System and method for smart material monitoring

Abstract

A system for monitoring the characteristics of a material by measuring electrical properties of a material uses a material monitoring device and a cloud database that relates electrical properties of a material to characteristics of that material. The aging and fermentation processes of wine and other alcohols can be monitored. The status and decomposition of foodstuffs can be monitored. The progress of chemical reactions in a vessel can be monitored. Water quality of water from a water conduit can be monitored. These characteristics can be indicated on a product monitoring device or can be communicated to an external computing device.

1 - 20 . (canceled) 
     
     
         21 . A communications device for monitoring the characteristics of a material, the device comprising:
 an integrated circuit device that includes:
 a communications circuit for establishing a network connection to one or more devices; 
 a processor for coordinating a measurement process and for communicating measurement data; 
 a stimulus circuit that creates an electrical stimulus and provides the electrical stimulus to a sensor device; and 
 a measurement circuit that performs an electrical measurement from the sensor device to generate signal measurement data; 
 the processor configured to apply a first analytical methodology, wherein the processor configures the integrated circuit to perform a first analytical methodology of impedance spectroscopy; 
 the processor configured to apply a second analytical methodology, wherein the processor configures the integrated circuit device to perform a second analytical methodology selected from a group consisting of: potentiometry, coulometry, voltammetry, square wave voltammetry, stair-case voltammetry, cyclic voltammetry, alternating current voltammetry, amperometry, pulsed amperometry, galvanometry, polarography, current measurement, voltage measurement, impedance measurement, resistance measurement, and charge measurement; 
 the processor further configured to receive at least one signal from at least one electrode of the sensor device, the at least one electrode comprising a semiconductor-based material; 
 the processor further configured to arrange the signal measurement data into a data packet; 
 the processor further configured to communicate the signal measurement data related to the at least one signal from a first analytical methodology and the at least one signal from a second analytical methodology to a device configured to apply machine learning for determining a not directly measurable characteristic of the material based on the signal measurement data received from the integrated circuit, the machine learning applied via a machine learning model trained with library data to recognize the not directly measurable characteristic of the material, the library data relating previously measured signals relating to at least one electrical property of the material to known not directly measurable characteristics of the material; 
   the sensor device electrically connected to the integrated circuit device, the at least one semiconductor electrode to provide an electrical stimulus to a material, and to measure at least one signal responsive to the stimulus and relating to an electrical property of the material;
 the sensor device further configured to apply a first analytical methodology of impedance spectroscopy; 
   a body containing the sensor device and the integrated circuit device, the body positionable with respect to the material to position the at least one semiconductor electrode of the sensor device to interact with the material; and   a power source to power the integrated circuit.   
     
     
         22 . The communications device of  claim 21 , wherein the machine learning algorithm is of a type selected from a group consisting of: a neural network, a support vector machine, and a random forest algorithm. 
     
     
         23 . The communications device of  claim 22 , wherein the material is selected from a group consisting of: a gas, a liquid, a solid, a plasma, a fluid, and a vapor. 
     
     
         24 . The communications device of  claim 21 , wherein the device includes a wired network connection with another device. 
     
     
         25 . The communications device of  claim 24  wherein the wired connection is selected from a group consisting of: a Two Wire Interface (TWI), an I squared C (I2C) interface, an I2C-compatible interface, a Serial Peripheral Interface (SPI) interface, a Microwire interface,  1 -Wire interface, and a Single Wire Protocol (SWP) interface. 
     
     
         26 . The communications device of  claim 21 , wherein the communications device includes an antenna for establishing a wireless network connection with another device. 
     
     
         27 . The communications device of  claim 23 , wherein the power source is selected from a group consisting of: a power harvesting circuit, a battery, a solar cell, and an alternating current electrical power adapter. 
     
     
         28 . The communications device of  claim 27 , wherein the processor receives a response comprising a first signal from an incremental electrode comprising a metal, a metal alloy, a metal compound, or a chemical compound-based material while applying a first impedance spectroscopy analytical methodology and the processor further receives a second response signal from the incremental electrode, and wherein the processor applies a second analytical methodology. 
     
     
         29 . The communications device of  claim 28 , wherein the incremental electrode comprises gold, gold-plated, platinum, platinum-plated, silver, silver chloride, tin, iron, copper, or brass materials. 
     
     
         30 . The communications device of  claim 27 , wherein the processor receives a response comprising a first signal from the semiconductor electrode wherein the semiconductor electrode further consists of silicon or germanium , or graphene, or graphite, or carbon based materials while applying a first impedance spectroscopy analytical methodology, and the processor further receives a second response signal from a second analytical methodology from the semiconductor electrode. 
     
     
         31 . The communications device of  claim 27 , wherein the processor also obtains the signal measurement data from the sensor device, the signal measurement data representing one of: a temperature, a pressure, a viscosity, or a surface tension. 
     
     
         32 . The communications device of  claim 27 , wherein the processor also obtains the signal measurement data from the sensor device, the signal measurement data representing one of: a quantity and direction of force, a position, or a quantity of mass. 
     
     
         33 . The communications device of  claim 27 , wherein the processor also obtains the signal measurement data from the sensor device, the signal measurement data representing a magnetic field. 
     
     
         34 . The communications device of  claim 27 , wherein the processor also obtains the signal measurement data from the sensor device, the signal measurement data representing a property of light. 
     
     
         35 . The communications device of  claim 34 , wherein the property of light is one of: a wavelength, an intensity, or a phase. 
     
     
         36 . The communications device of  claim 27 , wherein the processor also obtains the signal measurement data from the sensor device, the signal measurement data representing a quantum mechanical property. 
     
