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Theoretical Insights into the Mechanism of Selective Nitrate-to-Ammonia Electroreduction on Single-Atom Catalysts
No full textSelective nitrate-to-ammonia electrochemical conversion is an efficient pathway to solve the pollution of nitrate and an attractive strategy for low-temperature ammonia synthesis. However, current studies for nitrate electroreduction (NO3RR) mainly focus on metal-based catalysts, which remains challenging because of the poor understanding of the catalytic mechanism. Herein, taking single transition metal atom supported on graphitic carbon nitrides (g-CN) as an example, the NO3RR feasibility of single-atom catalysts (SACs) is first demonstrated by using density functional theory calculations. The results reveal that highly efficient NO3RR toward NH3 can be achieved on Ti/g-CN and Zr/g-CN with low limiting potentials of −0.39 and −0.41 V, respectively. Furthermore, the considerable energy barriers are observed during the formation of byproducts NO2, NO, N2O, and N2 on Ti/g-CN and Zr/g-CN, guaranteeing their high selectivity. This work provides a new route for the application of SACs and paves the way to the development of NO3RR
Tunable Stochastic Oscillator Based on Hybrid VO/TaO Device for Compressed Sensing
No full textCompressed sensing with a tunable random sampling strategy has great potential in reducing the energy/time consumption during the constant acquisition of external information, thereby it is considered one of the most promising sensing strategies in edge-terminal. In this letter, a novel tunable stochastic oscillator (TSO) based on the VO2/TaOx structure was proposed and employed as the core controlling device for compressed sensing. The TSO is jointly governed by the metal-insulator-transition (IMT) based threshold switching of the VO2 layer and the stochastic resistive switching by the TaOx layer. The tunable memristor/resistor in series can modulate the oscillation frequency of the TSO. The orthogonal matching pursuit (OMP) algorithm is implemented to reconstruct the compressed sparse signals, demonstrating a good performance of TSO in compressed sensing
Boundary layer vortex sheet evolution around an accelerating and rotating cylinder
No full textThe evolution of the boundary layer vortex sheet on a rotating and translating accelerating circular cylinder at Reynolds numbers of 10 000 and 20 000 is investigated using planar particle image velocimetry. The vortex sheet is decomposed into contributions resulting from translation and rotation as well as from local and far-field vorticity. Their individual development is explored to understand the overall time history of the boundary layer as well as its evolution at the unsteady separation point. The boundary layer vortex sheet distribution changes considerably throughout the motion as well as between different kinematic cases. The same is observed for the vortex sheet strength at the unsteady separation point. A non-dimensional parameter is proposed which removes the effect of rotation rate, instantaneous velocity and shed vorticity accumulating in the far field. It was found that this was successful at collapsing the vortex sheet strength at the unsteady separation point during cylinder motion as well as for the individual kinematic test cases investigated. This confirms that cylinder kinematics and far-field vorticity are driving factors contributing to the development of the unsteady boundary layer and its strength at the separation point
Guided assembly of cancer ellipsoid on suspended hydrogel microfibers estimates multi-cellular traction force
No full textThree-dimensional (3D) multi-cellular aggregates hold important applications in tissue engineering and in vitro biological modeling. Probing the intrinsic forces generated during the aggregation process, could open up new possibilities in advancing the discovery of tissue mechanics-based biomarkers. We use individually suspended, and tethered gelatin hydrogel microfibers to guide multicellular aggregation of brain cancer cells (glioblastoma cell line, U87), forming characteristic cancer 'ellipsoids'. Over a culture period of up to 13 days, U87 aggregates evolve from a flexible cell string with cell coverage following the relaxed and curly fiber contour; to a distinct ellipsoid-on-string morphology, where the fiber segment connecting the ellipsoid poles become taut. Fluorescence imaging revealed the fiber segment embedded within the ellipsoidal aggregate to exhibit a morphological transition analogous to filament buckling under a compressive force. By treating the multicellular aggregate as an effective elastic medium where the microfiber is embedded, we applied a filament post-buckling theory to model the fiber morphology, deducing the apparent elasticity of the cancer ellipsoid medium, as well as the collective traction force inherent in the aggregation process
Magnum: A Distributed Framework for Enabling Transfer Learning in B5G-Enabled Industrial IoT
