Gate pulsed readout of floating gate fet gas sensors

AbstractFloating gate FET (FGFET) gas sensors based on work function readout allow using a wide range of materials to be included as sensing materials. Longterm stability of the FGFET signal is influenced by unintended surface conductivity. A novel active operation mode presented in this work uses voltage pulses at the suspended gate to increase baseline stability and selectivity. This transient readout strategy allows differentiation between capacitive, volume-based effects and surface-located response, yielding two physically independent readout signals. Using this readout, the baseline stability as well as the selectivity for e.g. a FGFETbased humidity sensor can be increased significantly for polymeric and porous isolating sensing layers

Using deep transfer learning and satellite imagery to estimate urban air quality in data-poor regions

Urban air pollution is a critical public health challenge in low-and-middle-income countries (LMICs). At the same time, LMICs tend to be data-poor, lacking adequate infrastructure to monitor air quality (AQ). As LMICs undergo rapid urbanization, the socio-economic burden of poor AQ will be immense. Here we present a globally scalable two-step deep learning (DL) based approach for AQ estimation in LMIC cities that mitigates the need for extensive AQ infrastructure on the ground. We train a DL model that can map satellite imagery to AQ in high-income countries (HICs) with sufficient ground data, and then adapt the model to learn meaningful AQ estimates in LMIC cities using transfer learning. The trained model can explain up to 54% of the variation in the AQ distribution of the target LMIC city without the need for target labels. The approach is demonstrated for Accra in Ghana, Africa, with AQ patterns learned and adapted from two HIC cities, specifically Los Angeles and New York

Effects of bioactive glass and β-TCP containing three-dimensional laser sintered polyetheretherketone composites on osteoblasts in vitro

Because of their excellent physical properties nonresorbable thermoplastic polymers have become more important for the field of reconstructive surgery. Aim of the present study was to investigate the effects of laser sintered polyetheretherketone (PEEK) with incorporated osteoconductive and bioactive bone substitution materials on osteoblasts in vitro. Human osteoblasts (hFOB 1.19) were seeded onto laser sintered PEEK samples containing nano-sized carbon black, β-tricalciumphosphate (β-TCP), and bioactive glass 45S5. Osteoblasts were investigated for cell viability, cell proliferation and cell morphology. A constant proliferation of osteoblasts could be observed on all samples with the highest values for bioactive glass containing samples at day 7 (OD 1.76 ± 0.22) and day 14 (QD 3.75 ± 0.31) and lowest values for β-TCP containing probes throughout the study compared with the PEEK pure control group. Highest cell viability was observed for Bioglass containing probes (95.5 ± 3.32)% whereas osteoblasts seeded on β-TCP containing probes showed reduced viability (84.4 ± 4.32)%. Laser sintered PEEK implants seem to be attractive candidates for use as bone substitutes for reconstructive surgery because of their biocompatibility, individual shape, and the possibility of compounding bioinert polymer powder with osteoconductive and bioactive materials which might benefit bone formation in vivo. © 2008 Wiley Periodicals, Inc

Effects of bioactive glass and beta-TCP containing three-dimensional laser sintered polyetheretherketone composites on osteoblasts in vitro

Because of their excellent physical properties nonresorbable thermoplastic polymers have become more important for the field of reconstructive surgery. Aim of the present study was to investigate the effects of laser sintered polyetheretherketone (PEEK) with incorporated osteoconductive and bioactive bone substitution materials on osteoblasts in vitro. Human osteoblasts (hFOB 1.19) were seeded onto laser sintered PEEK samples containing nano-sized carbon black, b-tricalciumphosphate (b-TCP), and bioactive glass 45S5. Osteoblasts were investigated for cell viability, cell proliferation and cell morphology. A constant proliferation of osteoblasts could be observed on all samples with the highest values for bioactive glass containing samples at day 7 (OD 1.76 6 0.22) and day 14 (OD 3.75 6 0.31) and lowest values for b-TCP containing probes throughout the study compared with the PEEK pure control group. Highest cell viability was observed for Bioglass containing probes (95.5 6 3.32)% whereas osteoblasts seeded on b-TCP containing probes showed reduced viability (84.4 6 4.32)%. Laser sintered PEEK implants seem to be attractive candidates for use as bone substitutes for reconstructive surgery because of their biocompatibility, individual shape, and the possibility of compounding bioinert polymer powder with osteoconductive and bioactive materials which might benefit bone formation in vivo

