Coupled dynamics of steady jet flow control for flexible membrane wings

We present a steady jet flow-based flow control of flexible membrane wings for an adaptive and efficient motion of bat-inspired drones in complex flight environments. A body-fitted variational computational aeroelastic framework is adopted for the modeling of fluid-structure interactions. High-momentum jet flows are injected from the leading edge and transported to the wake flows to alter the aerodynamic performance and the membrane vibration. The phase diagrams of the coupled fluid-membrane dynamics are constructed in the parameter space of the angle of attack and the jet momentum coefficient. The coupled dynamical effect of active jet flow control on the membrane performance is systematically explored. While the results indicate that the current active flow control strategy performs well at low angles of attack, the effectiveness degrades at high angles of attack with large flow separation. To understand the coupling mechanism, the variations of the vortex patterns at different jet momentum coefficients are examined by the proper orthogonal decomposition modes in the Eulerian view and the fluid transport process is studied by the coherent flow structures in the Lagrange description. Two scaling relations that quantitatively connect the membrane deformation with the aerodynamic loads presented in our previous work are verified even when active jet flow control is applied. A unifying feedback loop that reveals the fluid-membrane coupling mechanism is proposed. This feedback loop provides useful guidance for designing optimal active flow control strategies and enhancing flight capabilities. These findings can facilitate the development of next-generation bio-inspired drones that incorporate smart sensing and intelligent control

NPS: A Framework for Accurate Program Sampling Using Graph Neural Network

With the end of Moore's Law, there is a growing demand for rapid architectural innovations in modern processors, such as RISC-V custom extensions, to continue performance scaling. Program sampling is a crucial step in microprocessor design, as it selects representative simulation points for workload simulation. While SimPoint has been the de-facto approach for decades, its limited expressiveness with Basic Block Vector (BBV) requires time-consuming human tuning, often taking months, which impedes fast innovation and agile hardware development. This paper introduces Neural Program Sampling (NPS), a novel framework that learns execution embeddings using dynamic snapshots of a Graph Neural Network. NPS deploys AssemblyNet for embedding generation, leveraging an application's code structures and runtime states. AssemblyNet serves as NPS's graph model and neural architecture, capturing a program's behavior in aspects such as data computation, code path, and data flow. AssemblyNet is trained with a data prefetch task that predicts consecutive memory addresses. In the experiments, NPS outperforms SimPoint by up to 63%, reducing the average error by 38%. Additionally, NPS demonstrates strong robustness with increased accuracy, reducing the expensive accuracy tuning overhead. Furthermore, NPS shows higher accuracy and generality than the state-of-the-art GNN approach in code behavior learning, enabling the generation of high-quality execution embeddings

Investigation of the Cofiring Process of Raw or Torrefied Bamboo and Masson Pine by Using a Cone Calorimeter

Cofiring characteristics of raw or torrefied bamboo and masson pine blends with different blend ratios were investigated by cone calorimetry, and its ash performance from cofiring was also determined by a YX-HRD testing instrument, X-ray fluorescence, scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Results showed that bamboo and masson pine had the different physicochemical properties. Torrefaction improved fuel performances, resulting in a more stable cofiring process. It also decreased the heat release rate, total heat release, and total suspended particulates of fuels, especially CO2 and CO release. Masson pine ash mainly included CaO, SiO2, Fe2O3, K2O, and Al2O3. Bamboo ash was mainly composed of K2O, SiO2, MgO, and SO3. There were different melting temperatures and trends between different samples. The synergistic reaction of ash components was found during the cofiring process. The surface morphology of blend ash changed with the variation of bamboo or masson pine content

Nitrogen Self-Doped Activated Carbons Derived from Bamboo Shoots as Adsorbent for Methylene Blue Adsorption

Bamboo shoots, a promising renewable biomass, mainly consist of carbohydrates and other nitrogen-related compounds, such as proteins, amino acids and nucleotides. In this work, nitrogen self-doped activated carbons derived from bamboo shoots were prepared via a simultaneous carbonization and activation process. The adsorption properties of the prepared samples were evaluated by removing methylene blue from waste water. The factors that affect the adsorption process were examined, including initial concentration, contact time and pH of methylene blue solution. The resulting that BSNC-800-4 performed better in methylene blue removal from waste water, due to its high specific surface area (2270.9 m2 g−1), proper pore size (2.19 nm) and relatively high nitrogen content (1.06%). Its equilibrium data were well fitted to Langmuir isotherm model with a maximum monolayer adsorption capacity of 458 mg g−1 and a removal efficiency of 91.7% at methylene blue concentration of 500 mg L−1. The pseudo-second-order kinetic model could be used to accurately estimate the carbon material’s (BSNC-800-4) adsorption process. The adsorption mechanism between methylene blue solution and BSNC-800-4 was controlled by film diffusion. This study provides an alternative way to develop nitrogen self-doped activated carbons to better meet the needs of the adsorption applications