No full textIn this article, we propose a lightweight blockchain-inspired framework - Magnum - as a magazine of transfer learning models in blocks. We propose the storage of these blocks on proximal fog nodes to simplify access to pretrained base models by industrial plants to tune them before deployment. We design Magnum for B5G-enabled scenarios to reduce the block transfer time. We formulate a demand-centric distribution scheme to further reduce the search and access time by adopting a nonlinear program model and solving it using the branch-and-bound method. Through extensive experiments and comparison with state-of-the-art solutions, we show that Magnum retains the accuracy of the models and present its feasibility with a maximum CPU and memory usage of 80% and 6%, respectively. Additionally, while Magnum requires a maximum of 10 s for writing models as large as 17 Mb on the blocks, it requires 16 ms for fetching the same
Graphene for Biosensing Applications in Point-of-Care Testing
No full textGraphene and graphene-related materials (GRMs) exhibit a unique combination of electronic, optical, and electrochemical properties, which make them ideally suitable for ultrasensitive and selective point-of-care testing (POCT) devices. POCT device-based applications in diagnostics require test results to be readily accessible anywhere to produce results within a short analysis timeframe. This review article provides a summary of methods and latest developments in the field of graphene and GRM-based biosensing in POCT and an overview of the main applications of the latter in nucleic acids and enzymatic biosensing, cell detection, and immunosensing. For each application, we discuss scientific and technological advances along with the remaining challenges, outlining future directions for widespread use of this technology in biomedical applications
Shear design method for non-prismatic concrete beams reinforced using W-FRP
No full textFlexible fabric formworks and bespoke Wound Fibre-Reinforced Polymer (W-FRP) cages have been proposed to address the difficulties of constructing geometrically optimised concrete structures with minimised material consumptions. However, the existing codes of practice for FRP reinforced concrete structures are not able to provide accurate shear behaviour predictions, resulting in potential premature anchorage failure. This paper presents a revised shear design approach for non-prismatic beams reinforced with W-FRP based on the Modified Compression Field Theory (MCFT), which considers the non-prismatic geometries, geometrical nonlinearity and internal load redistributions. The revised shear design approach is calibrated against previous fabric-formed T beam tests and parametric analyses are conducted to minimise the material usage of the previous T-beams. The validity of the shear design approach is further confirmed with a new T-beam test. The new test shows that the T beam designed with the MCFT based approach failed in the expected flexural failure mode with an ultimate capacity of 245 kN applied load. The predictions can effectively model the bar force development of the inclined flexural reinforcement with a Normalised Root Mean Square Error (NRSME) of 4.4%. The proposed shear design method improved the prediction accuracy of flexural bar force at ultimate capacity by 40% compared to the codified shear design method. By addressing the shear contributions of inclined flexural reinforcement and required anchorage strength, further applications of this novel flexibly formed beams with optimised geometries are expected to achieve material consumption reduction and hence lower carbon emissions from concrete structures
3D Printable Sensorized Soft Gelatin Hydrogel for Multi-Material Soft Structures
No full textThe ability to 3D print soft materials with integrated strain sensors enables significant flexibility for the design and fabrication of soft robots. Hydrogels provide an interesting alternative to traditional soft robot materials, allowing for more varied fabrication techniques. In this work, we investigate the 3D printing of a gelatin-glycerol hydrogel, where transglutaminase is used to catalyse the crosslinking of the hydrogel such that its material properties can be controlled for 3D printing. By including electron-conductive elements (aqueous carbon black) in the hydrogel we can create highly flexible and linear soft strain sensors. We present a first investigation into adapting a desktop 3D printer and optimizing its control parameters to fabricate sensorized 2D and 3D structures which can undergo >300% strain and show a response to strain which is highly linear and synchronous. To demonstrate the capabilities of this material and fabrication approach, we produce some example 2D and 3D structures and show their sensing capabilities
Performant implementation of the atomic cluster expansion (PACE) and application to copper and silicon
No full textThe atomic cluster expansion is a general polynomial expansion of the atomic energy in multi-atom basis functions. Here we implement the atomic cluster expansion in the performant C++ code PACE that is suitable for use in large-scale atomistic simulations. We briefly review the atomic cluster expansion and give detailed expressions for energies and forces as well as efficient algorithms for their evaluation. We demonstrate that the atomic cluster expansion as implemented in PACE shifts a previously established Pareto front for machine learning interatomic potentials toward faster and more accurate calculations. Moreover, general purpose parameterizations are presented for copper and silicon and evaluated in detail. We show that the Cu and Si potentials significantly improve on the best available potentials for highly accurate large-scale atomistic simulations