Assessment of air quality microsensors versus reference methods: The EuNetAir Joint Exercise - Part II

The EuNetAir Joint Exercise focused on the evaluation and assessment of environmental gaseous, particulate matter (PM) and meteorological microsensors versus standard air quality reference methods through an experimental urban air quality monitoring campaign. This work presents the second part of the results, including evaluation of parameter dependencies, measurement uncertainty of sensors and the use of machine learning approaches to improve the abilities and limitations of sensors. The results confirm that the microsensor platforms, supported by post processing and data modelling tools, have considerable potential in new strategies for air quality control. In terms of pollutants, improved correlations were obtained between sensors and reference methods through calibration with machine learning techniques for CO (r2=0.13-0.83), NO2 (r2=0.24-0.93), O3 (r2=0.22-0.84), PM10 (r2=0.54-0.83), PM2.5 (r2=0.33-0.40) and SO2 (r2=0.49-0.84). Additionally, the analysis performed suggests the possibility of compliance with the data quality objectives (DQO) defined by the European Air Quality Directive (2008/50/EC) for indicative measurements

Assessment of air quality microsensors versus reference methods: The EuNetAir joint exercise

The 1st EuNetAir Air Quality Joint Intercomparison Exercise organized in Aveiro (Portugal) from 13th–27th October 2014, focused on the evaluation and assessment of environmental gas, particulate matter (PM) and meteorological microsensors, versus standard air quality reference methods through an experimental urban air quality monitoring campaign. The IDAD-Institute of Environment and Development Air Quality Mobile Laboratory was placed at an urban traffic location in the city centre of Aveiro to conduct continuous measurements with standard equipment and reference analysers for CO, NOx, O3, SO2, PM10, PM2.5, temperature, humidity, wind speed and direction, solar radiation and precipitation. The comparison of the sensor data generated by different microsensor-systems installed side-by-side with reference analysers, contributes to the assessment of the performance and the accuracy of microsensor-systems in a real-world context, and supports their calibration and further development. The overall performance of the sensors in terms of their statistical metrics and measurement profile indicates significant differences in the results depending on the platform and on the sensors considered. In terms of pollutants, some promising results were observed for O3 (r2: 0.12–0.77), CO (r2: 0.53–0.87), and NO2 (r2: 0.02–0.89). For PM (r2: 0.07–0.36) and SO2 (r2: 0.09–0.20) the results show a poor performance with low correlation coefficients between the reference and microsensor measurements. These field observations under specific environmental conditions suggest that the relevant microsensor platforms, if supported by the proper post processing and data modelling tools, have enormous potential for new strategies in air quality control

Assessment of air quality microsensors versus reference methods: The EuNetAir Joint Exercise – Part II

The EuNetAir Joint Exercise focused on the evaluation and assessment of environmental gaseous, particulate matter (PM) and meteorological microsensors versus standard air quality reference methods through an experimental urban air quality monitoring campaign. This work presents the second part of the results, including evaluation of parameter dependencies, measurement uncertainty of sensors and the use of machine learning approaches to improve the abilities and limitations of sensors. The results confirm that the microsensor platforms, supported by post processing and data modelling tools, have considerable potential in new strategies for air quality control. In terms of pollutants, improved correlations were obtained between sensors and reference methods through calibration with machine learning techniques for CO (r = 0.13–0.83), NO (r = 0.24–0.93), O (r = 0.22–0.84), PM10 (r = 0.54–0.83), PM2.5 (r = 0.33–0.40) and SO (r = 0.49–0.84). Additionally, the analysis performed suggests the possibility of compliance with the data quality objectives (DQO) defined by the European Air Quality Directive (2008/50/EC) for indicative measurements.Peer Reviewe