Superconducting Phases in Lithium Decorated Graphene LiC6.

A study of possible superconducting phases of graphene has been constructed in detail. A realistic tight binding model, fit to ab initio calculations, accounts for the Li-decoration of graphene with broken lattice symmetry, and includes s and d symmetry Bloch character that influences the gap symmetries that can arise. The resulting seven hybridized Li-C orbitals that support nine possible bond pairing amplitudes. The gap equation is solved for all possible gap symmetries. One band is weakly dispersive near the Fermi energy along Γ → M where its Bloch wave function has linear combination of [Formula: see text] and dxy character, and is responsible for [Formula: see text] and dxy pairing with lowest pairing energy in our model. These symmetries almost preserve properties from a two band model of pristine graphene. Another part of this band, along K → Γ, is nearly degenerate with upper s band that favors extended s wave pairing which is not found in two band model. Upon electron doping to a critical chemical potential μ1 = 0.22 eV the pairing potential decreases, then increases until a second critical value μ2 = 1.3 eV at which a phase transition to a distorted s-wave occurs. The distortion of d- or s-wave phases are a consequence of decoration which is not appear in two band pristine model. In the pristine graphene these phases convert to usual d-wave or extended s-wave pairing

Development of RF-MEMS Based Passive Wireless Respiratory Monitoring Systems

This dissertation explores the application of high frequency, low loss, high quality factor thin-film piezoelectric MEMS resonators as passive wireless respiratory monitoring sensors. In this work we build on advances in RF MEMS technology and wearables to develop a respiratory monitoring system capable of measuring respiration without any on-board circuitry and battery First, a wireless MEMS-based respiration sensor that operates without a battery or any on-board circuitry is presented. The sensing system is made up two major components, the wireless sensor and a base transceiver and computational unit. The sensor is only made of up of two RF-MEMS resonators and a custom UHF RFID antenna. The base unit is composed of transmitter antenna connected to a signal generator, and a receiver antenna connected to an oscilloscope. The frequency of the MEMS resonator is highly sensitive towards both temperature and also water vapor condensation. This sensitivity results in the modulation of the resonator (and sensor) when exposed to respiratory airflow. For a mean excitation signal power of 80µW, the sensor was measured to be capable of recording the respiratory profile of human subject from distance of up to 2 meters away from the base transceiver unit. At a distance of 0.5 meters the sensor was measured to have a SNR of 124.8dB. Next, building on the above sensor and using a novel time-of-flight sensing technique a respiratory flow sensor with only 7.2cm2 footprint was developed. To facilitate the time-of-flight sensor, two low loss, high quality factor TPoS MEMS resonators are placed ~1cm apart and are connected to a small (3.8cm2 ) planar ground antenna. We were able to measure flow rate and respiration profile of human subject from a distance of 20cm from the base transceiver

Wearable Passive Wireless MEMS Respiration Sensor

In this study a passive sensor that wirelessly monitors the profile of the human respiratory system is presented. The sensor was designed to be wearable, weighs less than 10 grams and is durable. The sensor is made of a RF piezoelectric MEMS resonator and an ultra-high frequency antenna made of a thin metal film formed on a flexible substrate . The resonance frequency of the TPoS resonator shifts as a function of condensation and evaporation of water vapor on the surface of the resonator and changes in resonator\u27s temperature. These parameters change in each in response to inspiration and expiration and a wireless measurement system detects the frequency shift of the sensor and converts it into the respiration profile. The respiration profile of a healthy human subject is measured and presented for a transmitter to sensor to receiver distance of ~25cm

Developments in Transduction, Connectivity and AI/Machine Learning for Point-of-Care Testing

We review some emerging trends in transduction, connectivity and data analytics for Point-of-Care Testing (POCT) of infectious and non-communicable diseases. The patient need for POCT is described along with developments in portable diagnostics, specifically in respect of Lab-on-chip and microfluidic systems. We describe some novel electrochemical and photonic systems and the use of mobile phones in terms of hardware components and device connectivity for POCT. Developments in data analytics that are applicable for POCT are described with an overview of data structures and recent AI/Machine learning trends. The most important methodologies of machine learning, including deep learning methods, are summarised. The potential value of trends within POCT systems for clinical diagnostics within Lower Middle Income Countries (LMICs) and the Least Developed Countries (LDCs) are highlighted

Mems-Based Passive Wireless Respiration Profile Sensor

In this paper we present a sensor that wirelessly records the breathing profile of the human respiratory system. The sensor works passively and does not contain a power source. Furthermore, it is lightweight, robust and flexible making it ideal as a wearable monitoring device. The sensor is made of a ∼902MHz thin film piezoelectric-on-substrate (TPoS) MEMS resonator and an ultra-high frequency (UHF) antenna made of a thin metal film formed on a flexible substrate. The resonance frequency of the TPoS resonator shifts in response to the inhaled/exhaled air flow and a wireless detection technique is utilized to sense the frequency shift and to translate it into the respiration profile. The Respiration profile of a subject is measured and presented for a sensor-to-transceiver distance of ∼25cm

Superconducting Phases in Lithium Decorated Graphene LiC6.

A study of possible superconducting phases of graphene has been constructed in detail. A realistic tight binding model, fit to ab initio calculations, accounts for the Li-decoration of graphene with broken lattice symmetry, and includes s and d symmetry Bloch character that influences the gap symmetries that can arise. The resulting seven hybridized Li-C orbitals that support nine possible bond pairing amplitudes. The gap equation is solved for all possible gap symmetries. One band is weakly dispersive near the Fermi energy along Γ → M where its Bloch wave function has linear combination of [Formula: see text] and dxy character, and is responsible for [Formula: see text] and dxy pairing with lowest pairing energy in our model. These symmetries almost preserve properties from a two band model of pristine graphene. Another part of this band, along K → Γ, is nearly degenerate with upper s band that favors extended s wave pairing which is not found in two band model. Upon electron doping to a critical chemical potential μ1 = 0.22 eV the pairing potential decreases, then increases until a second critical value μ2 = 1.3 eV at which a phase transition to a distorted s-wave occurs. The distortion of d- or s-wave phases are a consequence of decoration which is not appear in two band pristine model. In the pristine graphene these phases convert to usual d-wave or extended s-wave pairing

Superconducting Phases In Lithium Decorated Graphene Lic6

A study of possible superconducting phases of graphene has been constructed in detail. A realistic tight binding model, fit to ab initio calculations, accounts for the Li-decoration of graphene with broken lattice symmetry, and includes s and d symmetry Bloch character that influences the gap symmetries that can arise. The resulting seven hybridized Li-C orbitals that support nine possible bond pairing amplitudes. The gap equation is solved for all possible gap symmetries. One band is weakly dispersive near the Fermi energy along Γ → M where its Bloch wave function has linear combination of dx2−y2 and dxy character, and is responsible for dx2−y2 and dxy pairing with lowest pairing energy in our model. These symmetries almost preserve properties from a two band model of pristine graphene. Another part of this band, along K → Γ, is nearly degenerate with upper s band that favors extended s wave pairing which is not found in two band model. Upon electron doping to a critical chemical potential μ1 = 0.22 eV the pairing potential decreases, then increases until a second critical value μ2 = 1.3 eV at which a phase transition to a distorted s-wave occurs. The distortion of d- or s-wave phases are a consequence of decoration which is not appear in two band pristine model. In the pristine graphene these phases convert to usual d-wave or extended s-wave pairing