Abstract: Gas sensors are disclosed having an on-board, low-power data processor that uses multivariable gas classification and/or gas quantitation models to perform on-board data processing to resolve two or more gases in a fluid sample. To reduce computational complexity, the gas sensor utilizes low-power-consumption multivariable data analysis algorithms, inputs from available on-board sensors of ambient conditions, inputs representing contextual data, and/or excitation responses of a gas sensing material to select suitable gas classification and/or gas quantitation models. The data processor can then utilize these gas classification and quantitation models, in combination with measured dielectric responses of a gas sensing material of the gas sensor, to determine classifications and/or concentrations of two or more gases in a fluid sample, while consuming substantially less power than would be consumed if a global comprehensive model were used instead.

Abstract: An electrochemical gas sensor for multi-gas analysis of a fluid sample includes an electrochemical gas sensing element and a data collection component. The data collection component is configured to cycle the electrochemical gas sensing element between first excitation and signal detection values and second excitation and signal detection values at a predetermined time constant, and to measure responses of the electrochemical gas sensor to the fluid sample at the first excitation and signal detection values and the second excitation and signal detection values wherein the responses of the electrochemical gas sensor to the fluid sample at the first excitation and signal detection values and the second excitation and signal detection values are indicative of identities, respective concentrations, or a combination thereof, of at least two analyte gases of the fluid sample.

Abstract: A system and a method for multi-gas sensing using dielectric excitation of a single sensing material at multiple operating temperatures. By measuring dielectric excitation responses of the gas sensing material, enhanced multi-gas differentiation and differentiation can be achieved using fewer operating temperatures than would be used by the same MOS gas sensing material configured to perform multi-gas differentiation based on resistance responses alone. The disclosed gas sensors and gas sensing techniques enable improved response linearity, improved dynamic range, and reduced computational resource consumption for multi-gas quantitation relative to traditional resistance-based gas sensing methods.

Abstract: Gas sensors are disclosed having an on-board, low-power data processor that uses multivariable gas classification and/or gas quantitation models to perform on-board data processing to resolve two or more gases in a fluid sample. To reduce computational complexity, the gas sensor utilizes low-power-consumption multivariable data analysis algorithms, inputs from available on-board sensors of ambient conditions, inputs representing contextual data, and/or excitation responses of a gas sensing material to select suitable gas classification and/or gas quantitation models. The data processor can then utilize these gas classification and quantitation models, in combination with measured dielectric responses of a gas sensing material of the gas sensor, to determine classifications and/or concentrations of two or more gases in a fluid sample, while consuming substantially less power than would be consumed if a global comprehensive model were used instead.