Liquid Metal Application for Continuously Tunable Frequency Reconfigurable Antenna

This paper presents two different designs for frequency reconfigurable antennas capable of continuous tuning. The radiator, for both antenna designs, is a microstrip patch, formed from liquid metal, contained within a microfluidic channel structure. Both patch designs are aperture fed. The microfluidic channel structures are made from polydimethylsiloxane (PDMS). The microfluidic channel structure for the first design has a meander layout and incorporates rows of posts. The simulated antenna provides a frequency tuning range of approximately 118% (i.e. 4.36 GHz) over the frequency range from 1.51 GHz to 5.87 GHz. An experimental result for the fully filled case shows a resonance at 1.49 GHz (1.3% error compared with the simulation). Experienced rheological behavior of the liquid metal necessitates microfluidic channel modifications. For that reason, we modified the channel structure used to realise the radiating patch for the second design. Straight channels are implemented in the second microfluidic device. According to simulation the design yields a frequency tuning range of about 77% (i.e. 3.28 GHz) from 2.62 GHz to 5.90 GHz

Liquid Metal Bandwidth-Reconfigurable Antenna

This letter shows how slugs of liquid metal can be used to connect/disconnect large areas of metalization and achieve a radiation performance not possible by using conventional switches. The proposed antenna can switch its operating bandwidth between ultrawideband and narrowband by connecting/disconnecting the ground plane for the feedline from that of the radiator. This could be achieved by using conventional semiconductor switches. However, such switches provide point-like contacts. Consequently, there are gaps in electrical contact between the switches. Surface currents, flowing around these gaps, lead to significant back radiation. In this letter, the slugs of a liquid metal are used to completely fill the gaps. This significantly reduces the back radiation, increases the bore-sight gain, and produces a pattern identical to that of a conventional microstrip patch antenna. Specifically, the realized gain and total efficiency are increased by 2 dBi and 24%, respectively. The antenna has potential applications in wireless systems employing cognitive radio (CR) and spectrum aggregation

High-Gain On-Chip Antenna Design on Silicon Layer with Aperture Excitation for Terahertz Applications

This letter investigates the feasibility of designing a high gain on-chip antenna on silicon technology for subterahertz applications over a wide-frequency range. High gain is achieved by exciting the antenna using an aperture fed mechanism to couple electromagnetics energy froma metal slot line, which is sandwiched between the silicon and polycarbonate substrates, to a 15-element array comprising circular and rectangular radiation patches fabricated on the top surface of the polycarbonate layer. An open ended microstrip line, which is orthogonal to the metal slot-line, is implemented on the underside of the silicon substrate. When the open ended microstrip line is excited it couples the signal to the metal slot-line which is subsequently coupled and radiated by the patch array. Measured results show the proposed on-chip antenna exhibits a reflection coefficient of less than -10 dB across 0.290-0.316 THz with a highest gain and radiation efficiency of 11.71 dBi and 70.8%, respectively, occurred at 0.3 THz. The antenna has a narrow stopband between 0.292 and 0.294 THz. The physical size of the presented subterahertz on-chip antenna is 20 x 3.5 x 0.126 mm(3)

Disordered protein-graphene oxide co-assembly and supramolecular biofabrication of functional fluidic devices

Supramolecular chemistry offers an exciting opportunity to assemble materials with molecular precision. However, there remains an unmet need to turn molecular self-assembly into functional materials and devices. Harnessing the inherent properties of both disordered proteins and graphene oxide (GO), we report a disordered protein-GO co-assembling system that through a diffusion-reaction process and disorder-to-order transitions generates hierarchically organized materials that exhibit high stability and access to non-equilibrium on demand. We use experimental approaches and molecular dynamics simulations to describe the underlying molecular mechanism of formation and establish key rules for its design and regulation. Through rapid prototyping techniques, we demonstrate the system's capacity to be controlled with spatio-temporal precision into well-defined capillary-like fluidic microstructures with a high level of biocompatibility and, importantly, the capacity to withstand flow. Our study presents an innovative approach to transform rational supramolecular design into functional engineering with potential widespread use in microfluidic systems and organ-on-a-chip platforms

Financing micro-entrepreneurs for poverty alleviation: a performance analysis of microfinance services offered by BRAC, ASA, and Proshika from Bangladesh

Microfinance services have emerged as an effective tool for financing microentrepreneurs to alleviate poverty. Since the 1970s, development theorists have considered non-governmental microfinance institutions (MFIs) as the leading practitioners of sustainable development through financing micro-entrepreneurial activities. This study evaluates the impact of micro-finance services provided by MFIs on poverty alleviation. In this vein, we examine whether microfinance services contribute to poverty alleviation, and also identify bottlenecks in micro-finance programs and operations. The results indicate that the micro-loans have a statistically significant positive impact on the poverty alleviation index and consequently improve the living standard of borrowers by increasing their level of income

Machine Learning Based Approach for Diffraction Loss Variation Prediction by the Human Body

This paper presents a machine learning (ML) based model to predict the diffraction loss around the human body. Practically, it is not reasonable to measure the diffraction loss changes for all possible body rotation angles, builds and line of sight (LoS) elevation angles. A diffraction loss variation prediction model based on a non-parametric learning technique called Gaussian process (GP) is introduced. Analysed results state that 86% correlation and normalised mean square error (NMSE) of 0.3 on the test data is achieved using only 40% of measured data. This allows a 60% reduction in required measurements in order to achieve a well-fitted ML loss prediction model. It also confirms the model generalizability for non-measured rotation angles