研究目的
To characterize a new commercially available, laser-based, and ultra-portable formaldehyde (HCHO) gas sensor and assess its usefulness for monitoring HCHO mixing ratios in both indoor and outdoor environments.
研究成果
The Aeris sensor is a viable alternative for ambient air monitoring networks or indoor air chemistry studies due to its ease of use, portability, and cost. It can distinguish between ambient levels of HCHO normally found in outdoor and indoor locations with sufficient precision and accuracy. However, it is not a replacement for the most sensitive research-grade instrumentation in some applications.
研究不足
The compact Aeris sensor is currently not a replacement for the most sensitive research-grade instrumentation available. The sensor's performance is affected by instabilities and movements in fringes caused by the optical train’s etalons, particles passing through the inline filter, and minor gas-phase absorbers not listed in the HITRAN database.
1:Experimental Design and Method Selection:
The study involved stepped calibrations and intercomparison with well-established laser-induced fluorescence (LIF) instrumentation to evaluate the performance of the absorption-based, mid-infrared HCHO sensor from Aeris Technologies, Inc. A nonlinear least-squares fitting algorithm was developed independently to fit the sensor’s raw absorption data during post-processing.
2:Sample Selection and Data Sources:
The sensor was compared with LIF instruments from Harvard and NASA Goddard. Calibrations were performed using HCHO gas standards diluted with ultra-zero air.
3:List of Experimental Equipment and Materials:
The Aeris sensor, LIF instruments, HCHO gas cylinder standards, ultra-zero air, PTFE filter membrane, DNPH cartridges.
4:Experimental Procedures and Operational Workflow:
The sensor was operated in both HDO and CH4 modes under various conditions. Data were collected at different integration times and compared with LIF measurements.
5:Data Analysis Methods:
A nonlinear least-squares fitting algorithm was used for post-processing. The Allan–Werle deviation curves were calculated to determine the optimal averaging time and the sensor’s true 3σ limit of detection.
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