     
         37 . Use of the communications device of  claim 21  for an application of one or more of the following: chemical monitoring, vaccine monitoring, medication monitoring, medication authentication, wine monitoring, foodstuffs monitoring, water monitoring, and the monitoring of chemicals undergoing a chemical reaction. 
     
     
         38 . A non-transitory machine-readable storage medium comprising instructions that when executed:
 cause a processor in an integrated circuit device to configure an integrated circuit device connected to a sensor device to perform an impedance spectroscopy first analytical methodology, the processor receiving a signal from an electrode constructed with semiconductor-based materials, the semiconductor electrode further configured to interact with the material, and apply a stimulus signal to the material generated by a stimulus circuit in the integrated circuit and measure a response signal using the measurement circuit in the integrated circuit to generate measurement data, the response signal from the material and related to an electrical property of the material, and wherein a body enables the semiconductor electrode to interact with the material, and wherein the material consist of a material selected from a group consisting of: a gas, a liquid, a solid, a plasma, a fluid, a vapor;   cause a processor in an integrated circuit device to apply a second incremental analytical methodology selected from a group consisting of: potentiometry, coulometry, voltammetry, square wave voltammetry, stair-case voltammetry, cyclic voltammetry, alternating current voltammetry, amperometry, pulsed amperometry, galvanometry, polarography, current measurement, voltage measurement, impedance measurement, resistance measurement, and charge measurement;   cause a processor in an integrated circuit device to arrange the measurement data into a data packet;   cause a processor in an integrated circuit device to configure a communications connection with a computing device, and further cause the computing device to receive the measurement data via the communications connection; and   cause a computing device to apply machine learning to determine a not directly measurable characteristic of a material based on at least one signal relating to electrical property of the material based on the measurement data received from the integrated circuit, the machine learning applied via a machine learning model trained with library data to recognize the not directly measurable characteristic of the material, the library data relating previously measured signals relating to at least one electrical property of the material to known not directly measurable characteristics of the material.   
     
     
         39 . The non-transitory machine-readable storage medium of  claim 38 , comprising instructions that when executed cause the processor of a computing device execute a machine learning algorithm is of a specific type selected from a group consisting of: neural network, support vector machine, random forest algorithm. 
     
     
         40 . The non-transitory machine-readable storage medium of  claim 39 , comprising instructions that when executed cause a processor to receive a signal from an incremental electrode while applying an impedance spectroscopy analytical methodology, the incremental electrode comprising a metal, metal alloy, a metal compound, or a chemical compound-based material, the incremental electrode further configured to interact with the material, and apply a stimulus to the material. 
     
     
         41 . The non-transitory machine-readable storage medium of  claim 39 , comprising instructions that when executed further cause a processor to measure additional properties from a sensor device selected from a group consisting of: a magnetic field, a light property, a quantity and direction of force, a position, a quantity of mass, a quantum mechanical property, acceleration, gyroscope measurement, a magnetometer measurement, a temperature, a pressure, a density, a surface tension, a viscosity, a resistance, an impedance, a voltage, a current, and a charge. 
     
     
         42 . The non-transitory machine-readable storage medium of  claim 41 , comprising instructions that when executed further cause a processor to further measure a light property from a sensor selected from a group consisting of: a wavelength, an intensity, and a phase. 
     
     
         43 . The non-transitory machine-readable storage medium of  claim 38 , comprising instructions that when executed cause a processor to generate an alert on an indicator device. 
     
     
         44 . The non-transitory machine-readable storage medium of  claim 43 , wherein the indicator further comprises of a display device, or an audio device. 
     
     
         45 . A communications system for monitoring a characteristic of a material, the communications system comprising:
 a communications device comprising:
 a sensor device, the sensor device comprising at least one electrode comprising a semiconductor-based material to provide an electrical stimulus to a material and to measure the at least one signal responsive to the stimulus and relating to an electrical property of the material; and 
 an integrated circuit electrically connected to the sensor device, the integrated circuit to communicate measurement data related to the at least one signal via a network; 
 a power source to power at least the integrated circuit; and 
   a body containing the sensor device, and the integrated circuit, the body positionable with respect to the material to position the at least one semiconductor electrode of the sensor device to interact with the material; and   a processor that configures the integrated circuit connected to a sensor device to perform a first analytical methodology of impedance spectroscopy, and
 the processor further configured to perform a second analytical methodology selected from a group consisting of: potentiometry, coulometry, voltammetry, square wave voltammetry, stair-case voltammetry, cyclic voltammetry, alternating current voltammetry, amperometry, pulsed amperometry, galvanometry, polarography. current measurement, voltage measurement, impedance measurement, resistance measurement, and charge measurement, and
 the processor further configured to communicate with the communications device via the network, the processor further configured to apply machine learning for determining a not directly measurable characteristic of the material based on the measurement data received from the integrated circuit, the machine learning applied via a machine learning model trained with library data to recognize the not directly measurable characteristic of the material, the library data relating previously measured signals relating to at least one electrical property of the material to known not directly measurable characteristics of the material. 
 
   
     
     
         46 . The communications system of  claim 45 , wherein the machine learning algorithm is of a specific type selected from a group consisting of: a neural network, a support vector machine, and a random forest algorithm. 
     
     
         47 . The communications system of  claim 45  connected to a display device, wherein a processor is configured to display information associated with a material characteristic determined by the machine learning. 
     
     
         48 . The communications system of  claim 47  wherein a processor causes an alert message to be displayed on the display device to notify a user. 
     
     
         49 . The communications system of  claim 45  connected to an audio device, wherein a processor is configured to cause an audio alert based on a material characteristic determined by the machine learning algorithm to notify a user. 
     
     
         50 . The communications system of  claim 45  wherein a processor of the system is in communication with one of: a smartphone, a tablet, a mobile device, a computer, or a cloud computing device.