential of InN based NGs as a power source for self-powered systems and the importance of NW morphology in overall NG performance. The second part is devoted to demonstrate a series of flexible transparent ZnO p-n homojunction nanowire (NW)-based piezoelectric nanogenerators (NGs) with different p-doping concentrations. The lithium-doped segments are grown directly and consecutively on top of intrinsic nanowires (n-type). When characterized under cyclic compressive strains, the overall NG performance is enhanced by up to eleven-fold if the doping concentration is properly controlled. This improvement is attributable to reduction in the mobile charge screening effect and optimization of the NGs’ internal electrical characteristics. Experimental results also show that an interfacial MoO3 barrier layer, at an optimized thickness of 5-10 nm, reduces leakage current and substantially improves piezoelectric NG performance. The third part presents the first cascade-type compact hybrid energy cell (CHEC) that is capable of simultaneously or individually harvesting solar and strain energies. It is made of an n-p junction NW-based piezoelectric nanogenerator to harvest strain energy and an nc/a-Si:H single junction cell to harvest solar energy. The CHECs ability to harvest energy effectively simultaneously, and complementary is demonstrated by deploying six CHECs to power LEDs and a wireless strain gauge sensor node. Under ~10 mW/cm2 illumination and vibrations of 3 m/s2 at 3 Hz frequency, the output current and voltage from a single 1.0 cm2 CHEC are 280 μA and 3.0 V, respectively; enough to drive many low power commercial electronics. This dissertation aims to deepen understanding of the piezoelectric behavior of semiconductor NWs on hard and flexible substrates. Thus, this research in the field of nanopiezoelectrics could have a substantial impact on many areas, ranging from the fundamental study of new nanomaterial properties and mechanical effects in nanostructures to diverse applications like aerospace

Microbial production of hyaluronic acid: current state, challenges, and perspectives

Hyaluronic acid (HA) is a natural and linear polymer composed of repeating disaccharide units of β-1, 3-N-acetyl glucosamine and β-1, 4-glucuronic acid with a molecular weight up to 6 million Daltons. With excellent viscoelasticity, high moisture retention capacity, and high biocompatibility, HA finds a wide-range of applications in medicine, cosmetics, and nutraceuticals

Heterologous expression, biochemical characterization, and overproduction of alkaline α-amylase from Bacillus alcalophilus in Bacillus subtilis

<p>Abstract</p> <p>Background</p> <p>Alkaline α-amylases have potential applications for hydrolyzing starch under high pH conditions in the starch and textile industries and as ingredients in detergents for automatic dishwashers and laundries. While the alkaline α-amylase gains increased industrial interest, the yield of alkaline α-amylases from wild-type microbes is low, and the combination of genetic engineering and process optimization is necessary to achieve the overproduction of alkaline α-amylase.</p> <p>Results</p> <p>The alkaline α-amylase gene from <it>Bacillus alcalophilus </it>JN21 (CCTCC NO. M 2011229) was cloned and expressed in <it>Bacillus subtilis </it>strain WB600 with vector pMA5. The recombinant alkaline α-amylase was stable at pH from 7.0 to 11.0 and temperature below 40°C. The optimum pH and temperature of alkaline α-amylase was 9.0 and 50°C, respectively. Using soluble starch as the substrate, the <it>K</it>_mand <it>V</it>_maxof alkaline α-amylase were 9.64 g/L and 0.80 g/(L·min), respectively. The effects of medium compositions (starch, peptone, and soybean meal) and temperature on the recombinant production of alkaline α-amylase in <it>B. subtilis </it>were investigated. Under the optimal conditions (starch concentration 0.6% (w/v), peptone concentration 1.45% (w/v), soybean meal concentration 1.3% (w/v), and temperature 37°C), the highest yield of alkaline α-amylase reached 415 U/mL. The yield of alkaline α-amylase in a 3-L fermentor reached 441 U/mL, which was 79 times that of native alkaline α-amylase from <it>B. alcalophilus </it>JN21.</p> <p>Conclusions</p> <p>This is the first report concerning the heterologous expression of alkaline α-amylase in <it>B. subtilis</it>, and the obtained results make it feasible to achieve the industrial production of alkaline α-amylase with the recombinant <it>B. subtilis</it>.</p

Similarity Principle and its Acoustical Verification

This study finds a similarity principle the waves emanated from the same source are similar to each other as long as two wave receivers are close enough to each other the closer to each other the wave receivers are the more similar to each other the received waves are We define the similarity mathematically and verify the similarity principle by acoustical experiment

Improvement of magnetite adsorption performance for Pb (II) by introducing defects

Surface defect engineering is an efficient strategy to enhance the adsorption properties of materials. After calcination in argon, the adsorption capacity of natural magnetite to Pb (II) is significantly improved. The Rietveld refinement, Mössbauer spectrum, and XPS were used to prove the existence of oxygen and cation vacancies in the crystal structure of magnetite after calcination, and it is found that the vacancy content is linearly related to the adsorption amount of Pb (II). This indicates that the increase in the adsorption performance of magnetite after calcination is determined by the vacancy. The adsorption capacity increases from 8 to 26 mg/g when the calcination temperature reaches 700°C. The equilibrium adsorption process of Pb (II) on magnetite can be well fitted to the Langmuir model, and the kinetic adsorption followed a pseudo-second-order mechanism. The improvement of the adsorption performance of magnetite is mainly due to the change in its structure, which depends on the oxidation degree and surface effect of magnetite in the calcination process. This work also provides a theoretical basis for the broad application of magnetite as environmental material

Dr2^2Net: Dynamic Reversible Dual-Residual Networks for Memory-Efficient Finetuning

Large pretrained models are increasingly crucial in modern computer vision tasks. These models are typically used in downstream tasks by end-to-end finetuning, which is highly memory-intensive for tasks with high-resolution data, e.g., video understanding, small object detection, and point cloud analysis. In this paper, we propose Dynamic Reversible Dual-Residual Networks, or Dr$^2$Net, a novel family of network architectures that acts as a surrogate network to finetune a pretrained model with substantially reduced memory consumption. Dr$^2$Net contains two types of residual connections, one maintaining the residual structure in the pretrained models, and the other making the network reversible. Due to its reversibility, intermediate activations, which can be reconstructed from output, are cleared from memory during training. We use two coefficients on either type of residual connections respectively, and introduce a dynamic training strategy that seamlessly transitions the pretrained model to a reversible network with much higher numerical precision. We evaluate Dr$^2$Net on various pretrained models and various tasks, and show that it can reach comparable performance to conventional finetuning but with significantly less memory usage