Dynamic monitoring of blood pressure (BP) is beneficial to obtain comprehensive cardiovascular information of patients throughout the day. However, the clinical BP measurement method relies on wearing a bulky cuff, which limits the long-term monitoring and control of BP. In this work, a microcavity assisted graphene pressure sensor (MAGPS) for single-vessel local BP monitoring is designed to replace the cuff. The microcavity structure increases the working range of the sensor by gas pressure buffering. Therefore, the MAGPS achieves a wide linear response of 0-1,050 kPa and sensitivity of 15.4 kPa-1. The large working range and the microcavity structure enable the sensor to fully meet the requirements of BP detection at the radial artery. A database of 228 BP data (60-second data fragment detected by MAGPS) and 11,804 pulse waves from 9 healthy subjects and 5 hypertensive subjects is built. Finally, the BP was detected and analyzed automatically by combining MAGPS and a two-stage convolutional neural network algorithm. For the BP detection method at local radial artery, the first stage algorithm first determines whether the subject has hypertension by the pulse wave. Then, the second stage algorithm can diagnose systolic and diastolic BP with the accuracy of 93.5% and 97.8% within a 10 mmHg error, respectively. This work demonstrates a new BP detection method based on single vessel, which greatly promotes the efficiency of BP detection.
- Article type
- Year
- Co-author
![](/assets/img/article/article-lock.png)
Since 2021, the concept of the metaverse has gained significant popularity and attention, not only among the general public but also among researchers who are interested in novel technologies and human-machine interfaces. Sensors, a critical component of human-machine interaction, have seen rapid advancements in recent years, particularly graphene-based sensors. These sensors offer a number of benefits, including flexibility, lightweight, ease of integration, and outstanding electrical properties. Over the past decade, our research team has focused on developing advanced graphene sensors for use in human-machine interaction and wearable healthcare. In this review, we showcase our team’s efforts by presenting the design, manufacturing process, and performance of various graphene-based sensors, focusing on their suitability for diverse human-machine interaction needs across the human body. Additionally, we discuss potential future directions for the development of graphene-based sensors in human-machine interaction and share our insights.
![](/assets/img/article/article-lock.png)
The discovery of ferroelectricity in hafnium oxide (HfO2) based thin films in 2011 renewed the interest in ferroelectrics. These new ferroelectrics possess completely different crystal morphology with conventional perovskite ferroelectrics, and present more robust ferroelectric properties upon aggressive scaling and compatibility with standard integrated circuit fabrication processes. In this article, we give a brief introduction to the conventional ferroelectric memories, then review the basic properties, recent progress, and memory applications of these HfO2-based ferroelectrics.
This study proposes a feasible and scalable production strategy to naturally obtain aligned platinum diselenide (PtSe2) nanoribbon arrays with anisotropic conductivity. The anisotropic properties of two-dimensional (2D) materials, especially transition-metal dichalcogenides (TMDs), have attracted great interest in research. The dependence of physical properties on their lattice orientations is of particular interest because of its potential in diverse applications, such as nanoelectronics and optoelectronics. One-dimensional (1D) nanostructures facilitate many feasible production strategies for shaping 2D materials into unidirectional 1D nanostructures, providing methods to investigate the anisotropic properties of 2D materials based on their lattice orientations and dimensionality. The natural alignment of zigzag (ZZ) PtSe2 nanoribbons is experimentally demonstrated using angle-resolved polarized Raman spectroscopy (ARPRS), and the selective growth mechanism is further theoretically revealed by comparing edges and edge energies of different orientations using the density functional theory (DFT). Back-gate field-effect transistors (FETs) are also constructed of unidirectional PtSe2 nanoribbons to investigate their anisotropic electrical properties, which align with the results of the projected density of states (DOS) calculations. This work provides new insight into the anisotropic properties of 2D materials and a feasible investigation strategy from experimental and theoretical perspectives.
![](/assets/img/article/article-lock.png)
Three main ambipolar compact models for Two-Dimensional (2D) materials based Field-Effect Transistors (2D-FETs) are reviewed: (1) Landauer model, (2) 2D Pao-Sah model, and (3) virtual Source Emission-Diffusion (VSED) model. For the Landauer model, the Gauss quadrature method is applied, and it summarizes all kinds of variants, exhibiting its state-of-art. For the 2D Pao-Sah model, the aspects of its theoretical fundamentals are rederived, and the electrostatic potentials of electrons and holes are clarified. A brief development history is compiled for the VSED model. In summary, the Landauer model is naturally appropriate for the ballistic transport of short channels, and the 2D Pao-Sah model is applicable to long-channel devices. By contrast, the VSED model offers a smooth transition between ultimate cases. These three models cover a fairly completed channel length range, which enables researchers to choose the appropriate compact model for their works.
![](/assets/img/article/article-lock.png)
Short-Term Memory (STM) is a primary capability of the human brain. Humans use STM to remember a small amount of information, like someone’s phone number, for a short period of time. Usually the duration of STM is less than 1 minute. Synapses, the connections between neurons, are of vital importance to memory in biological brains. For mimicking the memory function of synapses, Carbon Nanotube (CNT) networks based thin-film transistors with Electric Double Layers (EDL) at the dielectric/channel interface were researched in this work. A response characteristic of pre-synaptic potential pulses on the gate electrode of this CNT synaptic transistor was shown remarkably similar to Excitatory Post-Synaptic Current (EPSC) of biological synapses. Also a multi-level modulatable STM of CNT synaptic transistors was investigated. Post-synaptic current was shown with tunable peak values, on-off ratio, and relaxation time.
![](/assets/img/article/article-lock.png)
Graphene has been widely used in gas-sensing applications due to its large specific surface area and strong adsorption ability. Among different forms of graphene used as gas-sensing materials, reduced graphene oxide is one of the most convenient and economical materials to integrate with Si-based electronics, which is very important to graphene-based gas sensors. In addition, the stacking structure of graphene oxide flakes facilitates absorption and detection of gas molecules. Based on reduced graphene oxide, a highly sensitive and portable gas-sensing system was demonstrated here. Solution-based graphene oxide was cast on a chip like a TF memory card and then reduced thermally. A signal acquisition system was designed to monitor resistance variation as a sign of gas concentration. This miniature graphene-based gas sensor array demonstrates a new path for the use of graphene in gas-detection technologies. And the creation of a sensitive and portable graphene gas sensor also shows great potential in fields such as medicine and environmental science.
![](/assets/img/article/article-lock.png)
Graphene-based materials have attracted much attention in recent years. Many researchers have demonstrated prototypes using graphene-based materials, but few specific applications have appeared. Graphene-based acoustic devices have become a popular topic. This paper describes a novel method to fabricate graphene-based earphones by laser scribing. The earphones have been used in wireless communication systems. A wireless communication system was built based on an ARM board. Voice from a mobile phone was transmitted to a graphene-based earphone. The output sound had a similar wave envelope to that of the input; some differences were introduced by the DC bias added to the driving circuit of the graphene-based earphone. The graphene-based earphone was demonstrated to have a great potential in wireless communication.
![](/assets/img/article/article-lock.png)
We report a novel self-powered nanocomposite sensor composed of K
![](/assets/img/article/article-lock.png)
This study presents a tiny pressure sensor which is used to measure the Intracranial Pressure (ICP). The sensor is based on the piezoresistive effect. The piezoresistive pressure sensor is simulated and designed by using nonlinear programming optimizing and Finite Element Analysis (FEA) tools. Two kinds of sensor sizes are designed in the case of childhood and adult. The sensors are fabricated by Microelectro Mechanical Systems (MEMS) process. The test results yield sensitivities of 1.033