Formation of Arbitrary Patterns in Ultraviolet Cured Polymer Film via Electrohydrodynamic Patterning

Electrohydrodynamic patterning of arbitrary patterns is achieved by optimizing the critical parameters (applied voltage and spacer height). The applied voltage has a great influence on the fidelity of L-shaped line structures with different sizes. The L-shaped line structures with high fidelity are obtained by using the moderate applied voltage. The spacer height has a great influence on the fidelity of square structures with different sizes. The square structures with high fidelity are obtained by using the low height spacer. The multi-field coupling transient finite element simulation demonstrates that the lack of polymer owing to the high height spacer leads to the formation of defects

Graphene Channel Liquid Container Field Effect Transistor as pH Sensor

Graphene channel liquid container field effect transistor pH sensor with interdigital microtrench for liquid ion testing is presented. Growth morphology and pH sensing property of continuous few-layer graphene (FLG) and quasi-continuous monolayer graphene (MG) channels are compared. The experiment results show that the source-to-drain current of the graphene channel FET has a significant and fast response after adsorption of the measured molecule and ion at the room temperature; at the same time, the FLG response time is less than 4 s. The resolution of MG (0.01) on pH value is one order of magnitude higher than that of FLG (0.1). The reason is that with fewer defects, the MG is more likely to adsorb measured molecule and ion, and the molecules and ions can make the transport property change. The output sensitivities of MG are from 34.5% to 57.4% when the pH value is between 7 and 8, while sensitivity of FLG is 4.75% when the pH=7. The sensor fabrication combines traditional silicon technique and flexible electronic technology and provides an easy way to develop graphene-based electrolyte gas sensor or even biological sensors

Hierarchical rose-petal surfaces delay the early-stage bacterial biofilm growth

A variety of natural surfaces exhibit antibacterial properties; as a result significant efforts in the past decade have been dedicated towards fabrication of biomimetic surfaces that can help control biofilm growth. Examples of such surfaces include rose petals, which possess hierarchical structures like the micro-papillae measuring tens of microns and nano-folds that range in the size of 700 ±100 nm. We duplicated the natural structures on rose-petal surfaces via a simple UV-curable nanocasting technique, and tested the efficacy of these artificial surfaces in preventing biofilm growth using clinically relevant bacteria strains. The rose-petal structured surfaces exhibited hydrophobicity (contact angle~130.8º ±4.3º) and high contact angle hysteresis (~91.0° ±4.9°). Water droplets on rose-petal replicas evaporated following the constant contact line mode, indicating the likely coexistence of both Cassie and Wenzel states (Cassie-Baxter impregnating wetting state). Fluorescent microscopy and image analysis revealed the significantly lower attachment of Staphylococcus epidermidis (86.1± 6.2% less) and Pseudomonas aeruginosa (85.9 ±3.2% less) on the rose-petal structured surfaces, compared with flat surfaces over a period of 2 hours. Extensive biofilm matrix was observed in biofilms formed by both species on flat surfaces after prolonged growth (several days), but was less apparent on rose-petal biomimetic surfaces. In addition, the biomass of S. epidermidis (63.2 ±9.4% less) and P. aeruginosa (76.0 ±10.0% less) biofilms were significantly reduced on the rose-petal structured surfaces, in comparison to the flat surfaces. By comparing P. aeruginosa growth on representative unitary nano-pillars, we demonstrated that hierarchical structures are more effective in delaying biofilm growth. The mechanisms are two-fold: 1) the nano-folds across the hemispherical micro-papillae restrict initial attachment of bacterial cells and delay the direct contacts of cells via cell alignment, and 2) the hemispherical micro-papillae arrays isolate bacterial clusters and inhibit the formation of a fibrous network. The hierarchical features on rose petal surfaces may be useful for developing strategies to control biofilm formation in medical and industrial contexts

Highly Sensitive Surface Enhanced Raman Spectroscopy from Ag Nanoparticles Decorated Graphene Sheet

Surface enhanced Raman spectroscopy (SERS) is a powerful analytical technique and has been most intensively studied. In this work, electroless deposition is proposed for Ag nanoparticles (NPs) decorated on chemical vapor deposition (CVD) growth graphene sheets (GS) to create hybrid SERS substrate. From three aspects of size distribution, morphology, and coverage, Ag NPs controllable decoration on GS and SERS enhancement factors of the hybrid SERS substrate is investigated. 200–300 times enhanced SERS intensities are detected from the Ag NPs on GS hybrid as compared to pure GS. Controllable decoration is crucial for improving SERS enhancement factors βEF, because βEF from quasi cubic Ag NPs on GS is 6.53 times stronger than that from spheric one; 1.6 times βEF is detected while the Ag NPs size distribution is reduced to half, and when the coverage is doubled, βEF is nearly doubled. This controllable Ag NPs/GS hybrid is capable of serving as a high performance SERS substrate for efficient chemical and biological sensing applications