The invention relates to a sensor device (1) for detecting a gas (G), particularly a permanent gas such as H.sub.2, CO, CO.sub.2, CH.sub.4, comprising: an adsorption filter (30) comprising a body (2) consisting of a molecular sieve material, a sensing element (10) for detecting said gas (G), and a carrier (4) for carrying the sensing element (10), wherein the carrier (4) comprises an opening (50) via which said gas (G) to be detected can reach the sensing element (10), and wherein the adsorption filter (30) is connected, particularly glued, to the carrier (4) and closes said opening (50) so that said gas (G) to be detected can diffuse through said body (2) towards the sensing element (10).

Improved optical absorption spectroscopy of species having broad spectral features is provided by choosing frequencies to cover the spectral feature(s) of interest, where the frequencies are slightly adjusted as needed to avoid narrow spectral features from interfering chemical species (i.e., clutter). The resulting clutter avoidance provides improved optical spectroscopy of species having broad spectral features.

A gas detector includes metal-oxide semiconductor gas sensors and their driving circuit. The gas detector stores the ratio of initial gas sensor resistance in air and that in an atmosphere including Freon gas, for the gas sensors. The gas detector learns sensor resistance in air for a gas sensor in use and detects Freon gas by comparing the sensor resistance of the gas sensor in use with the learned resistance in air divided by the ratio. When the first gas sensor has been used for a predetermined period, both the first gas sensor and a second gas sensor are used for a learning period to continue detection of Freon by the first gas sensor and to learn the resistance in air of the second gas sensor. After completion of the learning period, Freon is detected by the second gas sensor.

The invention relates to a system and micro monitor apparatus, a space-, time-, and cost-efficient device to concentrate, identify, and quantify organic compounds in gas environments. The invention further relates to a method centered on gas chromatography for identifying and quantifying organic compounds in gas environments, using air as the carrier gas, without the need for a compressed pre-bottled purified carrier gas.

The invention disclosed herein relates to a dry sensor for measuring the concentration of an analyte in a liquid beverage sample. Described herein is a novel dry sensor which is able to receive a liquid sample and adjust the pH of the liquid to be suitable for assaying an analyte of interest without the need to add reagents to the sample and/or to perform manually timed operations and able to detect a redox reaction in the presence of a liquid sample. The meter disclosed herein, when connected to the sensor disclosed herein is able to adjust the temperature of the liquid to be suitable for the assay, apply a series of potentials, measure the current at several times, measure the diffusion coefficient of the limiting electrochemical species, calculate the concentration of one or more analytes, and rapidly provide the user with the required information on the liquid sample.

In an example method, a system obtains first data indicating a plurality of properties of a plurality of gas flow lines. The properties include, for each of the gas flow lines (i) data representing a flow rate of a gas through that gas flow line, (ii) data representing a pressure of the gas in that gas flow line, and (iii) data representing an additive included in the gas in that gas flow line, such as a substance for inhibiting corrosion. For each of the gas flow lines, the system uses a computerized neural network to determine a risk of corrosion associated with that gas flow line based on the properties of that gas flow line, determines whether the metric for that gas flow line is greater than a threshold level, and if so, generates a notification for presentation to a user.

A gas sensor 10 has a measuring channel 11 with a gas inlet 12 and with a gas outlet 13, at least one receptor layer 20, a reference electrode 30 and a voltage-controlled analysis unit 50. The reference electrode 30 is capacitively coupled with the receptor layer 20. The reference electrode 30 is connected to the analysis unit 50 in an electrically conductive manner. The receptor layer 20 is formed in measuring channel 11. The measuring channel 11 forms a dielectric layer between the receptor layer 20 and the reference electrode 30. The receptor layer 20 has a support 21 and an analyte-binding layer 22. The present invention provides for the analyte-binding layer 22 to be a self-assembling monolayer (SAM).

A particle detecting module is provided and includes a base, a piezoelectric actuator, a driving circuit board, a laser component, a particulate sensor and an outer cover. A gas-guiding-component loading region and a laser loading region are separated by the base. By the design of the gas flowing path, the driving circuit board covering the bottom surface of the base, and the outer cover covering the surfaces of the base, an inlet path is defined by the gas inlet groove of the base, and an outlet path is defined by a gas outlet groove of the base. Consequently, the thickness of the particle detecting module is drastically reduced.

A sample introduction device 10 includes a tube holding section 21 and a sample removing mechanism 40. The sample removing mechanism 40 removes a sample 6 in a sample tube 2 held by the tube holding section 21. Thus, in the sample introduction device 10, the sample 6 in the sample tube 2 held by the tube holding section 21 can be automatically removed. As a result, the operator no longer needs to perform an operation of taking out the sample 6 from the sample tube 2. Thus, a work load on the operator can be reduced.

Provided is a method and system for extracting features from raw data for machine learning processing. Using an array of gas sensors, raw data for at least one compound of interest are extracted based upon the type of output signals. Where the output signals are amplitude-variant, mean features are extracted by chunking the raw data into slices and calculating the mean area under the curve. Where the output signals are amplitude-and-time-variant, mean-plus-slope features are extracted by taking logarithmic values of the raw data and calculating the mean area under the curve.