To this day, the micro-heating unit in a multiparametric capillary sensor’s setup has been controlled using laboratory power supply with constant voltage. In this method it was assumed that the micro heater’s resistance value is semi-constant. However, due to the fact that degradation effects induced by high power density dissipation in multiple, intense or prolonged heating cycles may cause it to vary, a new approach had to be found. Therefore, in this paper, a development of a power stabilization method using PID controller to compensate for micro-heater’s resistance changes during intense heating is described. Additionally, a current sensing resistor, a programmable power supply and a data acquisition system are incorporated into the setup to provide closed-loop feedback.
MEMS actuators are currently widely used in the industry. Micro-heaters, being a prime example, attracted much attention in recent years due to their good operating parameters and low cost fabrication process. This paper focuses on a design and development of a micro-heater to be used as an actuator in a multiparametric capillary sensor. The micro-heater is an evolution of a previous design and uses a 200nm-thick thin film of 80/20 NiCr alloy as a heating layer. The paper presents results of fabrication and testing of the micro-heater, including temperature distribution and resistance changes during the heating cycle. Additionally, is presented a PWM based control system providing the stability of power and temperature distribution.
Local liquid sample heating is used in multiparametric sensors of liquid type classification and in sensors of liquid flow. In such applications, the heating of the liquid is done by micro-heaters, with the liquid separated from the micro-heater. The presented paper concentrates on the physical conditions of liquid sample heating used in capillary sensors. In such devices the repeatable transfer of heat is required. The basic measurements include time of liquid to vapor phase transitions and local transfer of heat. In the work were used experimental and simulation techniques. The obtained results show that in the capillary sensor repeatable local heat transfer conditions can be easier achieved than repeatable time of liquid to vapor phase transitions. In the analyzed case, the local heating depends mostly on the capillary to micro-heater distance. The liquid to vapor transition times, beside of the liquid type, depend on the powers used for micro-heater heating and on capillary cross-section parameters, such as the inner and outer diameter values. By increasing the power to the micro-heater the transition time variability is reduced.
The local heating enables liquid classification in multiparametric capillary sensors. The dispersion of capillary and microheater parameters may determine the sensor action. Therefore, this paper focuses on the analysis of a local heating implemented in mentioned sensor. The microheater consist of 4H-SiC volume heating unit, alundum ceramic base and a glass capillary is modeled and simulated using CoventorWare™. We use finite element method (FEM) to determine thermo-mechanical parameters of the designed structure. Obtained results are then compared and verified with experimental research. The influences of a capillary to microheater distance and capillary’s thickness on the output results are examined.
This paper focuses on the design and analysis of a MEMS piezoresistive pressure sensor. The absolute pressure sensor with a 150μm wide and 3μm thick silicon membrane is modeled and simulated using CoventorWare™ softwareprofiting from a finite element method (FEM) implemented to determine specific electro-mechanical parameter values characterizing MEMS structure being designed. Optimization of piezoresistor parameters has been also performed to determine optimum dimensions of piezoresistors and their location referred to the center on the pressure sensor diaphragm. The output voltage measured on a piezoresistive Wheatstone bridge has been obtained and compared for two different resistor materials along with and linearity error analysis.